The document discusses an agenda for a meeting on managing district and school information. It outlines various tools and systems used in Sayreville Public Schools such as EdAnalyzer, Learnia, PowerSchool, and NJ SMART for collecting and analyzing student performance data. Participants were assigned a blog entry reflecting on what defines a data-rich district and how to effectively manage student data.
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
Advances in Learning Analytics and Educational Data Mining MehrnooshV
This presentation is about the state-of-the-art of Learning Analytics and Edicational Data Mining. It is presented by Mehrnoosh Vahdat as the introductory tutorial of Special Session 'Advances in Learning Analytics and Educational Data Mining' at ESANN 2015 conference.
This paper highlights important issues of higher education system such as predicting student’s academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an in-depth awareness about how these algorithms predict student the performance of different student and enhance their skill.
Student Performance Evaluation in Education Sector Using Prediction and Clust...IJSRD
Data mining is the crucial steps to find out previously unknown information from large relational database. various technique and algorithm are their used in data mining such as association rules, clustering and classification and prediction techniques. Ease of the techniques contains particular characteristics and behaviour. In this paper the prime focus on clustering technique and prediction technique. Now a days large amount of data stored in educational database increasing rapidly. The database for particular set of student was collected. The clustering and prediction is made on some detailed manner and the results were produce. The K-means clustering algorithm is used here. To find nearest possible a cluster a similar group the turning point India is the performance in higher education for all students. This academic performance is influenced by various factor, therefore to identify the difference between high learners and slow learner students it is important for student performance to develop predictive data mining model.
Data Driven College Counseling by SchooLinksKatie Fang
This workshop will expose school counselors and administrators to a framework for data-driven college planning and accountability. Attendees will learn about data collection, pattern analysis, and translating insight into intervention to best support students in their college planning process. No special statistical knowledge is required for this session, just enthusiasm to understand how using data unlock better student outcomes.
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
Advances in Learning Analytics and Educational Data Mining MehrnooshV
This presentation is about the state-of-the-art of Learning Analytics and Edicational Data Mining. It is presented by Mehrnoosh Vahdat as the introductory tutorial of Special Session 'Advances in Learning Analytics and Educational Data Mining' at ESANN 2015 conference.
This paper highlights important issues of higher education system such as predicting student’s academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an in-depth awareness about how these algorithms predict student the performance of different student and enhance their skill.
Student Performance Evaluation in Education Sector Using Prediction and Clust...IJSRD
Data mining is the crucial steps to find out previously unknown information from large relational database. various technique and algorithm are their used in data mining such as association rules, clustering and classification and prediction techniques. Ease of the techniques contains particular characteristics and behaviour. In this paper the prime focus on clustering technique and prediction technique. Now a days large amount of data stored in educational database increasing rapidly. The database for particular set of student was collected. The clustering and prediction is made on some detailed manner and the results were produce. The K-means clustering algorithm is used here. To find nearest possible a cluster a similar group the turning point India is the performance in higher education for all students. This academic performance is influenced by various factor, therefore to identify the difference between high learners and slow learner students it is important for student performance to develop predictive data mining model.
Data Driven College Counseling by SchooLinksKatie Fang
This workshop will expose school counselors and administrators to a framework for data-driven college planning and accountability. Attendees will learn about data collection, pattern analysis, and translating insight into intervention to best support students in their college planning process. No special statistical knowledge is required for this session, just enthusiasm to understand how using data unlock better student outcomes.
DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVEIJDKP
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehouse and the whole process is divded into action plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it.
Application of Higher Education System for Predicting Student Using Data mini...AM Publications
The aim of research paper is to improve the current trends in the higher education systems to understand
from the outside which factors might create loyal students. The necessity of having loyal students motivates higher
education systems to know them well, one way to do this is by using valid management and processing of the students
database. Data mining methods represent a valid approach for the extraction of precious information from existing
students to manage relations with future students. This may indicate at an early stage which type of students will
potentially be enrolled and what areas to concentrate upon in higher education systems for support. For this purpose
the data mining framework is used for mining related to academic data from enrolled students. The rule generation
process is based on the classification method. The generated rules are studied and evaluated using different
evaluation methods and the main attributes that may affect the student’s loyalty have been highlighted. Software that
facilitates the use of the generated rules is built which allows the higher education systems to predict the student’s
loyalty (numbers of enrolled students) so that they can manage and prepare necessary resources for the new enrolled students.
