5/25/2020 Rubric Detail – 31228.202030
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Name: ITS836 (8 Week) Research Paper Rubric
Description: Please use this rubric for grading research papers
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No requirements are met
Includes a few of the required components as speci�ed in the assignment.
Includes some of the required components as speci�ed in the assignment.
Includes most of the required components as speci�ed in the assignment.
Includes all of the required components as speci�ed in the assignment.
Requirements
--
No Evidence 0 (0.00%) points
Limited Evidence 3 (3.00%) points
Below Expectations 7 (7.00%) points
Approaches Expectations 11 (11.00%) points
Meets Expectations 15 (15.00%) points
Fails to provide enough content to show a demonstration of knowledge
Major errors or omissions in demonstration of knowledge.
Some signi�cant but not major errors or omissions in demonstration of knowledge.
A few errors or omissions in demonstration of knowledge.
Demonstrates strong or adequate knowledge of the materials; correctly represents knowledge
from the readings and sources.
Content
--
No Evidence 0 (0.00%) points
Limited Evidence 3 (3.00%) points
Below Expectations 7 (7.00%) points
Approaches Expectations 11 (11.00%) points
Meets Expectations 15 (15.00%) points
5/25/2020 Rubric Detail – 31228.202030
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g
Fails to provide a critical thinking analysis and interpretation
Major errors or omissions in analysis and interpretation.
Some signi�cant but not major errors or omissions in analysis and interpretation.
A few errors or omissions in analysis and interpretation.
Provides a strong critical analysis and interpretation of the information given.
Critical Analysis
--
No Evidence 0 (0.00%) points
Limited Evidence 5 (5.00%) points
Below Expectations 10 (10.00%) points
Approaches Expectations 15 (15.00%) points
Meets Expectations 20 (20.00%) points
Fails to demonstrate problem solving.
Major errors or omissions in problem solving.
Some signi�cant but not major errors or omissions in problem solving.
A few errors or omissions in problem solving.
Demonstrates strong or adequate thought and insight in problem solving.
Problem Solving
--
No Evidence 0 (0.00%) points
Limited Evidence 5 (5.00%) points
Below Expectations 10 (10.00%) points
Approaches Expectations 15 (15.00%) points
Meets Expectations 20 (20.00%) points
Source or example selection and integration of knowledge.
Man7057 Proposal Feedback Form
Student Name
Sanaullah
Student No
18150003
Title
MSc Management and International Business MAN7057-A-S2-2019/0
Assessing the leadership styles of non-Profit organization & its impacts on overall business operations
Supervisor’s Name
Vanessa Clarke
Section
Feedback
Mark
Title, Background and Problem statement (10%). A clear and critical understanding of your chosen topic. The topic must be both worthwhile and relevant as reflected by: a working title; an explanation of the background; a problem statement which focuses the background onto the research topic, providing the rationale for the research aim
There are some good, yet broad and basic, ideas here. There is plenty of refining to be done to make it clear and focused.
The problem statement could be worded as a question, for example, what are the critical success factors in effective leaders within non-profit organisations? You could then include the specifics within the research objectives. It has to be SMART, otherwise is it too broad and you won’t be able to measure the extent to which you have achieved the outcomes at the end of the project.
5
Aim, Objectivesand Research Question (10%):
· Aims – usually one main aim, consistent with the title
· Objectives – usually 3 to 7 more specific that aims, and refine the aim(s) into smaller parts
· One or more clear research questions, consistent with the aim and title
The overall aim should have been repeated here.
Objectives need to be made explicitly clear and the research questions need to be better formulated. As stated above, one clear overall question would help you focus.
5
Outline Literature Review(25%): A short review of literature relevant to your topic. The review must reflect:
· Provide proof of scholarship
· Reflect your basic understanding of your topic
· Reflect your intellectual ability to construct critical arguments based on your reading of the relevant literature.
Some limited and basic, yet mostly relevant, literature is cited with some appropriate reference sources. Definitions list is weak and needs strengthening. Please note that, in order to achieve higher marks, you need considerably more references than those listed here. Some links made between the elements chosen as the research topic. There is confusion due to the lack of clarity and broad focus. Narrowing it down will help you significantly.
11
Outline Methodology (20%): A well-reasoned methodological approach in terms of:
· The scope of the research
· The choice of a research strategy
· A basic understanding of methods and techniques to collect primary data (if appropriate)
If a survey is to be used this should include how you intend to select your sample and distribute questionnaires.
· What tools and techniques are proposed to be used to analyse the data.
You need to make it clear how you intend to collect the data as it is currently unclear.
If this is a secondary-only piece of research, will you be do.
Man7057 Proposal Feedback Form
Student Name
Sanaullah
Student No
18150003
Title
MSc Management and International Business MAN7057-A-S2-2019/0
Assessing the leadership styles of non-Profit organization & its impacts on overall business operations
Supervisor’s Name
Vanessa Clarke
Section
Feedback
Mark
Title, Background and Problem statement (10%). A clear and critical understanding of your chosen topic. The topic must be both worthwhile and relevant as reflected by: a working title; an explanation of the background; a problem statement which focuses the background onto the research topic, providing the rationale for the research aim
There are some good, yet broad and basic, ideas here. There is plenty of refining to be done to make it clear and focused.
The problem statement could be worded as a question, for example, what are the critical success factors in effective leaders within non-profit organisations? You could then include the specifics within the research objectives. It has to be SMART, otherwise is it too broad and you won’t be able to measure the extent to which you have achieved the outcomes at the end of the project.
5
Aim, Objectivesand Research Question (10%):
· Aims – usually one main aim, consistent with the title
· Objectives – usually 3 to 7 more specific that aims, and refine the aim(s) into smaller parts
· One or more clear research questions, consistent with the aim and title
The overall aim should have been repeated here.
Objectives need to be made explicitly clear and the research questions need to be better formulated. As stated above, one clear overall question would help you focus.
5
Outline Literature Review(25%): A short review of literature relevant to your topic. The review must reflect:
· Provide proof of scholarship
· Reflect your basic understanding of your topic
· Reflect your intellectual ability to construct critical arguments based on your reading of the relevant literature.
Some limited and basic, yet mostly relevant, literature is cited with some appropriate reference sources. Definitions list is weak and needs strengthening. Please note that, in order to achieve higher marks, you need considerably more references than those listed here. Some links made between the elements chosen as the research topic. There is confusion due to the lack of clarity and broad focus. Narrowing it down will help you significantly.
11
Outline Methodology (20%): A well-reasoned methodological approach in terms of:
· The scope of the research
· The choice of a research strategy
· A basic understanding of methods and techniques to collect primary data (if appropriate)
If a survey is to be used this should include how you intend to select your sample and distribute questionnaires.
· What tools and techniques are proposed to be used to analyse the data.
You need to make it clear how you intend to collect the data as it is currently unclear.
If this is a secondary-only piece of research, will you be do.
Remarks from the professor on milestone 1 and for milestone 2A.docxsodhi3
Remarks from the professor on milestone 1 and for milestone 2
A good start on the paper. Please look at the introduction again. It does not match the case problem or what the issues of the company are. If you look at the first paragraph it does not lead into the next paragraph and the facts and flow are disjointed.
I read through the paper and it is a series of unjoined statements that don't really flow into a research work. You do have the correct issue that monthly variance in demand is the key to solving the problem but the supporting work for this statement is not there.
The next step for milestone 2 is to analyze the descriptive statistics, ANOVA and correlation/regression asked for in the case. From that you should see how to develop a model to correct the forecasting problem. Make sure your Milestone 2, gives a model that solves the problem.
QSO 510 Milestone Two Guidelines and Rubric
The final project for this course is the creation of a statistical analysis report. Operations management professionals are often relied upon to make decisions
regarding operational processes. Those who utilize a data-driven, structured approach have a clear advantage over those offering decisions based solely on
intuition. You will be provided with a scenario often encountered by an operations manager. Your task is to review the “A-Cat Corp.: Forecasting” scenario, the
addendum, and the accompanying data in the case scenario and addendum.
In Module Seven, you will submit your selection of statistical tools and data analysis, which are critical elements III and IV. You will submit a 3- to 4-page paper
and a spreadsheet that provides justification for the appropriate statistical tools needed to analyze the company’s data, a hypothesis, the results of your analysis,
any inferences from your hypothesis test, and a forecasting model that addresses the company’s problem.
Specifically, the following critical elements must be addressed:
III. Identify statistical tools and methods to collect data:
A. Identify the appropriate family of statistical tools that you will use to perform your analysis. What are your statistical assumptions concerning
the data that led you to selecting this family of tools? In other words, why did you select this family of tools for statistical analysis?
B. Determine the category of the provided data in the given case study. Be sure to justify why the data fits into this category type. What is the
relationship between the type of data and the tools?
C. From the identified family of statistical tools, select the most appropriate tool(s) for analyzing the data provided in the given case study.
D. Justify why you chose this tool to analyze the data. Be sure to include how this tool will help predict the use of the data in driving decisions.
E. Describe the quantitative method that will best inform data-driven decisions. Be sure to include how this method will point out the relationships
between ...
Rubric Detail A rubric lists grading criteria that instruct.docxrobert345678
Rubric Detail
A rubric lists grading criteria that instructors use to evaluate student work. Your instructor linked a rubric to this item and made it available to you. Select Grid View or List View to change the rubric's layout.
Content
https%3A%2F%2Fkeiseruniversity.blackboard.com%2Fwebapps%2Frubric%2FWEB-INF%2Fjsp%2Fcourse%2FrubricGradingPopup.jsp%3Fmode%3Dgrid%26isPopup%3Dtrue%26rubricCount%3D1%26prefix%3D_7714706_1%26course_id%3D_411476_1%26maxValue%3D100.0%26rubricId%3D_345993_1%26viewOnly%3Dtrue%26displayGrades%3Dfalse%26type%3Dgrading%26rubricAssoId%3D_605243_1
Name: Week 7 Video Presentation
Description: Up to 10% deduction may be implemented for not following APA style standards (e.g., references and in-text citations).
Grid ViewList View
Poor
Satisfactory
Good
Excellent
Introduction
Points:
Points Range:
0 (0.00%) - 10.35 (10.35%)
One of the following components are included in the presentation: (1) Student introduces the article topic, tells the reader what to expect. (2) rough background of literature (3) Student describes significance of the problem, (4)researcher's question(s) and hypothesis introduces the article topic, tells the reader what to expect. Student describes significance of the problem, researcher's hypothesis, and rough background of literature.
Feedback:
Points:
Points Range:
10.5 (10.50%) - 11.85 (11.85%)
Two of the following components are included in the presentation: (1) Student introduces the article topic, tells the reader what to expect. (2) rough background of literature (3) Student describes significance of the problem, (4)researcher's question(s) and hypothesis what to expect. Student describes significance of the problem, researcher's hypothesis, and rough background of literature.
Feedback:
Points:
Points Range:
12 (12.00%) - 13.35 (13.35%)
Three of the following components are included in the presentation: (1) Student introduces the article topic, tells the reader what to expect. (2) rough background of literature (3) Student describes significance of the problem, (4)researcher's question(s) and hypothesis
Feedback:
Points:
Points Range:
13.5 (13.50%) - 15 (15.00%)
All of the following components are included in the presentation: (1) Student introduces the article topic, tells the reader what to expect. (2) rough background of literature (3) Student describes significance of the problem, (4)researcher's question(s) and hypothesis
Feedback:
Methods.
#35537 Topic Course Project Part 3—Translating Evidence Into Pra.docxAASTHA76
#35537 Topic: Course Project: Part 3—Translating Evidence Into Practice. Continuation of the assignment attached
Number of Pages: 3 (Double Spaced)
Number of sources: 3
Writing Style: APA
Type of document: Coursework
Academic Level:Master
Category: Nursing
VIP Support: N/A
Language Style: English (U.S.)
Order Instructions: Attached
In Part 3 of the Course Project, you consider how the evidence you gathered during Part 2 can be translated into nursing practice.
Now that you have located available research on your PICOT question, you will examine what the research indicates about nursing practices. Connecting research evidence and findings to actual decisions and tasks that nurses complete in their daily practice is essentially what evidence-based practice is all about. This final component of the Course Project asks you to translate the evidence and data from your literature review into authentic practices that can be adopted to improve health care outcomes. In addition, you will also consider possible methods and strategies for disseminating evidence-based practices to your colleagues and to the broader health care field.
To prepare:
Consider Parts 1 and 2 of your Course Project. How does the research address your PICOT question?
With your PICOT question in mind, identify at least one nursing practice that is supported by the evidence in two or more of the articles from your literature review. Consider what the evidence indicates about how this practice contributes to better outcomes.
Explore possible consequences of failing to adopt the evidence-based practice that you identified.
Consider how you would disseminate information about this evidence-based practice throughout your organization or practice setting. How would you communicate the importance of the practice?
To complete:
In a 3- to 4-page paper:
Restate your PICOT question and its significance to nursing practice.
Summarize the findings from the articles you selected for your literature review. Describe at least one nursing practice that is supported by the evidence in the articles. Justify your response with specific references to at least 2 of the articles.
Explain how the evidence-based practice that you identified contributes to better outcomes. In addition, identify potential negative outcomes that could result from failing to use the evidence-based practice.
Outline the strategy for disseminating the evidence-based practice that you identified throughout your practice setting. Explain how you would communicate the importance of the practice to your colleagues. Describe how you would move from disseminating the information to implementing the evidence-based practice within your organization. How would you address concerns and opposition to the change in practice?It should be combined with the other two components of the Course Project and turned in as your Portfolio Assignment for this course.
IMPORTANT
Reminder: The School of Nursing requires th.
Man7057 Proposal Feedback Form
Student Name
Sanaullah
Student No
18150003
Title
MSc Management and International Business MAN7057-A-S2-2019/0
Assessing the leadership styles of non-Profit organization & its impacts on overall business operations
Supervisor’s Name
Vanessa Clarke
Section
Feedback
Mark
Title, Background and Problem statement (10%). A clear and critical understanding of your chosen topic. The topic must be both worthwhile and relevant as reflected by: a working title; an explanation of the background; a problem statement which focuses the background onto the research topic, providing the rationale for the research aim
There are some good, yet broad and basic, ideas here. There is plenty of refining to be done to make it clear and focused.
The problem statement could be worded as a question, for example, what are the critical success factors in effective leaders within non-profit organisations? You could then include the specifics within the research objectives. It has to be SMART, otherwise is it too broad and you won’t be able to measure the extent to which you have achieved the outcomes at the end of the project.
5
Aim, Objectivesand Research Question (10%):
· Aims – usually one main aim, consistent with the title
· Objectives – usually 3 to 7 more specific that aims, and refine the aim(s) into smaller parts
· One or more clear research questions, consistent with the aim and title
The overall aim should have been repeated here.
Objectives need to be made explicitly clear and the research questions need to be better formulated. As stated above, one clear overall question would help you focus.
5
Outline Literature Review(25%): A short review of literature relevant to your topic. The review must reflect:
· Provide proof of scholarship
· Reflect your basic understanding of your topic
· Reflect your intellectual ability to construct critical arguments based on your reading of the relevant literature.
Some limited and basic, yet mostly relevant, literature is cited with some appropriate reference sources. Definitions list is weak and needs strengthening. Please note that, in order to achieve higher marks, you need considerably more references than those listed here. Some links made between the elements chosen as the research topic. There is confusion due to the lack of clarity and broad focus. Narrowing it down will help you significantly.
11
Outline Methodology (20%): A well-reasoned methodological approach in terms of:
· The scope of the research
· The choice of a research strategy
· A basic understanding of methods and techniques to collect primary data (if appropriate)
If a survey is to be used this should include how you intend to select your sample and distribute questionnaires.
· What tools and techniques are proposed to be used to analyse the data.
You need to make it clear how you intend to collect the data as it is currently unclear.
If this is a secondary-only piece of research, will you be do.
Man7057 Proposal Feedback Form
Student Name
Sanaullah
Student No
18150003
Title
MSc Management and International Business MAN7057-A-S2-2019/0
Assessing the leadership styles of non-Profit organization & its impacts on overall business operations
Supervisor’s Name
Vanessa Clarke
Section
Feedback
Mark
Title, Background and Problem statement (10%). A clear and critical understanding of your chosen topic. The topic must be both worthwhile and relevant as reflected by: a working title; an explanation of the background; a problem statement which focuses the background onto the research topic, providing the rationale for the research aim
There are some good, yet broad and basic, ideas here. There is plenty of refining to be done to make it clear and focused.
The problem statement could be worded as a question, for example, what are the critical success factors in effective leaders within non-profit organisations? You could then include the specifics within the research objectives. It has to be SMART, otherwise is it too broad and you won’t be able to measure the extent to which you have achieved the outcomes at the end of the project.
5
Aim, Objectivesand Research Question (10%):
· Aims – usually one main aim, consistent with the title
· Objectives – usually 3 to 7 more specific that aims, and refine the aim(s) into smaller parts
· One or more clear research questions, consistent with the aim and title
The overall aim should have been repeated here.
Objectives need to be made explicitly clear and the research questions need to be better formulated. As stated above, one clear overall question would help you focus.
5
Outline Literature Review(25%): A short review of literature relevant to your topic. The review must reflect:
· Provide proof of scholarship
· Reflect your basic understanding of your topic
· Reflect your intellectual ability to construct critical arguments based on your reading of the relevant literature.
Some limited and basic, yet mostly relevant, literature is cited with some appropriate reference sources. Definitions list is weak and needs strengthening. Please note that, in order to achieve higher marks, you need considerably more references than those listed here. Some links made between the elements chosen as the research topic. There is confusion due to the lack of clarity and broad focus. Narrowing it down will help you significantly.
11
Outline Methodology (20%): A well-reasoned methodological approach in terms of:
· The scope of the research
· The choice of a research strategy
· A basic understanding of methods and techniques to collect primary data (if appropriate)
If a survey is to be used this should include how you intend to select your sample and distribute questionnaires.
· What tools and techniques are proposed to be used to analyse the data.
You need to make it clear how you intend to collect the data as it is currently unclear.
If this is a secondary-only piece of research, will you be do.
Remarks from the professor on milestone 1 and for milestone 2A.docxsodhi3
Remarks from the professor on milestone 1 and for milestone 2
A good start on the paper. Please look at the introduction again. It does not match the case problem or what the issues of the company are. If you look at the first paragraph it does not lead into the next paragraph and the facts and flow are disjointed.
I read through the paper and it is a series of unjoined statements that don't really flow into a research work. You do have the correct issue that monthly variance in demand is the key to solving the problem but the supporting work for this statement is not there.
The next step for milestone 2 is to analyze the descriptive statistics, ANOVA and correlation/regression asked for in the case. From that you should see how to develop a model to correct the forecasting problem. Make sure your Milestone 2, gives a model that solves the problem.
QSO 510 Milestone Two Guidelines and Rubric
The final project for this course is the creation of a statistical analysis report. Operations management professionals are often relied upon to make decisions
regarding operational processes. Those who utilize a data-driven, structured approach have a clear advantage over those offering decisions based solely on
intuition. You will be provided with a scenario often encountered by an operations manager. Your task is to review the “A-Cat Corp.: Forecasting” scenario, the
addendum, and the accompanying data in the case scenario and addendum.
In Module Seven, you will submit your selection of statistical tools and data analysis, which are critical elements III and IV. You will submit a 3- to 4-page paper
and a spreadsheet that provides justification for the appropriate statistical tools needed to analyze the company’s data, a hypothesis, the results of your analysis,
any inferences from your hypothesis test, and a forecasting model that addresses the company’s problem.
Specifically, the following critical elements must be addressed:
III. Identify statistical tools and methods to collect data:
A. Identify the appropriate family of statistical tools that you will use to perform your analysis. What are your statistical assumptions concerning
the data that led you to selecting this family of tools? In other words, why did you select this family of tools for statistical analysis?
B. Determine the category of the provided data in the given case study. Be sure to justify why the data fits into this category type. What is the
relationship between the type of data and the tools?
C. From the identified family of statistical tools, select the most appropriate tool(s) for analyzing the data provided in the given case study.
D. Justify why you chose this tool to analyze the data. Be sure to include how this tool will help predict the use of the data in driving decisions.
E. Describe the quantitative method that will best inform data-driven decisions. Be sure to include how this method will point out the relationships
between ...
Rubric Detail A rubric lists grading criteria that instruct.docxrobert345678
Rubric Detail
A rubric lists grading criteria that instructors use to evaluate student work. Your instructor linked a rubric to this item and made it available to you. Select Grid View or List View to change the rubric's layout.
Content
https%3A%2F%2Fkeiseruniversity.blackboard.com%2Fwebapps%2Frubric%2FWEB-INF%2Fjsp%2Fcourse%2FrubricGradingPopup.jsp%3Fmode%3Dgrid%26isPopup%3Dtrue%26rubricCount%3D1%26prefix%3D_7714706_1%26course_id%3D_411476_1%26maxValue%3D100.0%26rubricId%3D_345993_1%26viewOnly%3Dtrue%26displayGrades%3Dfalse%26type%3Dgrading%26rubricAssoId%3D_605243_1
Name: Week 7 Video Presentation
Description: Up to 10% deduction may be implemented for not following APA style standards (e.g., references and in-text citations).
Grid ViewList View
Poor
Satisfactory
Good
Excellent
Introduction
Points:
Points Range:
0 (0.00%) - 10.35 (10.35%)
One of the following components are included in the presentation: (1) Student introduces the article topic, tells the reader what to expect. (2) rough background of literature (3) Student describes significance of the problem, (4)researcher's question(s) and hypothesis introduces the article topic, tells the reader what to expect. Student describes significance of the problem, researcher's hypothesis, and rough background of literature.
Feedback:
Points:
Points Range:
10.5 (10.50%) - 11.85 (11.85%)
Two of the following components are included in the presentation: (1) Student introduces the article topic, tells the reader what to expect. (2) rough background of literature (3) Student describes significance of the problem, (4)researcher's question(s) and hypothesis what to expect. Student describes significance of the problem, researcher's hypothesis, and rough background of literature.
Feedback:
Points:
Points Range:
12 (12.00%) - 13.35 (13.35%)
Three of the following components are included in the presentation: (1) Student introduces the article topic, tells the reader what to expect. (2) rough background of literature (3) Student describes significance of the problem, (4)researcher's question(s) and hypothesis
Feedback:
Points:
Points Range:
13.5 (13.50%) - 15 (15.00%)
All of the following components are included in the presentation: (1) Student introduces the article topic, tells the reader what to expect. (2) rough background of literature (3) Student describes significance of the problem, (4)researcher's question(s) and hypothesis
Feedback:
Methods.
#35537 Topic Course Project Part 3—Translating Evidence Into Pra.docxAASTHA76
#35537 Topic: Course Project: Part 3—Translating Evidence Into Practice. Continuation of the assignment attached
Number of Pages: 3 (Double Spaced)
Number of sources: 3
Writing Style: APA
Type of document: Coursework
Academic Level:Master
Category: Nursing
VIP Support: N/A
Language Style: English (U.S.)
Order Instructions: Attached
In Part 3 of the Course Project, you consider how the evidence you gathered during Part 2 can be translated into nursing practice.
Now that you have located available research on your PICOT question, you will examine what the research indicates about nursing practices. Connecting research evidence and findings to actual decisions and tasks that nurses complete in their daily practice is essentially what evidence-based practice is all about. This final component of the Course Project asks you to translate the evidence and data from your literature review into authentic practices that can be adopted to improve health care outcomes. In addition, you will also consider possible methods and strategies for disseminating evidence-based practices to your colleagues and to the broader health care field.
To prepare:
Consider Parts 1 and 2 of your Course Project. How does the research address your PICOT question?
With your PICOT question in mind, identify at least one nursing practice that is supported by the evidence in two or more of the articles from your literature review. Consider what the evidence indicates about how this practice contributes to better outcomes.
Explore possible consequences of failing to adopt the evidence-based practice that you identified.
Consider how you would disseminate information about this evidence-based practice throughout your organization or practice setting. How would you communicate the importance of the practice?
To complete:
In a 3- to 4-page paper:
Restate your PICOT question and its significance to nursing practice.
Summarize the findings from the articles you selected for your literature review. Describe at least one nursing practice that is supported by the evidence in the articles. Justify your response with specific references to at least 2 of the articles.
Explain how the evidence-based practice that you identified contributes to better outcomes. In addition, identify potential negative outcomes that could result from failing to use the evidence-based practice.
Outline the strategy for disseminating the evidence-based practice that you identified throughout your practice setting. Explain how you would communicate the importance of the practice to your colleagues. Describe how you would move from disseminating the information to implementing the evidence-based practice within your organization. How would you address concerns and opposition to the change in practice?It should be combined with the other two components of the Course Project and turned in as your Portfolio Assignment for this course.
IMPORTANT
Reminder: The School of Nursing requires th.
Educational Data Mining/Learning Analytics issue brief overviewMarie Bienkowski
An overview of the Draft Issue Brief prepared by SRI International for the US Department of Education on Educational Data Mining and Learning Analytics
Introduction to Epidemiology Course Project Detailed Article Cri.docxmariuse18nolet
Introduction to Epidemiology Course Project
Detailed Article Critique
Reading, interpreting, and judging the value of epidemiologic literature is a skill that you will exercise throughout your academic career in public health and beyond. To help prepare you for your thesis or capstone experience as well as future coursework, your project this term involves writing a detailed critique of an article. (See link in blackboard.) Construct your critique using complete paragraphs. DO NOT present your critique as an outline.
Please consult the table on the next page to ensure that your review includes the required major elements for this assignment. We will use this table in our evaluation of your project. Each element is worth a specified number of points. Please understand that full credit will not be awarded for simply mentioning each element. You must justify your assessment. For example, you cannot simply state, “The authors justified the importance of their study.” You need to point out how the authors did or did not meet each criterion. Your critique should not be longer than 4-6 pages double-spaced with one-inch margins. There is no minimum page requirement.
For additional assistance, please refer to the article by Young et al. (2009) that is also posted in Blackboard along with the article that you are to review.
See next page for more details. ( ( ( ( ( (
Name___________________________
Element
Possible Points
Grade
Objectives:
· What is/are the research question(s) addressed by this study?
· Did the authors justify the need for their study? What was it?
5
5
Study Design:
· Describe the study design and methods used for the study.
· Critique the overall design by addressing the following:
· Did the authors correctly identify their study design (e.g., the authors stated that their study was a cohort study when it really was a cross-sectional study)?
· Did the authors present clear definitions of their exposure/health outcomes?
· Did the authors adequately describe their sampling (e.g., convenience, consecutive, or random; response rate; study population) and data collection methods (e.g., questionnaire, records, biomarkers, surveys, etc.)?
15
10
Analysis:
· Describe the data analysis plan overall.
· Critique the analysis plan by addressing the following:
· Is the analysis appropriate for the study design (considering you level of statistical knowledge)? Was potential confounding addressed in the analysis stage? If so, how?
· Are the tables and graphs well organized and labeled? Do the tables and graphs provide enough data for reader to draw their own conclusions (e.g. do they stand alone?)?
· Are the findings within the tables/figures consistent? Is each table/figure consistent with the text and other tables/figures?
· Can the findings be attributed simply to chance?
5
10
Validity and Conclusions:
· Internal validity- Consider the potential for selection bias, information bias, and .
Evaluation Criteria for Applications and Formal Papers Level.docxSANSKAR20
Evaluation Criteria for Applications and Formal Papers
Levels of Achievement
Criteria
Outstanding Performance
Excellent Performance
Competent Performance
Proficient Performance
Room for Improvement
QUALITY OF WORK SUBMITTED -
1. The extent to which work meets the assigned criteria and work reflects graduate level critical and analytic thinking (0-30 Points)
30 to 30 points
Assignment exceeds expectations. All topics are addressed with a minimum of 75% containing exceptional breadth and depth about each of the assignment topics
25 to 29 points
Assignment exceeds expectations. All topics are addressed with a minimum of 75% containing exceptional breadth and depth about each of the assignment topics
20 to 24 points
Assignment meets expectations. All topics are addressed with a minimum of 50% containing good breadth and depth about each of the assignment topics.
16 to 19 points
Assignment meets most of the expectations. One required topic is either not addressed or inadequately addressed.
0 to 15 points
Assignment superficially meets some of the expectations. Two or more required topics are either not addressed or inadequately addressed.
QUALITY OF WORK SUBMITTED: Purpose of the paper is clear (0-5 Points)
5 to 5 points
A clear and comprehensive purpose statement is provided which delineates all required criteria.
5 to 5 points
A clear and comprehensive purpose statement is provided which delineates all required criteria.
4 to 4 points
Purpose of the assignment is stated, yet is brief and not descriptive.
1 to 3 points
Purpose of the assignment is vague.
0 to 0 points
No purpose statement was provided.
ASSIMILATION AND SYNTHESIS OF IDEAS
The extent to which the work reflects the student’s ability to-
1. Understand and interpret the assignment’s key concepts (0-10 Points)
10 to 10 points
Demonstrates the ability to critically appraise and intellectually explore key concepts.
9 to 9 points
Demonstrates the ability to critically appraise and intellectually explore key concepts.
8 to 8 points
Demonstrates a clear understanding of key concepts.
5 to 7 points
Shows some degree of understanding of key concepts.
0 to 4 points
Shows a lack of understanding of key concepts, deviates from topics.
ASSIMILATION AND SYNTHESIS OF IDEAS 2. Apply and integrate material in course resources (i.e. video, required readings, and textbook) and credible outside resources (0-20 Points)
20 to 20 points
Demonstrates and applies exceptional support of major points and integrates 2 or more credible outside sources, in addition to 3-4 course resources to support point of view.
15 to 19 points
Demonstrates and applies exceptional support of major points and integrates 2 or more credible outside sources, in addition to 3-4 course resources to support point of view.
10 to 14 points
Integrates specific information from 1 credible outside resource and 3 to 4 course resources to support major points and point of view.
3 to 9 points
Minimally includes and integrates specific ...
Group Member Discussion RubricStudent NameTotal Points PossibleTot.docxwhittemorelucilla
Group Member Discussion RubricStudent NameTotal Points PossibleTotal Points ReceivedLate Work Percentage (select from drop down menu)Late Work DeductionFinal Points Received 750.000%
Windows User: Windows User:
Select dropdown menu for percentage deducted for late work.0.000.00CriterionSuperior Criteria (100%)Excellent Criteria (95%)Satisfactory Criteria (85%)Marginal Criteria (75%)Unacceptable Criteria (0%)Points Possible Evaluation (select from drop down menu)Points ReceivedFaculty FeedbackElement 1: Initial Posting Content for Weeks 2, 4, 6 (28 points maximum; 37% of total points)Student provides an original, thought-provoking, in-depth initial post addressing more than one summative consideration. The student's post stimulates critical inquiry and uses at least five additional scholarly resources to support thinking.Student provides an original, thought-provoking, in-depth initial post addressing more than one summative consideration. The student's post stimulates critical inquiry and uses at least five additional scholarly resources to support thinking. However, there are one or two minor errors in the content of the post.Student provides a good, organized post addressing at least one summative consideration, but does not consistently demonstrate higher-order thinking. The student's post stimulates some critical inquiry and uses at least five additional scholarly resources, but some details are lacking or not accurate. Student provides a weak or incomplete post addressing at least one summative consideration. The student's post demonstrates a low level of thinking and/or resources are lacking and/or do not support thinking.Does not meet minimal standards.280%
Windows User: Windows User:
Select drop down menu for inputting grade for this criterion.0Element 2: Follow-up Responses to Colleagues and Interaction for Weeks 3, 5, 7 (27 points maximum; 36% of total points)Student engages with several peers bringing the discussion to a higher level of inquiry and investigation. Responses are thorough and fully contribute to the quality of interaction by offering constructive critique, suggestions, in-depth questions, additional resources, and stimulating thoughts and/or probes.
Student engages with several peers bringing the discussion to a higher level of inquiry and investigation. Responses are thorough and fully contribute to the quality of interaction by offering constructive critique, suggestions, in-depth questions, additional resources, and stimulating thoughts and/or probes. However, there are one or two minor errors in content of responses.Student engages with at least two peers and helps extend the discussion. Responses are good and somewhat contribute to the quality of interaction by offering constructive critique, suggestions, in-depth questions, additional resources, and stimulating thoughts and/or probes. Student engages with at least two peers. Responses are minimal and do not fully contribute to the q ...
Concordia University Page 3 of 3
Concordia University Page 1 of 3
CAPSTONE PROJECTDOCUMENTATION FORM
Action Research is an exciting, disciplined process of discovery designed to integrate theory into one’s daily practice in a way that improves educational practices and the individual conducting the research. Action Research is the Capstone Project in the Master’s of Education program for Concordia University online. It gives the educator, as a scholarly practitioner, the opportunity to examine relevant issues in his or her own classroom or school which may complicate, compromise, or complement the learning process—and to find meaningful, practical, research-based answers.
In Action Research, teachers are empowered to design a research-based plan, identify learning issues or problems, review relevant literature that examines identified problems, implement specific, research-based strategies, and discover convincing evidence that supports or contravenes their teaching strategies. The most exciting part of Action Research is the teacher can often observe student improvement during the project and can demonstrate, in a quantitative manner, the improvement of student learning. Sagor notes, “Seeing students grow is probably the greatest joy educators can experience” (2002, p. 5).
The steps to the Capstone Project are detailed below. Read through all of the steps before creating your implementation plan. Save this form as a draft until all Action Research steps have been completed and all responses are documented. You will submit this form at different stages of completion throughout EDU 698.
ACTION RESEARCH PROJECT
Name:
Insert text here.
Title of Project:
Insert text here.
Date Completed:
Insert text here.
IMPLEMENTATION TIME FRAME:
Number of weeks:
Insert text here.