Predicting instructor performance using data mining techniques in higher educ...redpel dot com
Predicting instructor performance using data mining techniques in higher education
for more ieee paper / full abstract / implementation , just visit www.redpel.com
Teachers Name The Digital Learning Tools They Use Most OftenJustin Wedell
As part of our survey of ed tech uses and perceptions in U.S. PreK-12 public schools, we asked teachers to name the digital learning tools that they use most often for the subject(s) that they teach. A teacher’s subject area was determined by the teacher noting their main subject area(s) taught. These charts outline the breakdown for those teachers who listed Math, ELA, Reading, or Science as their primary or secondary subject. The percentages within the bar charts reflect the percentage of teacher respondents within that category who named that particular tool as one of their most often used. We highlighted any tools that were named by 5% or more of teacher respondents within that category. The percentages in donut charts reflect how many times a tool was named relative to others within that specific grade band. We highlighted any tools that accounted for two percent % or more of all named tools within that category.
Note: readers should not interpret the results as an indicator of tool popularity or endorsement of particular tools. The survey - as well as this particular question - was not designed to rank tool preferences or quality within the educator population.
Data Mining Techniques for School Failure and Dropout SystemKumar Goud
Abstract: Data mining techniques are applied to predict college failure and bum of the student. This is method uses real data on middle-school students for prediction of failure and drop out. It implements white-box classification strategies, like induction rules and decision trees or call trees. Call tree could be a call support tool that uses tree-like graph or a model of call and their possible consequences. A call tree is a flowchart-like structure in which internal node represents a "test" on an attribute. Attribute is the real information of students that is collected from college in middle or pedagogy, each branch represents the outcome of the test and each leaf node represents a class label. The paths from root to leaf represent classification rules and it consists of three kinds of nodes which incorporates call node, likelihood node and finish node. It is specifically used in call analysis. Using this technique to boost their correctness for predicting which students might fail or dropout (idler) by first, using all the accessible attributes next, choosing the most effective attributes. Attribute choice is done by using WEKA tool.
Keywords: dataset, classification, clustering.
The Relationship of Electronic Reference and the Development of Distance Educ...Dr. Starr Hoffman
This presentation discusses the relationship of electronic reference and the development of distance education programs with data and research findings.
DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVEIJDKP
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehouse and the whole process is divded into action plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it.
Application of Higher Education System for Predicting Student Using Data mini...AM Publications
The aim of research paper is to improve the current trends in the higher education systems to understand
from the outside which factors might create loyal students. The necessity of having loyal students motivates higher
education systems to know them well, one way to do this is by using valid management and processing of the students
database. Data mining methods represent a valid approach for the extraction of precious information from existing
students to manage relations with future students. This may indicate at an early stage which type of students will
potentially be enrolled and what areas to concentrate upon in higher education systems for support. For this purpose
the data mining framework is used for mining related to academic data from enrolled students. The rule generation
process is based on the classification method. The generated rules are studied and evaluated using different
evaluation methods and the main attributes that may affect the student’s loyalty have been highlighted. Software that
facilitates the use of the generated rules is built which allows the higher education systems to predict the student’s
loyalty (numbers of enrolled students) so that they can manage and prepare necessary resources for the new enrolled students.
Predicting instructor performance using data mining techniques in higher educ...redpel dot com
Predicting instructor performance using data mining techniques in higher education
for more ieee paper / full abstract / implementation , just visit www.redpel.com
Teachers Name The Digital Learning Tools They Use Most OftenJustin Wedell
As part of our survey of ed tech uses and perceptions in U.S. PreK-12 public schools, we asked teachers to name the digital learning tools that they use most often for the subject(s) that they teach. A teacher’s subject area was determined by the teacher noting their main subject area(s) taught. These charts outline the breakdown for those teachers who listed Math, ELA, Reading, or Science as their primary or secondary subject. The percentages within the bar charts reflect the percentage of teacher respondents within that category who named that particular tool as one of their most often used. We highlighted any tools that were named by 5% or more of teacher respondents within that category. The percentages in donut charts reflect how many times a tool was named relative to others within that specific grade band. We highlighted any tools that accounted for two percent % or more of all named tools within that category.
Note: readers should not interpret the results as an indicator of tool popularity or endorsement of particular tools. The survey - as well as this particular question - was not designed to rank tool preferences or quality within the educator population.
Data Mining Techniques for School Failure and Dropout SystemKumar Goud
Abstract: Data mining techniques are applied to predict college failure and bum of the student. This is method uses real data on middle-school students for prediction of failure and drop out. It implements white-box classification strategies, like induction rules and decision trees or call trees. Call tree could be a call support tool that uses tree-like graph or a model of call and their possible consequences. A call tree is a flowchart-like structure in which internal node represents a "test" on an attribute. Attribute is the real information of students that is collected from college in middle or pedagogy, each branch represents the outcome of the test and each leaf node represents a class label. The paths from root to leaf represent classification rules and it consists of three kinds of nodes which incorporates call node, likelihood node and finish node. It is specifically used in call analysis. Using this technique to boost their correctness for predicting which students might fail or dropout (idler) by first, using all the accessible attributes next, choosing the most effective attributes. Attribute choice is done by using WEKA tool.