TIMELINE of ACTION RESEARCH PROJECT:
Start Date:
Insert text here.
End Date:
Insert text here.
AREA OF FOCUS: What is your chosen area of focus? Why did you choose this area? How does it directly impact you?
Insert text here.
RESEARCH QUESTION:
Insert text here.
DEMOGRAPHICS
DEMOGRAPHIC DATA: Where/What is the research site? Who is directly involved? What statistics will give a clear understanding of the context and culture of the research site? (Do not use name as an identifier.) Provide references for sources used.
Insert text here.
TARGET GROUP: Who are the students you are trying to impact? (Do not use names - you must use another identifier.) How do you think this strategy or content focus will benefit the target group?
Insert text here.
BASELINE DATA: What are the baseline data that support your choice for this area of focus? What patterns or trends do you see in the data? What is your proof that.
Comment this post (DL) W3-T1Pricewaterhouse Coopers is one of th.docxmccormicknadine86
Comment this post (DL) W3-T1
Pricewaterhouse Coopers is one of the Big 4 accounting firms and they are fully embraced in data analytics and innovative technology. “We deploy the transformative capabilities of data and analytics from strategy and design through to execution and technology enablement” (PricewaterhouseCoopers, 2019, para 2). They support their clients with both technology and data analysis.
PWC educates their customers in what their data analysis means for their business and how to best interpret the results. “We put leading-edge data and analytics to work solving our clients’ most critical issues, so they can focus on defining their future – faster” (PricewaterhouseCoopers, 2019, para 2). PWC is continuously moving forward with data analysis and any innovations that will further benefit their clients’ decision-making.
Much the same as PWC, Ernst & Young are also one of the Big 4 and offer data analytics services for their clients. They have taken their technology integration a step further with the use of artificial intelligence, “Infusion means that by embedding analytics and artificial intelligence (AI) into the very core of your business processes, we can help you drive capital allocation strategies and investment decisions, create an end-to-end digital audit, generate new revenue opportunities, manage risk, conduct investigations, measure financial and nonfinancial performance, capture tax big data to inform decisions, increase customer satisfaction, and improve the customer experience” (Ernst & Young, n.d., para 2). It is evident that E&Y is there for their clients in all ways that data analytics and innovative technology allow them to be. They too continue to seek new ways to benefit their clients and their use of data.
New technology appears to be the bridge necessary to close the gap between big data and it analytics. Acquiring the data is only part of the process, while “The ability to analyze meaningful and relevant data and convert data to information, knowledge, and ultimately action in time to favorably influence an organization is a key competitive differentiator” (Bumblauskas, Nold, Bumblauskas, & Igou, 2017, p. 703). The current use of technology in data analytics is still suffering some barriers with knowledgeable professionals, training and education. Moving forward, these barriers should steadily disappear as data analytics further integrates with the technology and it becomes a regular occurrence in the business structure.
Comment this post (DL) W3-T2 (AM)
All organizations disregarding their size needs meaningful data and insights specially when the organization needs to understand their target market and customer needs, it even play a crucial role in anticipating their preferences. For organizations big data provide a picture that helps understand trends, patterns and preferences from a large database produce when people interact with them and each other (simplilearn.com).
Based on the vid ...
EDUC 815Final Exam Grading RubricCriteriaLevels of Achieveme.docxtoltonkendal
EDUC 815
Final Exam Grading Rubric
Criteria
Levels of Achievement
Content 70%
Advanced
Proficient
Developing
Not present
Introduction
10 points
Persuades the reader that the topic is important by using 3-4 of relevant and quality literature published within the last 5 years. Provides a detailed overview of the topic at hand and prepares the reader for the background section of the manuscript.
9 points
Persuades the reader that the topic is important by using 3-4 pieces of relevant and quality literature published within the last 5 years. Provides an overview of the topic at hand and prepares the reader for the background section of the manuscript.
1 to 8 points
Provides support for the topic by using less than 2 pieces of literature. Provides a minimal overview of the topic at hand.
0 points
Not present
Participants
14 to 15 points
Clearly and accurately describes the target population, sample size, type of sample, and the sampling procedures. Provides demographic information and support for adequate sample size.
13 points
Describes the target population, sample size, type of sample, and the sampling procedures. Provides demographic information and some support for adequate sample size.
1 to 12 points
Somewhat describes target population, sample size, type of sample, and the sampling procedures. Does not provides demographic information and/or support for adequate sample size.
0 points
Not present
Setting
14 to 15 points
Important features of the site and treatment setting are clearly identified. The setting, especially the treatment setting is described in sufficient details so that the study could be replicated.
13 points
Important features of the site and treatment setting are mentioned. The setting, especially the treatment setting needs to be described in detail but lacking some key features.
1 to 12 points
Important features of the site and treatment setting are identified but not clearly. The setting is lacking some key features.
0 points
Not present
Research Design
14 to 15 points
Research design and all variables are clearly identified. Provides a logical and accurate rationale that is supported by research texts and other literature.
13 points
Research design and most variables are identified. Provides a rationale that is supported by research texts and other literature.
1 to 12 points
Research design and variables are inaccurately identified. Fails to provide a rationale that is supported by research texts and other literature.
0 points
Not present
Instrumentation
24 to 25 points
Clearly describes instrument including the name, purpose, and contents. Scales of measurement and the scoring procedures are clearly explained. Validity of the instrument is discussed using previous studies to establish validity. Reliability of the instrument is discussed including reliability coefficients.
22 to 23 points
Describes instrument including the name, purpose, and contents. Scales of measurement and the scoring procedures are .
Discussion Examining Nursing SpecialtiesYou have probably seen .docxduketjoy27252
Discussion: Examining Nursing Specialties
You have probably seen one or more of the many inspirational posters about decisions. A visual such as a forked road or a street sign is typically pictured, along with a quote designed to inspire.
Often decisions are not so easily inspired. Perhaps you discovered this when choosing a specialty within the MSN program. This decision is a critical part of your plan for success, and you no doubt want to get it right. This is yet another area where your network can help, as well as other sources of information that can help you make an informed choice.
To Prepare:
Reflect on your decision to pursue a specialty within the MSN program, including your professional and academic goals as they relate to your program/specialization.
By Day 3
Post
an explanation of your choice of a nursing specialty within the program. Describe any difficulties you had (or are having) in making your choice, and the factors that drove/are driving your decision. Identify at least one professional organization affiliated with your chosen specialty and provide details on becoming a member.
Support main post with 3 of more current, credible sources and cite source within content of posting and on a reference list in proper APA.
By Day 6
Be sure to offer support from at least 2 current, credible sources in each required response to classmates’ main post and cite per APA.
Respond
to at least
two
of your colleagues
on two different days
, by sharing your thoughts on their specialty, supporting their choice or offering suggestions if they have yet to choose.
Submission and Grading Information
Grading Criteria
Learning Resources
Note:
To access this week’s required library resources, please click on the link to the Course Readings List, found in the
Course Materials
section of your Syllabus.
Required Readings
Bickford, C. J., Marion, L., & Gazaway, S. (2015). Nursing: Scope and standards of practice, third edition—2015. Retrieved from https://www.augusta.edu/nursing/cnr/documents/seminar-files/pp8.28.pdf
Quinn-Szcesuil, J. (2016). Why you should join a nursing association. Retrieved from https://dailynurse.com/join-nursing-association/
Robert Wood Johnson Foundation. (2011). Implementing the ION future of nursing report—part II: The potential of interprofessional collaborative care to improve safety and quality.
Charting Nursing’s Future
, (17)1–8. Retrieved from https://www.rwjf.org/content/dam/farm/reports/issue_briefs/2011/rwjf71709
Robert Wood Johnson Foundation. (2010, November 22). Interdisciplinary collaboration improves safety, quality of care, experts say. Retrieved from https://www.rwjf.org/en/library/articles-and-news/2010/11/interdisciplinary-collaboration-improves-safety-quality-of-care-.html
Walden University. (n.d.). Master of Science in Nursing (MSN). Retrieved October 12, 2018, from https://www.waldenu.edu/masters/master-of-science-in-nursing
Document:
Academic Success and Professional.
Page 1 of 6
[377]
COM7005D
Information Security Strategy
Development
Assignment: Part 1
Date for Submission: Please refer to the timetable on ilearn
(The submission portal on ilearn will close at 14.00 UK time on the date
of submission)
Page 2 of 6
[377]
Assignment Brief
As part of the formal assessment for the programme you are required to submit an
Information Security Strategy Development assignment. Please refer to your Student
Handbook for full details of the programme assessment scheme and general information on
preparing and submitting assignments.
Learning Outcomes:
After completing the module, you should be able to:
1) Evaluate the basic external and internal threats to electronic assets and
countermeasures to thwart such threats by utilising relevant standards and best
practice guidelines.
2) Analyse the legalities of computer forensics phases and the impact of the legal
requirements on the overall information security policy.
3) Critically assess the boundaries between the different service models (SaaS, PaaS,
IaaS) and operational translations (i.e. cloud computing) and to identify the associated
risks.
4) Critically investigate a company information security strategy to provide consultation
and coaching through reporting and communication.
5) Assess, compare and judge computer media for evidentiary purposes and/or root
cause analysis.
6) Apply relevant standards, best practices and legal requirements for information security
to develop information security policies.
7) Lifelong Learning: Manage employability, utilising the skills of personal development
and planning in different contexts to contribute to society and the workplace.
Your assignment should include: a title page containing your student number, the module
name, the submission deadline and a word count; the appendices if relevant; and a
reference list in Arden University (AU) Harvard format. You should address all the elements
of the assignment task listed below. Please note that tutors will use the assessment criteria
set out below in assessing your work.
Maximum word count: 2,500 words
Please note that exceeding the word count will result in a reduction in grade proportionate to
the number of words used in excess of the permitted limit.
You must not include your name in your submission because Arden University operates
anonymous marking, which means that markers should not be aware of the identity of the
student. However, please do not forget to include your STU number.
Page 3 of 6
[377]
Assignment Task: Part 1
This assignment is worth 50% of the total marks for the module.
Using your current or previous workplace1 as the case study, please answer the
following:
1) Critically analyse the different types of software acquisition models and try to relate that
to those systems you are u.
Week 3: Assignment: Organizational Needs Assessment
Submit Assignment
Due
Mar 21 by 11:59pm
Points
125
Submitting
a file upload
Purpose
The identification of a need is the cornerstone of a project. The purpose of this assignment is to conduct an organizational needs assessment. The formulation of a comprehensive organizational needs assessment supports the professional formation of the DNP-prepared nurse. To complete the assessment of the organizational need, you will need to interview a key decision-maker at the practicum site. For students not implementing their DNP Project at a practicum site, complete the assignment as if you had interviewed a key decision-maker at a practicum site.
Course Outcomes
This assignment enables the student to meet the following course outcomes:
CO 2: Formulate a needs-based organizational assessment to inform strategic leadership decision-making. (POs 3, 5, 7)
CO 3: Develop strategies to lead project planning, implementation, management, and evaluation to promote high value healthcare. (POs 3, 5, 7)
Due Date(s)
Submit your assignment by 11:59 p.m. MT Sunday at the end of Week 3. The late assignment policy applies to this assignment.
Total points possible: 125
Page Requirement:
Length: 3-4 pages, excluding cover page and references
Instructions
To create flexibility, we are providing you options on this assignment. Concept maps are an effective way to express complex ideas, especially for visual learners. For this assignment, each of the sections can be presented
either
as a narrative
or
as a concept map.
Please note that you are not required to complete any or all sections as a concept map. If you choose to use a concept map for a section, it should be created in Microsoft Word and placed in that section of your paper. The concept map must meet all the requirements of the assignment rubric for that section. The rubric and page length requirements of the paper are unchanged.
If you need additional information about concept maps and how to create a concept map in Microsoft Word, please access the following resources.
Link (video):
Microsoft Word: Creating a Flowchart, Concept Map, or Process Map
(4:03)
Link (video):
Concept Mapping for Developing your Research
(3:37)
Review the Graduate Re-Purpose Policy in the Student Handbook, page 15:
Repurposed Work (Chamberlain University Graduate Programs only): Graduate students have the opportunity to use previously submitted ideas as a foundation for future courses. No more than 50 percent of an assignment, excluding references, may be repurposed from another Chamberlain University course (excluding practicum courses). Previous course assignments that are deemed building blocks will be notated in the syllabus by the course leader. As with every assignment, students must uphold academic integrity; therefore, students must follow the guidelines for remaining academically honest according to the Academic Integrity policy. If the instr ...
A report writingAt least 5 pagesTitle pageExecutive Su.docxfredharris32
A report writing
At least 5 pages
Title page
Executive Summary
Table of Contents (automated)
Clear Purpose and Problem
Clear Recommendations
Clear plan for implementing those recommendations
References page
easy-to-ready format
pdf so formatting doesn't shift
.
A reflection of how your life has changedevolved as a result of the.docxfredharris32
A reflection of how your life has changed/evolved as a result of the pandemic. The following are general questions to get you going (and to give you an idea of what I’m looking for).
· What has challenged you as a result of COVID-19?
· In what way has it changed your thinking of some of the topics we covered in class – food, gender, race, class, etc.?
· How has this pandemic affected your perspective of food, social media, news, and/or critical thinking (such as evaluating sources/information)?
· In what way has the shift into online learning affected your perspective of education, access to technology, and/or social inequity?
How you answer the above questions (all, a few, or just one) is up to you. In other words, what you say and how you say it, as well as what medium you want to convey the reflection is entirely your choice. The story, nonfiction essay, poem, play, art – these are all viable options in creating your reflection. But more than anything else, reflect on the impact of COVID-19 in a personal way.
2-3 pages
Double-spaced
.
A Princeton University study argues that the preferences of average.docxfredharris32
A Princeton University study argues that "the preferences of average American appear to have only a minuscule, near zero, statistically non-significant impact upon public policy." If that is indeed the case, can we still say that we have strong political institutions in the United States? Does this case pose a threat to our future economic growth?
must be atleast 400 words
.
A rapidly growing small firm does not have access to sufficient exte.docxfredharris32
A rapidly growing small firm does not have access to sufficient external financing to accommodate its planned growth. Discuss what alternatives the company can consider in order to implement its growth strategy.
How can the firm determine the cost of those alternative sources of capital?
Provide your explanations and definitions in detail and be precise. Comment on your findings. Provide references for content when necessary. Provide your work in detail and explain in your own words. Support your statements with peer-reviewed in-text citation(s) and reference(s).
.
A psychiatrist bills for 10 hours of psychotherapy and medication ch.docxfredharris32
A psychiatrist bills for 10 hours of psychotherapy and medication checks for a deceased woman. Has he committed fraud or abuse? Why? Can the deceased woman’s estate press charges if the bills were sent to Medicare, and not to the family?
S
upported by at least two references.
Must be 250 words
.
A project to put on a major international sporting competition has t.docxfredharris32
A project to put on a major international sporting competition has the following major deliverables: Sports Venues, Athlete Accommodation, Volunteer Organization, Security, Events, and Publicity (which has already been broken down into pre-event publicity and post-event publicity.) Prepare a WBS for any single major deliverable on the list. Remember the 100 percent rule, and number your objectives.
.
A professional services company wants to globalize by offering s.docxfredharris32
A professional services company wants to globalize by offering services to businesses and governments in other countries. What are the risks in globalization of services and how should the company address those risks in order to move forward with their plan?
Follow the ERM holistic Approach .Below are the holistic approach key points
1. Identify risk/challenges
2. Assess risks
3. Select risk response
4. Monitor risk
5. Communicate and report risks
6. Align ERM process to goals and objectives.
Below are challenges that need follow the ERM holistic approach:
1. Physical distance and Employees requirement in new locations.
2. Local taxes and export fees.
.
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Introduction to Epidemiology Course Project Detailed Article Cri.docxmariuse18nolet
Introduction to Epidemiology Course Project
Detailed Article Critique
Reading, interpreting, and judging the value of epidemiologic literature is a skill that you will exercise throughout your academic career in public health and beyond. To help prepare you for your thesis or capstone experience as well as future coursework, your project this term involves writing a detailed critique of an article. (See link in blackboard.) Construct your critique using complete paragraphs. DO NOT present your critique as an outline.
Please consult the table on the next page to ensure that your review includes the required major elements for this assignment. We will use this table in our evaluation of your project. Each element is worth a specified number of points. Please understand that full credit will not be awarded for simply mentioning each element. You must justify your assessment. For example, you cannot simply state, “The authors justified the importance of their study.” You need to point out how the authors did or did not meet each criterion. Your critique should not be longer than 4-6 pages double-spaced with one-inch margins. There is no minimum page requirement.
For additional assistance, please refer to the article by Young et al. (2009) that is also posted in Blackboard along with the article that you are to review.
See next page for more details. ( ( ( ( ( (
Name___________________________
Element
Possible Points
Grade
Objectives:
· What is/are the research question(s) addressed by this study?
· Did the authors justify the need for their study? What was it?
5
5
Study Design:
· Describe the study design and methods used for the study.
· Critique the overall design by addressing the following:
· Did the authors correctly identify their study design (e.g., the authors stated that their study was a cohort study when it really was a cross-sectional study)?
· Did the authors present clear definitions of their exposure/health outcomes?
· Did the authors adequately describe their sampling (e.g., convenience, consecutive, or random; response rate; study population) and data collection methods (e.g., questionnaire, records, biomarkers, surveys, etc.)?
15
10
Analysis:
· Describe the data analysis plan overall.
· Critique the analysis plan by addressing the following:
· Is the analysis appropriate for the study design (considering you level of statistical knowledge)? Was potential confounding addressed in the analysis stage? If so, how?
· Are the tables and graphs well organized and labeled? Do the tables and graphs provide enough data for reader to draw their own conclusions (e.g. do they stand alone?)?
· Are the findings within the tables/figures consistent? Is each table/figure consistent with the text and other tables/figures?
· Can the findings be attributed simply to chance?
5
10
Validity and Conclusions:
· Internal validity- Consider the potential for selection bias, information bias, and .
Evaluation Criteria for Applications and Formal Papers Level.docxSANSKAR20
Evaluation Criteria for Applications and Formal Papers
Levels of Achievement
Criteria
Outstanding Performance
Excellent Performance
Competent Performance
Proficient Performance
Room for Improvement
QUALITY OF WORK SUBMITTED -
1. The extent to which work meets the assigned criteria and work reflects graduate level critical and analytic thinking (0-30 Points)
30 to 30 points
Assignment exceeds expectations. All topics are addressed with a minimum of 75% containing exceptional breadth and depth about each of the assignment topics
25 to 29 points
Assignment exceeds expectations. All topics are addressed with a minimum of 75% containing exceptional breadth and depth about each of the assignment topics
20 to 24 points
Assignment meets expectations. All topics are addressed with a minimum of 50% containing good breadth and depth about each of the assignment topics.
16 to 19 points
Assignment meets most of the expectations. One required topic is either not addressed or inadequately addressed.
0 to 15 points
Assignment superficially meets some of the expectations. Two or more required topics are either not addressed or inadequately addressed.
QUALITY OF WORK SUBMITTED: Purpose of the paper is clear (0-5 Points)
5 to 5 points
A clear and comprehensive purpose statement is provided which delineates all required criteria.
5 to 5 points
A clear and comprehensive purpose statement is provided which delineates all required criteria.
4 to 4 points
Purpose of the assignment is stated, yet is brief and not descriptive.
1 to 3 points
Purpose of the assignment is vague.
0 to 0 points
No purpose statement was provided.
ASSIMILATION AND SYNTHESIS OF IDEAS
The extent to which the work reflects the student’s ability to-
1. Understand and interpret the assignment’s key concepts (0-10 Points)
10 to 10 points
Demonstrates the ability to critically appraise and intellectually explore key concepts.
9 to 9 points
Demonstrates the ability to critically appraise and intellectually explore key concepts.
8 to 8 points
Demonstrates a clear understanding of key concepts.
5 to 7 points
Shows some degree of understanding of key concepts.
0 to 4 points
Shows a lack of understanding of key concepts, deviates from topics.
ASSIMILATION AND SYNTHESIS OF IDEAS 2. Apply and integrate material in course resources (i.e. video, required readings, and textbook) and credible outside resources (0-20 Points)
20 to 20 points
Demonstrates and applies exceptional support of major points and integrates 2 or more credible outside sources, in addition to 3-4 course resources to support point of view.
15 to 19 points
Demonstrates and applies exceptional support of major points and integrates 2 or more credible outside sources, in addition to 3-4 course resources to support point of view.
10 to 14 points
Integrates specific information from 1 credible outside resource and 3 to 4 course resources to support major points and point of view.
3 to 9 points
Minimally includes and integrates specific ...
Group Member Discussion RubricStudent NameTotal Points PossibleTot.docxwhittemorelucilla
Group Member Discussion RubricStudent NameTotal Points PossibleTotal Points ReceivedLate Work Percentage (select from drop down menu)Late Work DeductionFinal Points Received 750.000%
Windows User: Windows User:
Select dropdown menu for percentage deducted for late work.0.000.00CriterionSuperior Criteria (100%)Excellent Criteria (95%)Satisfactory Criteria (85%)Marginal Criteria (75%)Unacceptable Criteria (0%)Points Possible Evaluation (select from drop down menu)Points ReceivedFaculty FeedbackElement 1: Initial Posting Content for Weeks 2, 4, 6 (28 points maximum; 37% of total points)Student provides an original, thought-provoking, in-depth initial post addressing more than one summative consideration. The student's post stimulates critical inquiry and uses at least five additional scholarly resources to support thinking.Student provides an original, thought-provoking, in-depth initial post addressing more than one summative consideration. The student's post stimulates critical inquiry and uses at least five additional scholarly resources to support thinking. However, there are one or two minor errors in the content of the post.Student provides a good, organized post addressing at least one summative consideration, but does not consistently demonstrate higher-order thinking. The student's post stimulates some critical inquiry and uses at least five additional scholarly resources, but some details are lacking or not accurate. Student provides a weak or incomplete post addressing at least one summative consideration. The student's post demonstrates a low level of thinking and/or resources are lacking and/or do not support thinking.Does not meet minimal standards.280%
Windows User: Windows User:
Select drop down menu for inputting grade for this criterion.0Element 2: Follow-up Responses to Colleagues and Interaction for Weeks 3, 5, 7 (27 points maximum; 36% of total points)Student engages with several peers bringing the discussion to a higher level of inquiry and investigation. Responses are thorough and fully contribute to the quality of interaction by offering constructive critique, suggestions, in-depth questions, additional resources, and stimulating thoughts and/or probes.
Student engages with several peers bringing the discussion to a higher level of inquiry and investigation. Responses are thorough and fully contribute to the quality of interaction by offering constructive critique, suggestions, in-depth questions, additional resources, and stimulating thoughts and/or probes. However, there are one or two minor errors in content of responses.Student engages with at least two peers and helps extend the discussion. Responses are good and somewhat contribute to the quality of interaction by offering constructive critique, suggestions, in-depth questions, additional resources, and stimulating thoughts and/or probes. Student engages with at least two peers. Responses are minimal and do not fully contribute to the q ...
Concordia University Page 3 of 3
Concordia University Page 1 of 3
CAPSTONE PROJECTDOCUMENTATION FORM
Action Research is an exciting, disciplined process of discovery designed to integrate theory into one’s daily practice in a way that improves educational practices and the individual conducting the research. Action Research is the Capstone Project in the Master’s of Education program for Concordia University online. It gives the educator, as a scholarly practitioner, the opportunity to examine relevant issues in his or her own classroom or school which may complicate, compromise, or complement the learning process—and to find meaningful, practical, research-based answers.
In Action Research, teachers are empowered to design a research-based plan, identify learning issues or problems, review relevant literature that examines identified problems, implement specific, research-based strategies, and discover convincing evidence that supports or contravenes their teaching strategies. The most exciting part of Action Research is the teacher can often observe student improvement during the project and can demonstrate, in a quantitative manner, the improvement of student learning. Sagor notes, “Seeing students grow is probably the greatest joy educators can experience” (2002, p. 5).
The steps to the Capstone Project are detailed below. Read through all of the steps before creating your implementation plan. Save this form as a draft until all Action Research steps have been completed and all responses are documented. You will submit this form at different stages of completion throughout EDU 698.
ACTION RESEARCH PROJECT
Name:
Insert text here.
Title of Project:
Insert text here.
Date Completed:
Insert text here.
IMPLEMENTATION TIME FRAME:
Number of weeks:
Insert text here.
TIMELINE of ACTION RESEARCH PROJECT:
Start Date:
Insert text here.
End Date:
Insert text here.
AREA OF FOCUS: What is your chosen area of focus? Why did you choose this area? How does it directly impact you?
Insert text here.
RESEARCH QUESTION:
Insert text here.
DEMOGRAPHICS
DEMOGRAPHIC DATA: Where/What is the research site? Who is directly involved? What statistics will give a clear understanding of the context and culture of the research site? (Do not use name as an identifier.) Provide references for sources used.
Insert text here.
TARGET GROUP: Who are the students you are trying to impact? (Do not use names - you must use another identifier.) How do you think this strategy or content focus will benefit the target group?
Insert text here.
BASELINE DATA: What are the baseline data that support your choice for this area of focus? What patterns or trends do you see in the data? What is your proof that.
Comment this post (DL) W3-T1Pricewaterhouse Coopers is one of th.docxmccormicknadine86
Comment this post (DL) W3-T1
Pricewaterhouse Coopers is one of the Big 4 accounting firms and they are fully embraced in data analytics and innovative technology. “We deploy the transformative capabilities of data and analytics from strategy and design through to execution and technology enablement” (PricewaterhouseCoopers, 2019, para 2). They support their clients with both technology and data analysis.
PWC educates their customers in what their data analysis means for their business and how to best interpret the results. “We put leading-edge data and analytics to work solving our clients’ most critical issues, so they can focus on defining their future – faster” (PricewaterhouseCoopers, 2019, para 2). PWC is continuously moving forward with data analysis and any innovations that will further benefit their clients’ decision-making.
Much the same as PWC, Ernst & Young are also one of the Big 4 and offer data analytics services for their clients. They have taken their technology integration a step further with the use of artificial intelligence, “Infusion means that by embedding analytics and artificial intelligence (AI) into the very core of your business processes, we can help you drive capital allocation strategies and investment decisions, create an end-to-end digital audit, generate new revenue opportunities, manage risk, conduct investigations, measure financial and nonfinancial performance, capture tax big data to inform decisions, increase customer satisfaction, and improve the customer experience” (Ernst & Young, n.d., para 2). It is evident that E&Y is there for their clients in all ways that data analytics and innovative technology allow them to be. They too continue to seek new ways to benefit their clients and their use of data.
New technology appears to be the bridge necessary to close the gap between big data and it analytics. Acquiring the data is only part of the process, while “The ability to analyze meaningful and relevant data and convert data to information, knowledge, and ultimately action in time to favorably influence an organization is a key competitive differentiator” (Bumblauskas, Nold, Bumblauskas, & Igou, 2017, p. 703). The current use of technology in data analytics is still suffering some barriers with knowledgeable professionals, training and education. Moving forward, these barriers should steadily disappear as data analytics further integrates with the technology and it becomes a regular occurrence in the business structure.
Comment this post (DL) W3-T2 (AM)
All organizations disregarding their size needs meaningful data and insights specially when the organization needs to understand their target market and customer needs, it even play a crucial role in anticipating their preferences. For organizations big data provide a picture that helps understand trends, patterns and preferences from a large database produce when people interact with them and each other (simplilearn.com).
Based on the vid ...
EDUC 815Final Exam Grading RubricCriteriaLevels of Achieveme.docxtoltonkendal
EDUC 815
Final Exam Grading Rubric
Criteria
Levels of Achievement
Content 70%
Advanced
Proficient
Developing
Not present
Introduction
10 points
Persuades the reader that the topic is important by using 3-4 of relevant and quality literature published within the last 5 years. Provides a detailed overview of the topic at hand and prepares the reader for the background section of the manuscript.
9 points
Persuades the reader that the topic is important by using 3-4 pieces of relevant and quality literature published within the last 5 years. Provides an overview of the topic at hand and prepares the reader for the background section of the manuscript.
1 to 8 points
Provides support for the topic by using less than 2 pieces of literature. Provides a minimal overview of the topic at hand.
0 points
Not present
Participants
14 to 15 points
Clearly and accurately describes the target population, sample size, type of sample, and the sampling procedures. Provides demographic information and support for adequate sample size.
13 points
Describes the target population, sample size, type of sample, and the sampling procedures. Provides demographic information and some support for adequate sample size.
1 to 12 points
Somewhat describes target population, sample size, type of sample, and the sampling procedures. Does not provides demographic information and/or support for adequate sample size.
0 points
Not present
Setting
14 to 15 points
Important features of the site and treatment setting are clearly identified. The setting, especially the treatment setting is described in sufficient details so that the study could be replicated.
13 points
Important features of the site and treatment setting are mentioned. The setting, especially the treatment setting needs to be described in detail but lacking some key features.
1 to 12 points
Important features of the site and treatment setting are identified but not clearly. The setting is lacking some key features.
0 points
Not present
Research Design
14 to 15 points
Research design and all variables are clearly identified. Provides a logical and accurate rationale that is supported by research texts and other literature.
13 points
Research design and most variables are identified. Provides a rationale that is supported by research texts and other literature.
1 to 12 points
Research design and variables are inaccurately identified. Fails to provide a rationale that is supported by research texts and other literature.
0 points
Not present
Instrumentation
24 to 25 points
Clearly describes instrument including the name, purpose, and contents. Scales of measurement and the scoring procedures are clearly explained. Validity of the instrument is discussed using previous studies to establish validity. Reliability of the instrument is discussed including reliability coefficients.
22 to 23 points
Describes instrument including the name, purpose, and contents. Scales of measurement and the scoring procedures are .
Discussion Examining Nursing SpecialtiesYou have probably seen .docxduketjoy27252
Discussion: Examining Nursing Specialties
You have probably seen one or more of the many inspirational posters about decisions. A visual such as a forked road or a street sign is typically pictured, along with a quote designed to inspire.
Often decisions are not so easily inspired. Perhaps you discovered this when choosing a specialty within the MSN program. This decision is a critical part of your plan for success, and you no doubt want to get it right. This is yet another area where your network can help, as well as other sources of information that can help you make an informed choice.
To Prepare:
Reflect on your decision to pursue a specialty within the MSN program, including your professional and academic goals as they relate to your program/specialization.
By Day 3
Post
an explanation of your choice of a nursing specialty within the program. Describe any difficulties you had (or are having) in making your choice, and the factors that drove/are driving your decision. Identify at least one professional organization affiliated with your chosen specialty and provide details on becoming a member.
Support main post with 3 of more current, credible sources and cite source within content of posting and on a reference list in proper APA.
By Day 6
Be sure to offer support from at least 2 current, credible sources in each required response to classmates’ main post and cite per APA.
Respond
to at least
two
of your colleagues
on two different days
, by sharing your thoughts on their specialty, supporting their choice or offering suggestions if they have yet to choose.
Submission and Grading Information
Grading Criteria
Learning Resources
Note:
To access this week’s required library resources, please click on the link to the Course Readings List, found in the
Course Materials
section of your Syllabus.
Required Readings
Bickford, C. J., Marion, L., & Gazaway, S. (2015). Nursing: Scope and standards of practice, third edition—2015. Retrieved from https://www.augusta.edu/nursing/cnr/documents/seminar-files/pp8.28.pdf
Quinn-Szcesuil, J. (2016). Why you should join a nursing association. Retrieved from https://dailynurse.com/join-nursing-association/
Robert Wood Johnson Foundation. (2011). Implementing the ION future of nursing report—part II: The potential of interprofessional collaborative care to improve safety and quality.
Charting Nursing’s Future
, (17)1–8. Retrieved from https://www.rwjf.org/content/dam/farm/reports/issue_briefs/2011/rwjf71709
Robert Wood Johnson Foundation. (2010, November 22). Interdisciplinary collaboration improves safety, quality of care, experts say. Retrieved from https://www.rwjf.org/en/library/articles-and-news/2010/11/interdisciplinary-collaboration-improves-safety-quality-of-care-.html
Walden University. (n.d.). Master of Science in Nursing (MSN). Retrieved October 12, 2018, from https://www.waldenu.edu/masters/master-of-science-in-nursing
Document:
Academic Success and Professional.
Page 1 of 6
[377]
COM7005D
Information Security Strategy
Development
Assignment: Part 1
Date for Submission: Please refer to the timetable on ilearn
(The submission portal on ilearn will close at 14.00 UK time on the date
of submission)
Page 2 of 6
[377]
Assignment Brief
As part of the formal assessment for the programme you are required to submit an
Information Security Strategy Development assignment. Please refer to your Student
Handbook for full details of the programme assessment scheme and general information on
preparing and submitting assignments.
Learning Outcomes:
After completing the module, you should be able to:
1) Evaluate the basic external and internal threats to electronic assets and
countermeasures to thwart such threats by utilising relevant standards and best
practice guidelines.
2) Analyse the legalities of computer forensics phases and the impact of the legal
requirements on the overall information security policy.
3) Critically assess the boundaries between the different service models (SaaS, PaaS,
IaaS) and operational translations (i.e. cloud computing) and to identify the associated
risks.
4) Critically investigate a company information security strategy to provide consultation
and coaching through reporting and communication.
5) Assess, compare and judge computer media for evidentiary purposes and/or root
cause analysis.
6) Apply relevant standards, best practices and legal requirements for information security
to develop information security policies.
7) Lifelong Learning: Manage employability, utilising the skills of personal development
and planning in different contexts to contribute to society and the workplace.
Your assignment should include: a title page containing your student number, the module
name, the submission deadline and a word count; the appendices if relevant; and a
reference list in Arden University (AU) Harvard format. You should address all the elements
of the assignment task listed below. Please note that tutors will use the assessment criteria
set out below in assessing your work.