Keywords: dataset, classification, clustering.
The Relationship of Electronic Reference and the Development of Distance Educ...Dr. Starr Hoffman
This presentation discusses the relationship of electronic reference and the development of distance education programs with data and research findings.
Education analytics – reporting students growth using sgp modeleSAT Journals
Abstract Every part of the education sector is struggling to produce actionable data favorable for their growth. The primary stakeholders of this sector are unable to take effective and productive decisions as the huge amount of data collected is not being processed properly. A lot of striking data are lost in the process as there are no schemas available for extracting the intelligence from them. Various external factors affecting the student’s growth are not identified and thus the parents and teachers fail to understand the real reason behind the student’s performance. Hence to measure student's growth SGP (Students Growth Percentile) can be used. It is also necessary to keep tab on student's future marks so as to take precautionary measure in case of negative growth. Here, time series analysis and forecasting can be used. Regression is use to calculate impact of any external factors on overall performance. When all these identified external myriad data along with the academic data is captured, processed using analytical models such as R. The stakeholders will be able to understand the core reasoning behind progress rate and thus take decisions accordingly. This is the fundamental idea behind Education Analytics. Keywords - Analytics, Education Analytics, Student’s Growth Percentile, Marks Forecasting, Student’s growth
This slide was presented in International the 2015 Conference on Education Research.
I aggregated several my other partial slides and reports to describe adaptive learning model pertaining to concept of learning analytics as well as LOD for curriculum standards and digital resources. There is short introduction to the project of ISO/IEC 20748 Learning analytics interoperability - Part 1: Reference model.
Data mining approach to predict academic performance of studentsBOHRInternationalJou1
Powerful data mining techniques are available in a variety of educational fields. Educational research is
advancing rapidly due to the vast amount of student data that can be used to create insightful patterns
related to student learning. Educational data mining is a tool that helps universities assess and identify student
performance. Well-known classification techniques have been widely used to determine student success in
data mining. A decisive and growing exploration area in educational data mining (EDM) is predicting student
academic performance. This area uses data mining and automaton learning approaches to extract data from
education repositories. According to relevant research, there are several academic performance prediction
methods aimed at improving administrative and teaching staff in academic institutions. In the put-forwarded
approach, the collected data set is preprocessed to ensure data quality and labeled student education data
is used to apply ANN classifiers, support vector classifiers, random forests, and DT Compute and train a
classifier. The achievement of the four classifications is measured by accuracy value, receiver operating curve
(ROC), F1 score, and confusion matrix scored by each model. Finally, we found that the top three algorithmic
models had an accuracy of 86–95%, an F1 score of 85–95%, and an average area under ROC curve of
OVA of 98–99.6%
Big Data and Advanced Analytics For Improving Teaching Practices In 2023 | Fu...Future Education Magazine
Here are 7 ways of big data and advanced analytics to improve teaching practices: 1. Data Sources in Education 2. The Role of Big Data in Education 3. Advanced Analytics in Education 4. Assessing Teaching Practices with Data 5. Enhancing Teaching Practices with Data
Learning analytics are more than measurementDragan Gasevic
Slides used for the keynote
Learning analytics are more than measurement
at
Policies for Educational Data Mining and Learning Analytics Briefing
organized by http://www.laceproject.eu/
5/25/2020 Rubric Detail – 31228.202030
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and made it available to you. Select Grid View or List View to change the rubric's layout.
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Name: ITS836 (8 Week) Research Paper Rubric
Description: Please use this rubric for grading research papers
Exit
Grid View List View
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
https://ucumberlands.blackboard.com/webapps/rubric/do/course/gradeRubric?mode=grid&isPopup=true&rubricCount=1&prefix=_843783_1&course_i… 2/4
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.
Data Driven Instructional Decision MakingA framework.docxwhittemorelucilla
Data Driven
Instructional Decision Making
A framework
Data –Driven Instruction
Data-driven instruction is characterized by cycles
that provide a feedback loop
in which teachers plan and deliver instruction, assess student
understanding through the collection of data, analyze the data, and
then pivot instruction based on insights from their analysis.