Maximum word count: 2,500 words
Please note that exceeding the word count will result in a reduction in grade proportionate to
the number of words used in excess of the permitted limit.
You must not include your name in your submission because Arden University operates
anonymous marking, which means that markers should not be aware of the identity of the
student. However, please do not forget to include your STU number.
Page 3 of 6
[377]
Assignment Task: Part 1
This assignment is worth 50% of the total marks for the module.
Using your current or previous workplace1 as the case study, please answer the
following:
1) Critically analyse the different types of software acquisition models and try to relate that
to those systems you are u.
Week 3: Assignment: Organizational Needs Assessment
Submit Assignment
Due
Mar 21 by 11:59pm
Points
125
Submitting
a file upload
Purpose
The identification of a need is the cornerstone of a project. The purpose of this assignment is to conduct an organizational needs assessment. The formulation of a comprehensive organizational needs assessment supports the professional formation of the DNP-prepared nurse. To complete the assessment of the organizational need, you will need to interview a key decision-maker at the practicum site. For students not implementing their DNP Project at a practicum site, complete the assignment as if you had interviewed a key decision-maker at a practicum site.
Course Outcomes
This assignment enables the student to meet the following course outcomes:
CO 2: Formulate a needs-based organizational assessment to inform strategic leadership decision-making. (POs 3, 5, 7)
CO 3: Develop strategies to lead project planning, implementation, management, and evaluation to promote high value healthcare. (POs 3, 5, 7)
Due Date(s)
Submit your assignment by 11:59 p.m. MT Sunday at the end of Week 3. The late assignment policy applies to this assignment.
Total points possible: 125
Page Requirement:
Length: 3-4 pages, excluding cover page and references
Instructions
To create flexibility, we are providing you options on this assignment. Concept maps are an effective way to express complex ideas, especially for visual learners. For this assignment, each of the sections can be presented
either
as a narrative
or
as a concept map.
Please note that you are not required to complete any or all sections as a concept map. If you choose to use a concept map for a section, it should be created in Microsoft Word and placed in that section of your paper. The concept map must meet all the requirements of the assignment rubric for that section. The rubric and page length requirements of the paper are unchanged.
If you need additional information about concept maps and how to create a concept map in Microsoft Word, please access the following resources.
Link (video):
Microsoft Word: Creating a Flowchart, Concept Map, or Process Map
(4:03)
Link (video):
Concept Mapping for Developing your Research
(3:37)
Review the Graduate Re-Purpose Policy in the Student Handbook, page 15:
Repurposed Work (Chamberlain University Graduate Programs only): Graduate students have the opportunity to use previously submitted ideas as a foundation for future courses. No more than 50 percent of an assignment, excluding references, may be repurposed from another Chamberlain University course (excluding practicum courses). Previous course assignments that are deemed building blocks will be notated in the syllabus by the course leader. As with every assignment, students must uphold academic integrity; therefore, students must follow the guidelines for remaining academically honest according to the Academic Integrity policy. If the instr ...
A report writingAt least 5 pagesTitle pageExecutive Su.docxfredharris32
A report writing
At least 5 pages
Title page
Executive Summary
Table of Contents (automated)
Clear Purpose and Problem
Clear Recommendations
Clear plan for implementing those recommendations
References page
easy-to-ready format
pdf so formatting doesn't shift
.
A reflection of how your life has changedevolved as a result of the.docxfredharris32
A reflection of how your life has changed/evolved as a result of the pandemic. The following are general questions to get you going (and to give you an idea of what I’m looking for).
· What has challenged you as a result of COVID-19?
· In what way has it changed your thinking of some of the topics we covered in class – food, gender, race, class, etc.?
· How has this pandemic affected your perspective of food, social media, news, and/or critical thinking (such as evaluating sources/information)?
· In what way has the shift into online learning affected your perspective of education, access to technology, and/or social inequity?
How you answer the above questions (all, a few, or just one) is up to you. In other words, what you say and how you say it, as well as what medium you want to convey the reflection is entirely your choice. The story, nonfiction essay, poem, play, art – these are all viable options in creating your reflection. But more than anything else, reflect on the impact of COVID-19 in a personal way.
2-3 pages
Double-spaced
.
A Princeton University study argues that the preferences of average.docxfredharris32
A Princeton University study argues that "the preferences of average American appear to have only a minuscule, near zero, statistically non-significant impact upon public policy." If that is indeed the case, can we still say that we have strong political institutions in the United States? Does this case pose a threat to our future economic growth?
must be atleast 400 words
.
A rapidly growing small firm does not have access to sufficient exte.docxfredharris32
A rapidly growing small firm does not have access to sufficient external financing to accommodate its planned growth. Discuss what alternatives the company can consider in order to implement its growth strategy.
How can the firm determine the cost of those alternative sources of capital?
Provide your explanations and definitions in detail and be precise. Comment on your findings. Provide references for content when necessary. Provide your work in detail and explain in your own words. Support your statements with peer-reviewed in-text citation(s) and reference(s).
.
A psychiatrist bills for 10 hours of psychotherapy and medication ch.docxfredharris32
A psychiatrist bills for 10 hours of psychotherapy and medication checks for a deceased woman. Has he committed fraud or abuse? Why? Can the deceased woman’s estate press charges if the bills were sent to Medicare, and not to the family?
S
upported by at least two references.
Must be 250 words
.
A project to put on a major international sporting competition has t.docxfredharris32
A project to put on a major international sporting competition has the following major deliverables: Sports Venues, Athlete Accommodation, Volunteer Organization, Security, Events, and Publicity (which has already been broken down into pre-event publicity and post-event publicity.) Prepare a WBS for any single major deliverable on the list. Remember the 100 percent rule, and number your objectives.
.
A professional services company wants to globalize by offering s.docxfredharris32
A professional services company wants to globalize by offering services to businesses and governments in other countries. What are the risks in globalization of services and how should the company address those risks in order to move forward with their plan?
Follow the ERM holistic Approach .Below are the holistic approach key points
1. Identify risk/challenges
2. Assess risks
3. Select risk response
4. Monitor risk
5. Communicate and report risks
6. Align ERM process to goals and objectives.
Below are challenges that need follow the ERM holistic approach:
1. Physical distance and Employees requirement in new locations.
2. Local taxes and export fees.
.
A presentation( PowerPoint) on the novel, Disgrace by J . M. Coetzee.docxfredharris32
A presentation( PowerPoint) on the novel, Disgrace by J . M. Coetzee. t
This is the prompt:
" Black and white relationships in Disgrace cross lines from the personal to the political. Examine and evaluate the way South African politics impacts the personal relationships for Professor Lurie and his daughter."
8 slides
.
a presentatiion on how the over dependence of IOT AI and robotics di.docxfredharris32
a presentatiion on how the over dependence of IOT AI and robotics distances the need for a medical practicioner for a patient .
do you agree with the technology or do you prefer the traditional medical system with doctor pateint diagnosis?
give examples or instances on situtions
.
A nursing care plan (NCP) is a formal process that includes .docxfredharris32
A
nursing care plan (NCP)
is a formal process that includes correctly identifying existing needs, as well as recognizing potential needs or risks. Care plans also provide a means of communication among nurses, their patients, and other healthcare providers to achieve health care outcomes. Without the nursing care planning process, quality and consistency in patient care would be lost.
Medical Diagnosis: Alzheimer's disease
.
A nurse educator is preparing an orientation on culture and the wo.docxfredharris32
A nurse educator is preparing an orientation on culture and the workplace. There is a need to address the many cultures that seek healthcare services and how to better understand the culture. This presentation will examine the role of the nurse as a culturally diverse practitioner.
Choose a culture that you feel less knowledgeable about: HISPANIC OR MEXICAN
Compare this culture with your own culture: ISLAND PACIFIC
Analyze the historical, socioeconomic, political, educational, and topographical aspects of this culture
What are the appropriate interdisciplinary interventions for hereditary, genetic, and endemic diseases and high-risk health behaviors within this culture?
What are the influences of their value systems on childbearing and bereavement practices
What are their sources of strength, spirituality, and magicoreligious beliefs associated with health and health care?
What are the health-care practices: acute versus preventive care; barriers to health care; the meaning of pain and the sick role; and traditional folk medicine practices?
What are cultural issues related to learning styles, autonomy, and educational preparation of content for this culture?
This PowerPoint® (Microsoft Office) or Impress® (Open Office) presentation should be a minimum of 20 slides, including a title, introduction, conclusion and reference slide, with detailed speaker notes and recorded audio comments for all content slides. Use at least four scholarly sources and make certain to review the module’s Signature Assignment Rubric before starting your presentation. This presentation is worth 400 points for quality content and presentation.
Total Point Value of Signature Assignment:
400 points
.
A NOVEL TEACHER EVALUATION MODEL 1 Branching Paths A Nove.docxfredharris32
A NOVEL TEACHER EVALUATION MODEL 1
Branching Paths: A Novel Teacher Evaluation Model for Faculty Development
Kim A. Park,1 James P. Bavis,1 and Ahn G. Nu2
1Department of English, Purdue University
2Center for Faculty Education, Department of Educational Psychology, Quad City University
Author Note
Kim A. Park https://orcid.org/0000-0002-1825-0097
James P. Bavis is now at the MacLeod Institute for Music Education, Green Bay, WI.
We have no known conflict of interest to disclose.
Correspondence concerning this article should be addressed to Ahn G. Nu, Dept. of
Educational Psychology, 253 N. Proctor St., Quad City, WA, 09291. Email: [email protected]
jforte
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Page numbers begin on the first page and follow on every subsequent page without interruption. No other information (e.g., authors' last names) are required.
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Note: Green text boxes contain explanations of APA 7's paper formatting guidelines...
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...while blue text boxes contain directions for writing and citing in APA 7.
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The paper's title should be centered, bold, and written in title case. It should be three or four lines below the top margin of the page. In this sample paper, we've put three blank lines above the title.
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The running head is a shortened version of the paper's title that appears on every page. It is written in all capitals, and it should be flush left in the document's header. No "Running head:" label is included in APA 7. If the paper's title is fewer than 50 characters (including spaces and punctuation), the actual title may be used rather than a shortened form.
jforte
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Author notes contain the following parts in this order:
1. Bold, centered "Author Note" label.
2. ORCID iDs
3. Changes of author affiliation.
4. Disclosures/ acknowledgments
5. Contact information.
Each part is optional (i.e., you should omit any parts that do not apply to your manuscript, or omit the note entirely if none apply).
Format each item as its own indented paragraph.
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Authors' names appear two lines below the title. They should be written as follows:
First name, middle initial(s), last name.
Omit all professional titles and/or degrees (e.g., Dr., Rev., PhD, MA).
jforte
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Authors' affiliations follow immediately after their names. If the authors represent multiple institutions, as is the case in this sample, use superscripted numbers to indicate which author is affiliated with which institution. If all authors represent the same institution, do not use any numbers.
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ORCID is an organization that allows researchers and scholars to register professional profiles so that they can easily connect with one another. To include an ORCID iD in your author note, simply provide the author's name, followed by the green iD icon (hyperlinked to the URL that follows) and a hyperlink to the appropriate ORCID page.
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A Look at the Marburg Fever OutbreaksThis week we will exami.docxfredharris32
A Look at the Marburg Fever Outbreaks
This week we will examine: Marburg Fever in Africa.
MARBURG VIRUS
The largest and deadliest outbreak of Marburg hemorrhagic fever on record occurred in 2005. The Ministry of Health (MOH) in Angola reported a total of 374 cases, including 329 deaths reported countrywide. The Angolan Government, WHO and other partners,
established a surveillance system for identification of suspected cases and follow up of their contacts. Mobile teams were sent to the field to investigate rumors, obtain clinical specimens for laboratory tests, hospitalize suspected patients and monitor their contacts
B. For the Marburg fever case, you will discuss the major obstacles and difficulties that public health officials and health care workers had in controlling the outbreak of Marburg fever and the solutions they found to these difficulties. Your response must also include the following:
1. What is Marburg hemorrhagic fever?
2. How is Marburg hemorrhagic fever prevented?
3. What needs to be done to address the threat of Marburg hemorrhagic fever?
Must be at least 250 words and supported by at least two references
.
A network consisting of M cities and M-1 roads connecting them is gi.docxfredharris32
A network consisting of M cities and M-1 roads connecting them is given. Cities are labeled with distinct integers within the range [o. (M-1)] Roads connect cities in such a way that each pair of distinct cities is connected either by a direct road or along a path consisting of direct roads. There is exactly one way to reach any city from any other city. In other words, cities and direct roads form a tree. The number of direct roads that must be traversed is called the distance between these two cities. For example, consider the following network consisting of ten cities and nine roads: 2 0 Cities 2 and 4 are connected directly, so the distance between them is 1. Cities 4 and 7 are connected by a path consisting of the direct roads 4-0,0-9 and 9-7; hence the distance between them is 3. One of the cities is the capital, and the goal is to count the number of cities positioned away from it at each of the distances 1,2,3,.., M -1. If city number 1 is the capital, then the cities positioned at the various distances from the If city number 1 is the capital, then the cities positioned at the various distances from the capital would be as follows: . 9 is at a distance of 1 · 0, 3, 7 are at a distance of 2; 8,4 are at a distance of 3; 2, 5, 6 are at a distance of 4. Write a function: class
Solution
t public int[] solution(int[] T)h that, given a non-empty array T consisting of M integers describing a network of M cities and M 1 roads, returns an array consisting of M-1 integers, specifying the number of cities positioned at each distance 1, 2,..., M - 1. Array T describes a network of cities as follows: · if T[P] Q and P = Q, then P is the capital; if T[P Q and P Q, then there is a direct road between cities P and Q. For example, given the following array T consisting of ten elements: T[2] 4 T[6]8 T[9] = 1 = 9 T[7] the function should return [1, 3, 2,3,0,0,0,0,01, as explained above. Write an efficient algorithm for the following assumptions: M is an integer within the range [1..100,000]; each element of array T is an integer within the range [0.M-1] there is exactly one (possibly indirect) connection between any two distinct cities.
.
A minimum 20-page (not including cover page, abstract, table of cont.docxfredharris32
A minimum 20-page (not including cover page, abstract, table of contents, and references), double-spaced, APA formatted academic research paper.
Topic - Cash flow estimation practices
The structure of the paper is as follows:
Abstract
Introduction
Statement of the problem
The purpose of the study
Method of the study (qualitative, quantitative or mixed study)
Literature review (10-15 peer-reviewed articles)
Results & Analysis
Conclusion & recommendations
References
.
A major component of being a teacher is the collaboration with t.docxfredharris32
A major component of being a teacher is the collaboration with the other teachers in your grade level to share ideas, resources, and learning activities in order to enhance instruction and meet the diverse needs of students.
For this assignment, create a 7-10 slide digital presentation professional development, for your peers, highlighting two forms of technology that can be used to enhance math instruction.
Include a title slide, reference slide, and presenter’s notes.
For each form of technology, include the following components:
A detailed description and how the technology works to engage students and enhance math instruction
A rationale for the benefits of using the technological tools to facilitate the creation or transfer of knowledge and skills
The safety precautions including the safe, legal, and ethical use of technology both at home and at school.
Description of how each form of technology can be used to support collaboration with families, students, and school personnel.
Description of how each form of technology engages students in collaboration with others in face-to-face or virtual environments
Support your findings with a minimum of three scholarly resources.
.
a mad professor slips a secret tablet in your food that makes you gr.docxfredharris32
a mad professor slips a secret tablet in your food that makes you grow up as normal,but then remain at that age until you are 200 years old.this means you cant die until at least 2201 AD. in 2150,you send your diary back through time to you,today , in 2012.by reading the the diary,describe life in london in 2150AD descrie technology,and people you meat
.
A New Mindset for Leading Change [WLO 1][CLO 6]Through.docxfredharris32
A New Mindset for Leading Change [WLO: 1][CLO: 6]
Throughout the MAECEL program so far, you have encountered many opportunities to consider how you can make a difference as a professional and as a leader in the field of early childhood education. As Fullan (1993) states, as educators our purpose is “to make a difference in the lives of students regardless of background, to help produce citizens who can live and work productively in increasingly dynamically complex societies” (p. 4). Meaning, you, as an early childhood education professional and leader, have incredible capacity and potential to be a change agent who makes a positive difference in the lives of young children. With this new mindset in mind, please respond to each of the following prompts to share your insights on influencing educational change through action research.
· If you were to implement this study, what would be your next steps? How might implementation support better outcomes for young children and their families?
· Given the conditions discussed in Chapter 7 of the Mills (2014) textbook, discuss how you could support these conditions in an organization from the perspective of your current or future role in early childhood education.
· Share what it means to you to be a change agent in early childhood education and how you can leverage inquiry and research skills to promote quality education for young children.
.
A N A M E R I C A N H I S T O R YG I V E M EL I B.docxfredharris32
A N A M E R I C A N H I S T O R Y
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L I B E R T Y !
W . W . N O R T O N & C O M P A N Y
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V o l u m e 2 : F r o m 1 8 6 5
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Fairbanks
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San Juan
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The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
1. 5/25/2020 Rubric Detail – 31228.202030
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Name: ITS836 (8 Week) Research Paper Rubric
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No requirements are met
Includes a few of the required components as speci�ed in the
assignment.
Includes some of the required components as speci�ed in the
assignment.
Includes most of the required components as speci�ed in the
assignment.
2. Includes all of the required components as speci�ed in the
assignment.
Requirements
--
No Evidence 0 (0.00%) points
Limited Evidence 3 (3.00%) points
Below Expectations 7 (7.00%) points
Approaches Expectations 11 (11.00%) points
Meets Expectations 15 (15.00%) points
Fails to provide enough content to show a demonstration of
knowledge
Major errors or omissions in demonstration of knowledge.
Some signi�cant but not major errors or omissions in
demonstration of knowledge.
A few errors or omissions in demonstration of knowledge.
Demonstrates strong or adequate knowledge of the materials;
correctly represents knowledge
from the readings and sources.
Content
--
No Evidence 0 (0.00%) points
Limited Evidence 3 (3.00%) points
3. Below Expectations 7 (7.00%) points
Approaches Expectations 11 (11.00%) points
Meets Expectations 15 (15.00%) points
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Fails to provide a critical thinking analysis and interpretation
Major errors or omissions in analysis and interpretation.
Some signi�cant but not major errors or omissions in analysis
and interpretation.
A few errors or omissions in analysis and interpretation.
Provides a strong critical analysis and interpretation of the
information given.
Critical Analysis
--
No Evidence 0 (0.00%) points
Limited Evidence 5 (5.00%) points
4. Below Expectations 10 (10.00%) points
Approaches Expectations 15 (15.00%) points
Meets Expectations 20 (20.00%) points
Fails to demonstrate problem solving.
Major errors or omissions in problem solving.
Some signi�cant but not major errors or omissions in problem
solving.
A few errors or omissions in problem solving.
Demonstrates strong or adequate thought and insight in problem
solving.
Problem Solving
--
No Evidence 0 (0.00%) points
Limited Evidence 5 (5.00%) points
Below Expectations 10 (10.00%) points
Approaches Expectations 15 (15.00%) points
Meets Expectations 20 (20.00%) points
Source or example selection and integration of knowledge from
the course is clearly de�cient.
Sources or examples meet required criteria and are poorly
chosen to provide substance and
5. perspectives on the issue under examination.
Sources or examples meet required criteria but are less than
adequately chosen to provide
substance and perspectives on the issue under examination.
Sources or examples meet required criteria but are less than
adequately chosen to provide
substance and perspectives on the issue under examination.
Sources/Examples
--
No Evidence 0 (0.00%) points
Limited Evidence 2 (2.00%) points
Below Expectations 4 (4.00%) points
Approaches Expectations 7 (7.00%) points
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Sources or examples meet required criteria and are well chosen
to provide substance and
perspectives on the issue under examination.
Meets Expectations 10 (10.00%) points
Project is not organized or well written, and is not in proper
6. paper format. Poor-quality work;
unacceptable in terms of grammar and spelling.
Project is poorly organized; does not follow proper paper
format. Inconsistent to inadequate
sentence and paragraph development; numerous errors in
grammar and spelling.
Project is adequately organized and written, and is in proper
format as outlined in the
assignment. Reasonably good sentence and paragraph structure;
signi�cant number of errors in
grammar and spelling.
Project is fairly well organized and written, and is in proper
format as outlined in the assignment.
Reasonably good sentence and paragraph structure; signi�cant
number of errors in grammar
and spelling.
Demonstrates strong or adequate thought and insight in problem
solving.
Organization, Grammar, Style
--
No Evidence 0 (0.00%) points
Limited Evidence 2 (2.00%) points
Below Expectations 4 (4.00%) points
Approaches Expectations 7 (7.00%) points
Meets Expectations 10 (10.00%) points
7. Numerous errors in APA formatting, with more than eight
signi�cant errors.
Numerous errors in APA formatting, with more than �ve
signi�cant errors.
Signi�cant errors in APA formatting, with four to �ve
signi�cant errors.
Sources or examples meet required criteria but are less than
adequately chosen to provide
substance and perspectives on the issue under examination.
Sources or examples meet required criteria and are well chosen
to provide substance and
perspectives on the issue under examination.
Proper use of APA formatting
--
No Evidence 0 (0.00%) points
Limited Evidence 2 (2.00%) points
Below Expectations 4 (4.00%) points
Approaches Expectations 7 (7.00%) points
Meets Expectations 10 (10.00%) points
Name:ITS836 (8 Week) Research Paper Rubric
Description:Please use this rubric for grading research papers
Exit
8. 5/25/2020 Rubric Detail – 31228.202030
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1
Learning Analytics or Educational Data Mining? This is the
Question...
Daniela Marcu
Ștefan cel Mare University of Suceava
Str. Universității 13, Suceava 720229
Phone: 0230 216 147
[email protected]
Mirela Danubianu
Ștefan cel Mare University of Suceava
Str. Universității 13, Suceava 720229
Phone: 0230 216 147
[email protected]
Abstract
In full expansion, a vital area such as education could not
remain indifferent to the use of
information and communication technology. Over the past two
decades we have witnessed the
9. emergence and development of e-learning systems, the
proliferation of MOOCs, and generally the
rise of Technology Enhanced Education. All of these
contributed to generation and storage of
unprecedented volumes of data concerning all areas of learning.
At the same time, domains such as data mining and big data
analytics have emerged and
developed. Their applications in education have spawned new
areas of research such as educational
data mining or learning analytics.
As an interdisciplinary research area Educational Data Mining
(EDM) aims to explore data
from educational environment to build models based on which
students' behavior and results are
better understood. In fact, EDM is a complex process that
consists of a few steps grouped in three
stages: data preprocessing, modelling and postprocessing. It
transforms raw data from educational
environments in useful information that could influence in a
positive way the educational process.
According to Society for Learning Analytics Research (SoLAR)
which took over the
wording of the first International Conference on Learning
Analytics and Knowledge, learning
analytics is ”the measurement, collection, analysis and reporting
of data about learners and their
contexts for purposes of understanding and optimizing learning
and the environments in which it
occurs” (Siemens, 2011).
This paper proposes a comparative study of the two concepts:
EDM and learning analytics.
Due to certain voices in the scientific environment that claim
10. that the two terms refer to the
same thing, we want to emphasize the similarities and
differences between them, and how each one
can serve to raise the quality in educational processes.
Keywords : EDM; LA; Data Mining; Education.
1. Introduction
The educational community has an interest in the great potential
of education. Why are
researchers so enthusiastic about this? The answer is simple.
Seeing the impact of applying data
mining to exploiting large data volumes and analyzing data
from areas such as the business
environment, social media, and other scientific areas, we can
think of the benefits for the education
system. If we could adapt the methods of finding models in the
data, used for analyzing the online
activity of clients and social media users for the educational
environment, we could get closer
evidence of reality on the activities of the training system.
The widespread use of computer-based pre-university learning,
the development of Web-
based courses, are additional reasons for EDM and LA research.
Designing educational policies based on practical evidence
provided by researchers can
bring benefits to the educational system.
11. BRAIN – Broad Research in Artificial Intelligence and
Neuroscience
Volume 10, Special Issue 2 (October, 2019), ISSN 2067-3957
2
The exploitation of large volumes of data from different
domains is done using specific
techniques and methods. It helps to develop tools to facilitate
progress in these areas.
The science of extracting useful information from large volumes
of data is called Data
Mining (DM) (Hand, Mannila & Smyth, 2001).
The concept is based on three key areas: statistics, artificial
intelligence and machine
learning (Figure 1).
Figure 1. Data Mining
Initially, DM used statistical algorithms. Specific techniques
such as decision trees,
association rules, clustering, artificial neural networks, and
others have been developed (Șușnea,
2012).
Applying exploitation methods for educational system data to
build models to better
understand students' behavior and outcomes is named
Educational Data Mining (EDM). Since data
and education issues are different from those in other areas,
classical DM methods have been
12. improved and supplemented with EDM specific methods
(Romero & Ventura, 2007). According to
some authors, there are four areas of application of EDM aimed
at: improving student modeling and
domain modeling, e-learning and scientific research (Baker,
2012).
In order to better understand learning, data from pupils and
from the educational
environment is measured, collected and analyzed. This is the
learning analysis and is a related field
of EDM. Among the Learning Analytics (LA) methods we can
list:
Buckingham Shum, 2012).
In the following sections we propose to detail relevant aspects
about EDM and LA in order
to provide viable arguments in a comparative study of the two
concepts.
2. Educational Data Mining
Over the past 10 years, the field of research aimed to exploit the
unique types of data from
education has developed quite internationally. In 2011, in
Massachusetts USA, the International
EDM Working Group (established in 2007) created the
International Society for EDM (online:
http://educationaldatamining.org/about/). Romania is, however,
at a pioneering stage in EDM.
There is currently a growing interest in using computers in
13. learning and Web-based training. With
the rapid increase in the volume of learning software resources,
the Romanian educational system
also accumulates huge amounts of data from students, teachers,
parents, libraries, secretariats, etc.
Getting the information needed to build models to improve the
quality of managerial decisions
becomes one of the greatest challenges of the present.
Traditional research in the field of education is time-consuming
and often non-ecological
through the waste of material resources. Developing an
experimental study, such as combating
school absenteeism, involves firstly the selection of schools,
teachers and pupils. It follows the
definition of strategies that lead to the identification of sources
of school stress, increasing the
D. Marcu, M. Danubianu - Learning Analytics or Educational
Data Mining? This is the Question...
3
motivation of students to attend classes, trust in school, family,
and so on. However, the studies
depend on context, class, geography, economic development,
teacher-student relationships.
Changing any parameter can lead to very different conclusions.
Soon there may be new factors that
could not be taken into consideration earlier in the demotivation
of students towards school. Making
traditional new studies for this topic involves the use of
important temporal resources.
14. By comparison, EDM proves to be more efficient. The analysis
of existing data in the
educational system through the use of specific EDM methods
allows the identification of new
models for new contexts. An enormous advantage is that the
same methods can be applied to
different data generating specific results without the need for
new analysis strategies.
More specifically, let's take the example of a course designed
for web-based training
(Romero, Ventura, De Bra, 2004). Traditionally, evaluating the
effectiveness of a course is done by
analyzing the results obtained by the student upon completion
of the course, which does not
necessarily lead to the improvement of the material or methods
and teaching tools used for the
future course versions. In fact, in the Romanian pre-university
system, the updating of educational
programs and educational resources does not present the
periodicity expected by the society.
What would it be like the knowledge of EDM data exploitation?
EDM methods aim at
discovering correlation rules between course components
(content, questions, various activities) and
student activities. In the Knowledge Discovery with Genetic
Programming for providing feedback
to the courseware author, C. Romero, S. Ventura and P. Bra
describe the four main steps in
building a software based on EDM (Romero, Ventura, De Bra,
2004): development, use,
discovering knowledge, improving
Other classification has three stages: preprocessing, data
exploitation and post processing
15. [3]. The cycle of these steps is illustrated in Figure 2.
Figure 2. Stages of the process of converting data into
information
If we refer again to the analysis of the efficiency of a course, in
the first stage, the
preprocessing is performed various operations such as:
formation on
pedagogical and methodological
aspects
time spent in the course, the
sections visited, the scores obtained and other interactions
appropriate for processing.
In the next step, EDM-specific algorithms are applied to obtain
different correlation rules.
The models will provide information in different formats for
analysis: numerical results of the
coefficients, tables, diagrams, correlation matrices (an example
is illustrated in Appendix 1 -
Correlation matrix obtained with the DataLab application based
on the results of the Olympiad of
computer science).
One of the most important rules for discovering knowledge is
if-else. Several such rules can
16. be defined in EDM: Association, Classification and Prediction
(Klosgen & Zytkow, 2002).
BRAIN – Broad Research in Artificial Intelligence and
Neuroscience
Volume 10, Special Issue 2 (October, 2019), ISSN 2067-3957
4
The teacher will analyze the results of the analyzes and study
the degree of achievement of
the initial goals.
Depending on the conclusions, it may take the decision to
improve the course and resume its
evaluation process. This may prove to be a difficult process
because opinions can differ
significantly from one teacher to another in relation to the
material and the way of interaction with
the student the course offers.
3. Methods of data exploitation
There are currently a wide variety of methods of exploiting data
in the education system.
These can be categorized into two broad categories according to
the ways to achieve the objectives:
ification, Regression, Outlier
Detecting
Discovery of data for human
judgment (Sasu, 2014).
17. Many of these are general DM methods: prediction,
classification, grouping, exploitation of
texts and others. But there are also specific EDM methods such
as nonnegative matrix factorization
and Knowledge tracing (KT) (Romero & Ventura, 2012). Here
are some of these:
Prediction
The method can be used in education to predict students'
behavior and outcomes. It is based
on the creation of predictive models. In the training phase, they
learn to make predictions about a
set of variables called predictors by analyzing them in
combination with other variables. Once the
enrollment phase is completed, the patterns can be applied to
the data sets for which the prediction
is to be applied. It is known the study by Baker, Gowda, Corbett
- Automatically detecting the
student's preparation for future learning: help use is key (Baker,
Gowda & Corbett, 2011). The
authors create a tool for automatically predicting a student's
future performance on the basis of
establishing positive or negative correlations between various
features such as: student test results,
time spent in response, time elapsed between receiving a clue
and typing the answer, and others. It
is experienced on a group of students, and then applied to
another group. The results are then
compared to those obtained using the Bayesian Knowledge
Tracing (BKT) model.
Classification
18. The method involves building a predictive model. The data in
the training set is
characterized by certain attributes. The model must identify
belonging to a class based on the set of
attributes. Suppose we built an educational software as an
interactive game for a given theme.
Based on user attributes such as age, gender, geographic area,
duration until the game is completed,
number of attempts we can build a classifier, and determine the
user's belonging to a specific class.
The model will learn to identify students. The analyzes can
provide information on the need to use
this educational method for certain age groups, interests and
education.
Methods that use the classification are: decision trees, neural
networks, bayesian
classifications, and others.
Clustering
The method involves building patterns that identify data
clustering after certain similarities.
For the model to provide quality predictions, the similarities
inside class must be maximized and
similarities between classes minimized.
The use of this method in Romanian high school education
could aim at grouping pupils
according to the pupil's learning style (auditory, visual,
practical - kinesthesis) based on the analysis
of behavior in relation to certain educational products and
pupils' characteristics. The prediction of
such a model could lead to an effective recommendation of how
19. to learn educational content. Thus,
the instructional process could be carried out efficiently in
relation to the learning particularities of
each student. At present, there is an attempt to unfold the
lessons in a way appropriate to the
D. Marcu, M. Danubianu - Learning Analytics or Educational
Data Mining? This is the Question...
5
students' learning styles, but the reality is that identifying
learning styles is superficial. The results
of the questionnaires are attached to the class catalog, but this
does not lead, in most cases, to the
improve teaching methods and techniques used in the lesson. In
the absence of clear alternatives,
the teacher has to improvise.
The method is successfully used in the detection of plagiarism
(Text Mining) and is also
applied in the educational sphere.
Outlier Detection
The method involves creating patterns that detect data that have
different features than
others. In Romanian education, this method could be used to
detect students with content
assimilation problems, or those with aberrant behavior.
In general, not only one EDM method is used in case studies.
Outlier Detection methods can
20. be used, for example, with data clustering techniques and
decision tree classification as presented in
the study by Ajith, Sai and Tejaswi (2013) - Evaluation of
student performance: an outlier detection
perspective (Ajith, Sai & Tejaswi, 2013). The study aims to
identify learners with special learning
needs to reduce the school failure rate. Input data are collected
from: participation in student
lessons, tests, notes on initial tests. In order to achieve the
proposed objective, they try to find
models for classifying students who will be helpful in setting up
study groups.
At present, in Romania, students in the high school education of
state do not have the
opportunity to trace the course matter in other groups than the
classes they belong to. Moreover,
pupils diagnosed as having special educational needs participate
in classes with other colleagues.
The teachers create for them specially programs. Then the
courses are held by under the guidance of
a single teacher who does not have any pedagogical and
methodical experience related to the
learning situation! There are special requirements for
conducting the educational process. This
based on grouping students within the same educational space
within the same timeframe to go
through different course materials. In the absence of a proper
classification, alternative methods and
means, and teachers with such experience, things happen more
or less in a manner that leads to the
best results.
Discovery with Models
Discovery with Models is the fifth category presented in Baker's
21. Taxonomy (Baker, 2012).
It is also one of the most widely used methods of data
exploitation in the field of education. It is
based on the use of a previously validated model as a
component in analyzes that use prediction or
exploitation of relationships in new contexts (Baker & Yacef,
2009). In this way information on
educational materials that contribute most to educational
progress can be obtained. A study carried
out by Beck and Mostow in 2008 - How who should practice:
Using learning decomposition to
evaluate the efficacy of different types of practice for different
types of students (Beck & Mostow,
2008) - on the analysis of different types of learners
demonstrates that the method supports
identifying relationships between student behavior and
characteristics of variables used.