From: Teachers know best: Making Data Work For Teachers and Students
Bill & Melinda Gates Foundation
https://s3.amazonaws.com/edtech-production/reports/Gates-TeachersKnowBest-MakingDataWork.pdf
Data-Driven Decision Making Process Cycle
Data Planning
and
Production
Data Analysis
Developing
an Action
Plan
Monitoring
progress
Measuring
Success
Implementing
the Action
Plan
Data is used
From : Teachers know best: Making Data Work For Teachers and Students
Bill & Melinda Gates Foundation
https://s3.amazonaws.com/edtech-production/reports/Gates-
TeachersKnowBest-MakingDataWork.pdf
Data –Driven Instruction Feedback Loop
Data Planning
and
Production
Data Analysis
Developing an
Action Plan
Monitoring
progress
Measuring
Success
Implementing
the Action
Plan
Data –Driven Instruction Feedback Loop
Data Planning
and
Production
Data Analysis
Developing an
Action Plan
Monitoring
progress
Measuring
Success
Implementing
the Action
Plan
Instructors need to
facilitate this data –driven
instruction decision loop
in a timely and smooth
fashion
…and on an ongoing basis
• Per student
• Per class
• Per group
Data –Driven Instruction Feedback Loop
Roles Inherent in the Data-Driven Instruction
Decision Making Loop
• Planner
• Data Producer
• Data Analyst
• Monitor
• Reporter
• Data End User
• IT
• Operations and Logistics
Data Planning and Production Questions
• What questions are to be addressed in future data-informed
conversations? Which questions are more important?
• What information (metrics) are needed to answer these question?
• Is the information available and feasibly attainable?
• Are the necessary technology and resources available?
• How can current non-data based instructional decision making be
mapped to data-based instructional decision making process?
• What are the costs associated with this endeavor?
• What are the timelines ?
• How and when will the data be collected and stored?
Data Analysis Questions
• What relations exists between the metrics? What patterns do
the data reveal?
• How many levels of the metric are needed to answer the
questions?
• Do the original questions need to be revised or expanded?
• Do the original metrics need to be redefined or expanded?
• What analytical tools are currently available? What tools
need to be designed to support the analysis?
• What method of analysis or evaluation will be used?
• What are the data limitations, strengths, challenges, context?
Monitor Questions
• How are the metrics evolving as the learning and instructional
processes evolve.
Dyslexia and Technology presentation at NJPAECET2 conference at Raritan Valley Community College 9/19 and 9/20. A community dedicated to the evelevating and celebrating of the teaching profession.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
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.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
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.
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.
1. Managing District and School Information Sandi Paul Director of Technology Sayreville Public Schools NJ EXCEL MIS Session
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7. Learnia New Jersey 2009-2010 Content Description Assessment & Information Pearson
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13. ClassViews™ State Benchmark Tests Key Feature Accountability snapshot Purpose Reports proficiency on assessments modeled after NJ ASK Content Areas Reading and mathematics Grades 3-8 Number of Forms (tests) 2 forms (per grade and subject) Length of Form Between 25-33 items per form About 2-6 open-ended (SCR & ER – in math) items per form
14. ClassLinks™ Item Bank Key Feature Answer choice rationale Purpose Diagnose student strengths and weaknesses Content Areas Reading, math, science, writing Grades 3-8, except science (4, 8) Number of Forms (tests) To be determined by educator Length of Form To be determined by educator
Talk about the multiple measures of data based upon Victoria Bernhard and ask how they are measuring these. http://eff.csuchico.edu/ is Victoria Bernhardt’s website All school data can fall into one of the 4 categories Student demographics can predict results at the district and school levels – need to look at policies for unit of change at district level Student leanring is the test scores. Answers often found tied to district demographic level School process is the instructional strategies. Standards and assessment at the school and classroom level Organize participants’ data around the room. Do a gallery walk and write three sentences “What I saw.” Share. The discuss with neighbor “so what”.
Data Warehousing is a process , not a product It is a process for properly assembling and managing data from various sources for the purpose of answering educational questions and making decisions that were not previously possible. The Educational Imperative Planning and Design The User Environment The Implementation Operation and Maintenance Accessible at different levels Builds graphs Disaggregates on the fly Point and click/drag and drop user interfact Creates standards reports with click of a mouse Able to follow a cohort Ex www.tetradata.com
http://www.mediabrains.com/client/eschooln/bg1/search.asp What is the scope of this integration project? What efficiencies (“most useful” automations) do you want to gain through integration? What data needs to move and to where? What are the changes you expect to see through integration?
Learnia is the Online Assessment Solution that your school/district has chosen for you to implement and use to gather formative data on your students. Site Code: LN02 Username: Sayre Password: S4936
What this training will answer for the attendees:
EDsmart is a solution for data driven decision-making from Public Consulting Group Username: njsmart\\spaul Password: T3ch!cian