Nonnegative Matrix Factorization (or Decomposition)
There are several algorithms used for factoring the nonnegative
matrix. This transforms
(decomposes, factorizes) a matrix V into two W and H matrices
with the property that they all have
non-negative elements. This is very useful in applications such
as determining the effectiveness of
an evaluation system in which matrices contain elements related
to: exams, abilities, and items.
Matrix V is obtained from the product of the two smaller
matrices as can be seen in Figure 3.
("Non-negative matrix factorization", 2019).
22. Figure 3. Illustration of approximate non-negative matrix
factorization. Source: wikipedia.org
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We propose to study the evaluation of two specific abilities
defined on the columns of the
matrix W for 4 work requirements (items), defined in the W
matrix on the four lines.
Matrix H will contain two lines representing the two abilities
and 6 columns representing the
assessed students.
The result will be recorded in Matrix V that has 4 lines for each
of the 4 items and 6
columns for each of the 6 students.
A value of 1 in the W matrix indicates the need for a certain
skill (Figure 4) (Desmarais,
2012).
W
I
te
m
s
23. skills
0 1
1 0
1 0
1 1
X
H
sk
il
ls
students
1 1 1 0 1 1
0 0 1 1 0 0
≈
V
it
em
s
students
0 0 1 1 0 0
24. 1 1 1 0 1 1
1 1 1 0 1 1
1 1 2 1 1 1
Figure 4. Non-negative matrix factorization - example
The first item requires the ability 2, W [1] [2] = 1. Only the 2
and 3 students have the ability
2, so item 1 will not be promoted by students 1, 2, 4 and 5.
To promote Item 4 both skills are required. Only one of the
candidates will promote this
item with the maximum score.
Using computerized analysis methods, interpretations can be
obtained in a much shorter
time and with great accuracy because machines are faster and
more accurate than humans.
4. Learning Analysis (LA)
Learning is the product of an interaction between learners and
the learning environment,
between among students / educators / teachers and others (Elias
& Lias, 2011).
The evaluation of learning, in the traditional sense, is based on
the evaluation of student /
pupil outcomes. This involves assessing knowledge but also
trying to answer questions such as:
how well this student needs, how can be improved, how to
change the course interface to make it
more accessible. At present, especially in the pre-university
25. system, learning evaluation is based on
questionnaires. Obtaining feed-back is lasting because the non-
automatic data processing takes time
and the analysis possibilities are quite limited.
The desire to improve the quality of learning and assessment in
the educational system is
increasing at the international level, but also in our country.
Traditional systems are confronted by
huge amounts of data and their diversity. Learning Analytics
(LA) attempts to answer questions
about how this data can be used and how it can be transformed
and analyzed to provide useful
information that can give value to the learning process (Liu &
Fan, 2014).
In 2011, at the first International Conference on Learning
Analysis (LAK 2011), the
definition of the new research area, LA, was adopted as:
"learning analysis is the measurement,
collection, analysis and reporting of pupils and students and
about the context of learning, in order
to understand and optimize learning and its environments "
(Siemens, 2011).
Data analytics was first used in sales, also called Business
Intelligence. This branch of research
uses computer techniques to synthesize huge amounts of data
and turn them into powerful tools for
making the best marketing decisions.
With the development of Web technologies, a branch of data
analysis research, Web Analytics,
has been developed. Web Analytics tools collect data about
users of a site and report on their behavior.
This leads to a better understanding of customers and making
26. the best decisions to improve your
browsing experience and to keep visitors to the site.
D. Marcu, M. Danubianu - Learning Analytics or Educational
Data Mining? This is the Question...
7
Learning Analytics borrows tools and methods used in Business
Intelligence and Web Analytics
to analyze educational data.
At present, many universities, companies, and organizations are
developing learning platforms
for both students and lifelong learning. An enormous advantage
of these is to personalize the learning
experience and adapt it to the physical deficiencies of the
learners.
In a research conducted by the New Media Consortium and the
EDUCAUSE Learning Initiative
in 2016, areas that will have a particular impact on university
education globally by 2020 are identified.
One of these is Learning Analytics. In the research report LA is
defined as an application in the
educational field of Web Analytics. It focuses on the collection
and detailed analysis of student
interactions with online learning platforms (Johnson, Adams
Becker & Cummins, 2016).
A free example of a Web Analytics tool is provided by Google
and is called Google Analytics. It
provides sophisticated user behavior on a website and provides
its administrators with reports about:
27. many of them
are new customers;
With these reports, can create additional features, add more
interesting content, enhance
interactivity, customize the interface of the application based on
the devices used for viewing.
In the following figures (5,6,7) there are illustrated sections of
various reports provided by
this tool for the site https://www.modinfo.ro - a site dedicated
to the preparation of the students
from the Romanian high schools at the course of computer
science.
Figure 5 provides a diagram representation of the number of
visitors per page of the site. We
note that students are looking for baccalaureate content
(bac.php), admission to faculty
(admission.php) and additional training for performance
(cex.php).
Figure 5. User preferred content
Figure 6 represents the percentage of visitors to the site over a
fixed period, by age category.
28. It can be seen that most users are aged between 25 and 34 years.
For administrators, given the
period under review, this reveals their student’s preoccupation
for to prepare for the Computer
Programming Exam.
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Figure 6. Demographics and interest categories - Age of users
Figure 7 provides information on analyzing the active presence
of a specific user on a site
within a selected time interval.
Figure 7. Behavior of a user on the site within a selected time
range
Choosing how to use and constructing analytics tools starts
from the choice of quantifiable
indicators that have to be defined according to the proposed
objectives. Examples of such indicators
for the educational environment:
29. tool within the course and
others.
4.1. Learning Analytics methods
Methods used for learning analysis include:
quality of the expression is analyzed.
ty in relation to learning: Students
interested in the topic will ask questions,
access links to supplementary resources
motivational learning.
LA uses some methods of data mining as EDM. They can be
classified in: Prediction,
Clustering, Relationship mining, Discovery with models,
Distillation of Data for Human Judgment
(Nunn, Avella, Kanai, & Kebritchi, 2016).
We will briefly describe the methods that have not already been
presented in the previous
section.
D. Marcu, M. Danubianu - Learning Analytics or Educational
Data Mining? This is the Question...
9
30. Relationship mining
It's a method that uses algorithms to find association rules to
detect, for example, mistakes
made by students when solving a set of exercises. Based on the
associations made, one can predict a
certain behavior of the student depending on the hypothesis of
solving the problem from which he
starts. Thus, the teacher or course manager can intervene in
order for the pupil / student not to
mistaken. There can be found, for example, relationships
between other activities of the student
(playing on the computer, talking to a chat room colleague)
while solving his or her work tasks and
erroneous answers (Baker, Corbett, Koedinger & Wagner,
2004).
Distillation of Data for Human Judgment
This method includes statistics and visualization techniques that
help people understand data
analytics. The method is the basis for the creation of many
useful tools that provide clear analysis
that can be quickly understood by unrelated users.
An example is the formation of a map to group learners by the
amount of heat emanating
from their bodies during learning the instructional material.
This can be done with sensors mounted
on the body. The analysis provides real-time learning about
learning performance indicators
(Merceron, 2015).
31. 5. Learning Analytics or Educational Data Mining?
Educational Data Mining is a new field of research. It is based
on the models, methods and
algorithms built for DM. However, there are also specific
methods of applying DM in education.
The main purpose of EDM is to explore large sets of data from
the educational system to create
knowledge-extraction models from the data. The main objective
is to provide useful information to
education decision makers about existing correlations between
sets of data that provide a deeper
understanding of the educational needs of students and the
system as a whole (de Almeida Neto &
Castro, 2017).
Learning Analytics is a newer field of research. It is based on
data analysis techniques in
Business Intelligence. LA uses highly sophisticated analysis
tools and predictive models to improve
learning. Most applications using LA have been created for the
university system and are dedicated
to early detection of concrete problems such as the risk of
abandoning a course by certain students.
LA also uses the expertise of other research areas, such as EDM
and Web Analytics, with the same
objectives of predicting learning outcomes and providing useful
information for improving the
quality of the learning process (Elias & Lias, 2011).
EDM is at the intersection of areas such as artificial
intelligence, machine learning,
education, and statistics.
Figure 8 shows the LA as an interdisciplinary subdomain of
32. Business Intelligence, Statistics
and Education.
Figure 8. Educational Data Mining and Learning Analytics
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The two new areas of research are quite similar in terms of the
aims pursued an methods
used, but there are also some significant differences between
them. Some of the most important
resemblances and differences between EDM and LA are shown
in Tables 1 and 2.
Table 1. Similarities between EDM and LA
EDM LA
Both areas contribute to improving the quality of education and
education policies in schools and universities, but in
alternative education systems as well.
It is a new field of research. In 2011, in Massachusetts USA, the
International Working Group on EDM (established in 2007)
created the International Society for EDM.
The definition of this new field of research was
adopted in 2011 at the first International Conference
33. on Learning Analytics (LAK 2011).
It is based on the exploitation of large data collections. It is
based on analysis of large data collections.
It is based on the formulation of specific research …
Uncertainty in big data analytics: survey,
opportunities, and challenges
Reihaneh H. Hariri* , Erik M. Fredericks and Kate M. Bowers
Introduction
According to the National Security Agency, the Internet
processes 1826 petabytes (PB)
of data per day [1]. In 2018, the amount of data produced every
day was 2.5 quintil-
lion bytes [2]. Previously, the International Data Corporation
(IDC) estimated that the
amount of generated data will double every 2 years [3], however
90% of all data in the
world was generated over the last 2 years, and moreover Google
now processes more
than 40,000 searches every second or 3.5 billion searches per
day [2]. Facebook users
upload 300 million photos, 510,000 comments, and 293,000
status updates per day [2, 4].
Needless to say, the amount of data generated on a daily basis is
staggering. As a result,
techniques are required to analyze and understand this massive
amount of data, as it is a
great source from which to derive useful information.
Abstract
Big data analytics has gained wide attention from both academia
and industry as the
34. demand for understanding trends in massive datasets increases.
Recent developments
in sensor networks, cyber-physical systems, and the ubiquity of
the Internet of Things
(IoT) have increased the collection of data (including health
care, social media, smart
cities, agriculture, finance, education, and more) to an
enormous scale. However, the
data collected from sensors, social media, financial records, etc.
is inherently uncer-
tain due to noise, incompleteness, and inconsistency. The
analysis of such massive
amounts of data requires advanced analytical techniques for
efficiently reviewing and/
or predicting future courses of action with high precision and
advanced decision-
making strategies. As the amount, variety, and speed of data
increases, so too does the
uncertainty inherent within, leading to a lack of confidence in
the resulting analytics
process and decisions made thereof. In comparison to traditional
data techniques and
platforms, artificial intelligence techniques (including machine
learning, natural lan-
guage processing, and computational intelligence) provide more
accurate, faster, and
scalable results in big data analytics. Previous research and
surveys conducted on big
data analytics tend to focus on one or two techniques or specific
application domains.
However, little work has been done in the field of uncertainty
when applied to big data
analytics as well as in the artificial intelligence techniques
applied to the datasets. This
article reviews previous work in big data analytics and presents
a discussion of open
36. Page 2 of 16Hariri et al. J Big Data (2019) 6:44
Advanced data analysis techniques can be used to transform big
data into smart data
for the purposes of obtaining critical information regarding
large datasets [5, 6]. As such,
smart data provides actionable information and improves
decision-making capabilities
for organizations and companies. For example, in the field of
health care, analytics per-
formed upon big datasets (provided by applications such as
Electronic Health Records
and Clinical Decision Systems) may enable health care
practitioners to deliver effective
and affordable solutions for patients by examining trends in the
overall history of the
patient, in comparison to relying on evidence provided with
strictly localized or current
data. Big data analysis is difficult to perform using traditional
data analytics [7] as they
can lose effectiveness due to the five V’s characteristics of big
data: high volume, low
veracity, high velocity, high variety, and high value [7–9].
Moreover, many other charac-
teristics exist for big data, such as variability, viscosity,
validity, and viability [10]. Several
artificial intelligence (AI) techniques, such as machine learning
(ML), natural language
processing (NLP), computational intelligence (CI), and data
mining were designed to
provide big data analytic solutions as they can be faster, more
accurate, and more pre-
cise for massive volumes of data [8]. The aim of these advanced
analytic techniques is
to discover information, hidden patterns, and unknown
correlations in massive datasets
37. [7]. For instance, a detailed analysis of historical patient data
could lead to the detection
of destructive disease at an early stage, thereby enabling either
a cure or more optimal
treatment plan [11, 12]. Additionally, risky business decisions
(e.g., entering a new mar-
ket or launching a new product) can profit from simulations that
have better decision-
making skills [13].
While big data analytics using AI holds a lot of promise, a wide
range of challenges
are introduced when such techniques are subjected to
uncertainty. For instance, each of
the V characteristics introduce numerous sources of uncertainty,
such as unstructured,
incomplete, or noisy data. Furthermore, uncertainty can be
embedded in the entire ana-
lytics process (e.g., collecting, organizing, and analyzing big
data). For example, dealing
with incomplete and imprecise information is a critical
challenge for most data mining
and ML techniques. In addition, an ML algorithm may not
obtain the optimal result if
the training data is biased in any way [14, 15]. Wang et al. [16]
introduced six main chal-
lenges in big data analytics, including uncertainty. They focus
mainly on how uncertainty
impacts the performance of learning from big data, whereas a
separate concern lies in
mitigating uncertainty inherent within a massive dataset. These
challenges normally pre-
sent in data mining and ML techniques. Scaling these concerns
up to the big data level
will effectively compound any errors or shortcomings of the
entire analytics process.
38. Therefore, mitigating uncertainty in big data analytics must be
at the forefront of any
automated technique, as uncertainty can have a significant
influence on the accuracy of
its results.
Based on our examination of existing research, little work has
been done in terms of
how uncertainty significantly impacts the confluence of big data
and the analytics tech-
niques in use. To address this shortcoming, this article presents
an overview of the
existing AI techniques for big data analytics, including ML,
NLP, and CI from the per-
spective of uncertainty challenges, as well as suitable directions
for future research in
these domains. The contributions of this work are as follows.
First, we consider uncer-
tainty challenges in each of the 5 V’s big data characteristics.
Second, we review several
Page 3 of 16Hariri et al. J Big Data (2019) 6:44
techniques on big data analytics with impact of uncertainty for
each technique, and also
review the impact of uncertainty on several big data analytic
techniques. Third, we dis-
cuss available strategies to handle each challenge presented by
uncertainty.
To the best of our knowledge, this is the first article surveying
uncertainty in big data
analytics. The remainder of the paper is organized as follows.
“Background” section pre-
39. sents background information on big data, uncertainty, and big
data analytics. “Uncer-
tainty perspective of big data analytics” section considers
challenges and opportunities
regarding uncertainty in different AI techniques for big data
analytics. “Summary of mit-
igation strategies” section correlates the surveyed works with
their respective uncertain-
ties. Lastly, “Discussion” section summarizes this paper and
presents future directions of
research.
Background
This section reviews background information on the main
characteristics of big data,
uncertainty, and the analytics processes that address the
uncertainty inherent in big data.
Big data
In May 2011, big data was announced as the next frontier for
productivity, innovation,
and competition [11]. In 2018, the number of Internet users
grew 7.5% from 2016 to over
3.7 billion people [2]. In 2010, over 1 zettabyte (ZB) of data
was generated worldwide
and rose to 7 ZB by 2014 [17]. In 2001, the emerging
characteristics of big data were
defined with three V’s (Volume, Velocity, and Variety) [18].
Similarly, IDC defined big
data using four V’s (Volume, Variety, Velocity, and Value) in
2011 [19]. In 2012, Veracity
was introduced as a fifth characteristic of big data [20–22].
While many other V’s exist
[10], we focus on the five most common characteristics of big
data, as next illustrated in
40. Fig. 1.
Volume refers to the massive amount of data generated every
second and applies to the
size and scale of a dataset. It is impractical to define a universal
threshold for big data
volume (i.e., what constitutes a ‘big dataset’) because the time
and type of data can influ-
ence its definition [23]. Currently, datasets that reside in the
exabyte (EB) or ZB ranges
are generally considered as big data [8, 24], however challenges
still exist for datasets in
smaller size ranges. For example, Walmart collects 2.5 PB from
over a million custom-
ers every hour [25]. Such huge volumes of data can introduce
scalability and uncertainty
problems (e.g., a database tool may not be able to accommodate
infinitely large datasets).
Many existing data analysis techniques are not designed for
large-scale databases and
can fall short when trying to scan and understand the data at
scale [8, 15].
Variety refers to the different forms of data in a dataset
including structured data,
semi-structured data, and unstructured data. Structured data
(e.g., stored in a rela-
tional database) is mostly well-organized and easily sorted, but
unstructured data
(e.g., text and multimedia content) is random and difficult to
analyze. Semi-structured
data (e.g., NoSQL databases) contains tags to separate data
elements [23, 26], but
enforcing this structure is left to the database user. Uncertainty
can manifest when
converting between different data types (e.g., from unstructured
41. to structured data),
in representing data of mixed data types, and in changes to the
underlying struc-
ture of the dataset at run time. From the point of view of
variety, traditional big data
Page 4 of 16Hariri et al. J Big Data (2019) 6:44
analytics algorithms face challenges for handling multi-modal,
incomplete and noisy
data. Because such techniques (e.g., data mining algorithms) are
designed to consider
well-formatted input data, they may not be able to deal with
incomplete and/or dif-
ferent formats of input data [7]. This paper focuses on
uncertainty with regard to big
data analytics, however uncertainty can impact the dataset itself
as well.
Efficiently analysing unstructured and semi-structured data can
be challenging,
as the data under observation comes from heterogeneous sources
with a variety of
data types and representations. For example, real-world
databases are negatively
influenced by inconsistent, incomplete, and noisy data.
Therefore, a number of data
preprocessing techniques, including data cleaning, data
integrating, and data trans-
forming used to remove noise from data [27]. Data cleaning
techniques address data
quality and uncertainty problems resulting from variety in big
data (e.g., noise and
inconsistent data). Such techniques for removing noisy objects
42. during the analysis
process can significantly enhance the performance of data
analysis. For example, data
cleaning for error detection and correction is facilitated by
identifying and eliminat-
ing mislabeled training samples, ideally resulting in an
improvement in classification
accuracy in ML [28].
Velocity comprises the speed (represented in terms of batch,
near-real time, real time,
and streaming) of data processing, emphasizing that the speed
with which the data is
processed must meet the speed with which the data is produced
[8]. For example, Inter-
net of Things (IoT) devices continuously produce large amounts
of sensor data. If the
device monitors medical information, any delays in processing
the data and sending the
results to clinicians may result in patient injury or death (e.g., a
pacemaker that reports
emergencies to a doctor or facility) [20]. Similarly, devices in
the cyber-physical domain
often rely on real-time operating systems enforcing strict timing
standards on execution,
Fig. 1 Common big data characteristics
Page 5 of 16Hariri et al. J Big Data (2019) 6:44
and as such, may encounter problems when data provided from a
big data application
fails to be delivered on time.
43. Veracity represents the quality of the data (e.g., uncertain or
imprecise data). For
example, IBM estimates that poor data quality costs the US
economy $3.1 trillion per
year [21]. Because data can be inconsistent, noisy, ambiguous,
or incomplete, data verac-
ity is categorized as good, bad, and undefined. Due to the
increasingly diverse sources
and variety of data, accuracy and trust become more difficult to
establish in big data
analytics. For example, an employee may use Twitter to share
official corporate informa-
tion but at other times use the same account to express personal
opinions, causing prob-
lems with any techniques designed to work on the Twitter
dataset. As another example,
when analyzing millions of health care records to determine or
detect disease trends,
for instance to mitigate an outbreak that could impact many
people, any ambiguities or
inconsistencies in the dataset can interfere or decrease the
precision of the analytics pro-
cess [21].
Value represents the context and usefulness of data for decision
making, whereas the
prior V’s focus more on representing challenges in big data. For
example, Facebook,
Google, and Amazon have leveraged the value of big data via
analytics in their respective
products. Amazon analyzes large datasets of users and their
purchases to provide prod-
uct recommendations, thereby increasing sales and user
participation. Google collects
location data from Android users to improve location services in
Google Maps. Face-
44. book monitors users’ activities to provide targeted advertising
and friend recommenda-
tions. These three companies have each become massive by
examining large sets of raw
data and drawing and retrieving useful insight to make better
business decisions [29].
Uncertainty
Generally, “uncertainty is a situation which involves unknown
or imperfect information”
[30]. Uncertainty exists in every phase of big data learning [7]
and comes from many dif-
ferent sources, such as data collection (e.g., variance in
environmental conditions and
issues related to sampling), concept variance (e.g., the aims of
analytics do not present
similarly) and multimodality (e.g., the complexity and noise
introduced with patient
health records from multiple sensors include numerical, textual,
and image data). For
instance, most of the attribute values relating to the timing of
big data (e.g., when events
occur/have occurred) are missing due to noise and
incompleteness. Furthermore, the
number of missing links between data points in social networks
is approximately 80% to
90% and the number of missing attribute values within patient
reports transcribed from
doctor diagnoses are more than 90% [31]. Based on IBM
research in 2014, industry ana-
lysts believe that, by 2015, 80% of the world’s data will be
uncertain [32].
Various forms of uncertainty exist in big data and big data
analytics that may nega-
45. tively impact the effectiveness and accuracy of the results. For
example, if training
data is biased in any way, incomplete, or obtained through
inaccurate sampling, the
learning algorithm using corrupted training data will likely
output inaccurate results.
Therefore, it is critical to augment big data analytic techniques
to handle uncertainty.
Recently, meta-analysis studies that integrate uncertainty and
learning from data
have seen a sharp increase [33–35]. The handling of the
uncertainty embedded in the
entire process of data analytics has a significant effect on the
performance of learning
Page 6 of 16Hariri et al. J Big Data (2019) 6:44
from big data [16]. Other research also indicates that two more
features for big data,
such as multimodality (very complex types of data) and
changed-uncertainty (the
modeling and measure of uncertainty for big data) is remarkably
different from that of
small-size data. There is also a positive correlation in
increasing the size of a dataset
to the uncertainty of data itself and data processing [34]. For
example, fuzzy sets may
be applied to model uncertainty in big data to combat vague or
incorrect information
[36]. Moreover, and because the data may contain hidden
relationships, the uncer-
tainty is further increased.
Therefore, it is not an easy task to evaluate uncertainty in big
46. data, especially when
the data may have been collected in a manner that creates bias.
To combat the many
types of uncertainty that exist, many theories and techniques
have been developed to
model its various forms. We next describe several common
techniques.
Bayesian theory assumes a subjective interpretation of the
probability based on past
event/prior knowledge. In this interpretation the probability is
defined as an expres-
sion of a rational agent’s degrees of belief about uncertain
propositions [37]. Belief
function theory is a framework for aggregating imperfect data
through an informa-
tion fusion process when under uncertainty [38]. Probability
theory incorporates
randomness and generally deals with the statistical
characteristics of the input data
[34]. Classification entropy measures ambiguity between classes
to provide an index
of confidence when classifying. Entropy varies on a scale from
zero to one, where val-
ues closer to zero indicate more complete classification in a
single class, while values
closer to one indicate membership among several different
classes [39]. Fuzziness is
used to measure uncertainty in classes, notably in human
language (e.g., good and
bad) [16, 33, 40]. Fuzzy logic then handles the uncertainty
associated with human
perception by creating an approximate reasoning mechanism
[41, 42]. The method-
ology was intended to imitate human reasoning to better handle
uncertainty in the
47. real world [43]. Shannon’s entropy quantifies the amount of
information in a variable
to determine the amount of missing information on average in a
random source [44,
45]. The concept of entropy in statistics was introduced into the
theory of communi-
cation and transmission of information by Shannon [46].
Shannon entropy provides
a method of information quantification when it is not possible
to measure crite-
ria weights using a decision–maker. Rough set theory provides a
mathematical tool
for reasoning on vague, uncertain or incomplete information.
With the rough set
approach, concepts are described by two approximations (upper
and lower) instead of
one precise concept [47], making such methods invaluable to
dealing with uncertain
information systems [48]. Probabilistic theory and Shannon’s
entropy are often used
to model imprecise, incomplete, and inaccurate data. Moreover,
fuzzy set and rough
theory are used for modeling vague or ambiguous data [49], as
shown in Fig. 2.
Evaluating the level of uncertainty is a critical step in big data
analytics. Although
a variety of techniques exist to analyze big data, the accuracy of
the analysis may be
negatively affected if uncertainty in the data or the technique
itself is ignored. Uncer-
tainty models such as probability theory, fuzziness, rough set
theory, etc. can be used
to augment big data analytic techniques to provide more
accurate and more mean-
ingful results. Based on the previous research, Bayesian model
48. and fuzzy set theory
are common for modeling uncertainty and decision-making.
Table 1 compares and
Page 7 of 16Hariri et al. J Big Data (2019) 6:44
summarizes the techniques we have identified as relevant,
including a comparison
between different uncertainty strategies, focusing on
probabilistic theory, Shannon’s
entropy, fuzzy set theory, and rough set theory.
Big data analytics
Big data analytics describe the process of analyzing massive
datasets to discover pat-
terns, unknown correlations, market trends, user preferences,
and other valuable
information that previously could not be analyzed with
traditional tools [52]. With
the formalization of the big data’s five V characteristics,
analysis techniques needed
to be reevaluated to overcome their limitations on processing in
terms of time and
space [29]. Opportunities for utilizing big data are growing in
the modern world of
digital data. The global annual growth rate of big data
technologies and services is
Measuring uncertainty in
big data
Imprecise, inaccurate, and
incomplete data
49. Probability
Theory
Shannon's
Entropy
Vague or ambiguous data
Fuzzy Set
Theory
Rough Set
Theory
Fig. 2 Measuring uncertainty in big data
Table 1 Comparison of uncertainty strategies
Uncertainty models Features
Probability theory
Bayesian theory
Shannon’s entropy
Powerful for handling randomness and subjective uncertainty
where precision is required
Capable of handling complex data [50]
Fuzziness Handles vague and imprecise information in systems
that are difficult to model
Precision not guaranteed
Easy to implement and interpret [50]
Belief function Handle situations with some degree of ignorance
Combines distinct evidence from several sources to compute the
50. probability of specific
hypotheses
Considers all evidence available for the hypothesis
Ideal for incomplete and high complex data
Mathematically complex but improves uncertainty reduction
[50]
Rough set theory Provides an objective form of analysis [47]
Deals with vagueness in data
Minimal information necessary to determine set membership
Only uses the information presented within the given data [51]
Classification entropy Handles ambiguity between the classes
[39]
Page 8 of 16Hariri et al. J Big Data (2019) 6:44
predicted to increase about 36% between 2014 and 2019, with
the global income for
big data and business analytics anticipated to increase more
than 60% [53].
Several advanced data analysis techniques (i.e., ML, data
mining, NLP, and CI) and
potential strategies such as parallelization, divide-and-conquer,
incremental learn-
ing, sampling, granular computing, feature selection [16], and
instance selection [34]
can convert big problems to small problems and can be used to
make better deci-
sions, reduce costs, and enable more efficient processing.
With respect to big data analytics, parallelization reduces
51. computation time by
splitting large problems into smaller instances of itself and
performing the smaller
tasks simultaneously (e.g., distributing the smaller tasks across
multiple threads,
cores, or processors). Parallelization does not decrease the
amount of work per-
formed but rather reduces computation time as the small tasks
are completed at the
same point in time instead of one after another sequentially
[16].
The divide-and-conquer strategy plays an important role in
processing big data.
Divide-and-conquer consists of three phases: (1) reduce one
large problem into sev-
eral smaller problems, (2) complete the smaller problems, where
the solving of each
small problem contributes to the solving of the large problem,
and (3) incorporate
the solutions of the smaller problems into one large solution
such that the large
problem is considered solved. For many years the divide-and-
conquer strategy has
been used in very massive databases to manipulate records in
groups rather than all
the data at once [54].
Incremental learning is a learning algorithm popularly used with
streaming data
that is trained only with new data rather than only training with
existing data. Incre-
mental learning adjusts the parameters in the learning algorithm
over time accord-
ing to each new input data and each input is used for training
only once [16].
52. Sampling can be used as a data reduction method for big data
analytics for deriv-
ing patterns in large data sets by choosing, manipulating, and
analyzing a subset of
the data [16, 55]. Some research indicates that obtaining
effective results using sam-
pling depends on the data sampling criteria used [56].
Granular computing groups elements from a large space to
simplify the elements
into subsets, or granules [57, 58]. Granular computing is an
effective approach to
define uncertainty of objects in the search space as it reduces
large objects to a
smaller search space [59].
Feature selection is a conventional approach to handle big data
with the purpose of
choosing a subset of relative features for an aggregate but more
precise data repre-
sentation [60, 61]. Feature selection is a very useful strategy in
data mining for pre-
paring high-scale data [60].
Instance selection is practical in many ML or data mining tasks
as a major feature
in data pre-processing. By utilizing instance selection, it is
possible to reduce train-
ing sets and runtime in the classification or training phases
[62].
The costs of uncertainty (both monetarily and computationally)
and challenges
in generating effective models for uncertainties in big data
analytics have become
53. key to obtaining robust and performant systems. As such, we
examine several open
issues of the impacts of uncertainty on big data analytics in the
next section.
Page 9 of 16Hariri et al. J Big Data (2019) 6:44
Uncertainty perspective of big data analytics
This section examines the impact of uncertainty on three AI
techniques for big data ana-
lytics. Specifically, we focus on ML, NLP, and CI, although
many other analytics tech-
niques exist. For each presented technique, we examine the
inherent uncertainties and
discuss methods and strategies for their mitigation.
Machine learning and big data
When dealing with data analytics, ML is generally used to
create models for predic-
tion and knowledge discovery to enable data-driven decision-
making. Traditional ML
methods are not computationally efficient or scalable enough to
handle both the char-
acteristics of big data (e.g., large volumes, high speeds, varying
types, low value density,
incompleteness) and uncertainty (e.g., biased training data,
unexpected data types, etc.).
Several commonly used advanced ML techniques proposed for
big data analysis include
feature learning, deep learning, transfer learning, distributed
learning, and active learn-
ing. Feature learning includes a set of techniques that enables a
system to automatically
54. discover the representations needed for feature detection or
classification from raw data.
The performances of the ML algorithms are strongly influenced
by the selection of data
representation. Deep learning algorithms are designed for
analyzing and extracting valu-
able knowledge from massive amounts of data and data
collected from various sources
(e.g., separate variations within an image, such as a light,
various materials, and shapes)
[56], however current deep learning models incur a high
computational cost. Distrib-
uted learning can be used to mitigate the scalability problem of
traditional ML by carry-
ing out calculations on data sets distributed among several
workstations to scale up the
learning process [63]. Transfer learning is the ability to apply
knowledge learned in one
context to new contexts, effectively improving a learner from
one domain by transfer-
ring information from a related domain [64]. Active learning
refers to algorithms that
employ adaptive data collection [65] (i.e., processes that
automatically adjust param-
eters to collect the most useful data as quickly as possible) in
order to accelerate ML
activities and overcome labeling problems. The uncertainty
challenges of ML techniques
can be mainly attributed to learning from data with low veracity
(i.e., uncertain and
incomplete data) and data with low value (i.e., unrelated to the
current problem). We
found that, among the ML techniques, active learning, deep
learning, and fuzzy logic
theory are uniquely suited to support the challenge of reducing
uncertainty, as shown
55. in Fig. 3. Uncertainty can impact ML in terms of incomplete or
imprecise training sam-
ples, unclear classification boundaries, and rough knowledge of
the target data. In some
cases, the data is represented without labels, which can become
a challenge. Manually
labeling large data collections can be an expensive and
strenuous task, yet learning from
unlabeled data is very …
Research Paper – Data Science & Big Data Analytics
While this week’s topic highlighted the uncertainty of Big Data,
the author identified the following as areas for future research.
Pick one of the following for your Research paper.
· Additional study must be performed on the interactions
between each big data characteristic, as they do not exist
separately but naturally interact in the real world.
· The scalability and efficacy of existing analytics techniques
being applied to big data must be empirically examined.
· New techniques and algorithms must be developed in ML and
NLP to handle the real-time needs for decisions made based on
enormous amounts of data.
· More work is necessary on how to efficiently model
uncertainty in ML and NLP, as well as how to represent
uncertainty resulting from big data analytics.
· Since the CI algorithms are able to find an approximate
solution within a reasonable time, they have been used to tackle
ML problems and uncertainty challenges in data analytics and
process in recent years.
Your paper should meet the following requirements:
• Be approximately 3-5 pages in length, not including the
required cover page and reference page.
• Follow APA guidelines. Your paper should include an
introduction, a body with fully developed content, and a
conclusion.
56. • Support your response with the readings from the course and
at least five peer-reviewed articles or scholarly journals to
support your positions, claims, and observations. The UC
Library is a great place to find resources.
• Be clear with well-written, concise, using excellent grammar
and style techniques. You are being graded in part on the
quality of your writing.
References:
Marcu, D., & Danubianu, M. (2019). Learning Analytics or
Educational Data Mining? This is the Question. BRAIN: Broad
Research in Artificial Intelligence & Neuroscience, 10, 1–14.
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Hariri, R.H., Fredericks, E.M. & Bowers, K.M. J Big Data
(2019) 6: 44. https://doi.org/10.1186/s40537-019-0206-3