This study aims to gather the perspectives on collaborative learning of preservice teachers from the Computer Education and Instructional Technology department of Fırat University.
A HYBRID CLASSIFICATION ALGORITHM TO CLASSIFY ENGINEERING STUDENTS’ PROBLEMS ...IJDKP
The social networking sites have brought a new horizon for expressing views and opinions of individuals.
Moreover, they provide medium to students to share their sentiments including struggles and joy during the
learning process. Such informal information has a great venue for decision making. The large and growing
scale of information needs automatic classification techniques. Sentiment analysis is one of the automated
techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to
classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful
to draw decisions in education system since they classify the sentiments into merely three pre-set
categories: positive, negative and neutral. Moreover, classifying the students’ sentiments into positive or
negative category does not provide deeper insight into their problems and perks. In this paper, we propose
a novel Hybrid Classification Algorithm to classify engineering students’ sentiments. Unlike traditional
predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process
descriptive. Moreover, it classifies engineering students’ perks in addition to problems into several
categories to help future students and education system in decision making.
NMONFORTPART 1ALSO I would suggest finding the height difference f.docxhenrymartin15260
NMONFORT
PART 1ALSO I would suggest finding the height difference for males and females.COLLECT DATA BASED ON THE RESEARCH PLAN, THEN:· CREATE A summary THAT should include:· The results of your data collection efforts. Did your collection tool work as you expected? What surprises or challenges did you encounter? Are there are sources of bias in your data? What might you do differently “next time”?· An initial analysis of your data IN RED. it should give the group an idea of what you have done, what you intend to do, and any questions or concerns you have about the analysis.· Specific questions or topics you would like feedback on.Research Plan
Ask a Question: Do boys or girls have a larger growth spurt between the grades of two and six?
Observational Unit: The boys and girls
Variable: Heights at grades 2 and 6
Collect Appropriate Data: Since school is closed. I will be collecting data from students at our local Elementary and Middle School. The information is housed in the nurse’s office and is accessible through her. I will take the student information which is in alphabetical order and choose every third student until I gather heights for 20 girls and 20 boys. There are a total of 100 sixth graders in the school to choose from.
Analyze the Data: My data will be organized by grade and by gender. I will use a double line plot for boys and a separate double line plot for girls. I will find the mean of each plot to determine who had the larger growth spurt over those two grades.
Interpret the Results: My expectations are to find that at this level girls will have the larger growth spurt. I am basing this simply on past observations. Boys seem to have their growth spurt in Middle school. Possible biases may include convenience sampling since my data is only being taken from one school. I may also have a measurement bias since these student’s heights were taken by hand and then copied onto a medical card. This information was then put into the computer.
COMPARING GROWTH SPURT OF BOYS AND GIRLS BETWEEN TWO GRADESTABLE OF CONTENTS
ABSTRUCT
Chapter One
1.1 Background information………………….....................................................
1.2 Statement of the problem...........................................................................
1.3Objectives of the study................................................................................
1.4Significance of the study..............................................................................
Chapter Two: Review Of the Literature ……………………………………………………..
Chapter Three: Research Methodology
3.1 Research Design and data collection..............................................................
ABSTRACT
In the study, the design applied to get the data will be simple random sampling without replacement.
The data will be analyzed and conclusions made by comparison of the students total heights in their genders at the two different grades.
Introduction
1..
Dr. Nasrin Nazemzadeh, PhD Dissertation Defense, Dr. William Allan Kritsonis,...William Kritsonis
Dr. William Allan Kritsonis, PhD Dissertation Chair for Dr. Nasrin Nazemzadeh, PhD Program in Educational Leadership, PVAMU, Member of the Texas A&M University System.
Running head METHODOLOGY 1METHODOLOGY 5Method.docxglendar3
Running head: METHODOLOGY 1
METHODOLOGY 5
Methodology
Linda Holmes
Capella University
CHAPTER 3. METHODOLOGY
Purpose of the Study
In the reviewed literature, cyberbullying and school bullying have been identified as the risk factors of social media (Underwood & Ehrenreich, 2017). However, even with the identified risks of social media, there has been increasing in penetration of internet access and growing use of the telephones and social media networking sites exposing teenagers to more danger. Schools have and others are adopting e-learning which creates space for students to interact on social media.
Considering the environment that we are living, in some cases, teachers are not aware of the impact of the cyberbullying and school bullying to academic performance. In other cases, students do not report incidences of the cyberbullying and school bullying which means they continue to suffer in silence. It is within this basis that this study is conducted which will seek to identify the connection of the cyberbullying and school bullying and their effects to the academic performance for grade 8 and 9 students.
Research question and hypothesis
RQ: Is there a positive relationship between cyberbullying and school bullying for experimental school and control school for girls aged 13-14 years and how they influence academic performance?
Ho1: There is no significant positive relationship between cyberbullying and school bullying for experimental school and control school for girls aged 13-14 years and how they influence academic performance.
Ha1: There is a significant positive relationship between cyberbullying and school bullying for experimental school and control school for girls aged 13-14 years and how they influence academic performance.
According to the research question cyberbullying and school bullying are dependent variables while academic performance will be an independent variable.
Research Design
A quasi-experimental design will be used for this study. This is because the research project entails the control and treatment group. For the control group, the sample of the students will be extracted and will not be exposed to cyberbullying but for the treatment groups, the selected sample will be exposed to the cyberbullying. Therefore, considering the nature of the information that will be required and types of the samples that will be needed, the quasi-experimental design is most appropriate and specifically when control and treatment experiments are carried out (Aussems, Boomsma & Snijders, 2011).
Target Population and Sample
In the study, will include only girls students aged 13 -14 years selected from both public school and private schools. There will be a total sample size of 100 students with 50 from public school and 50 from the private school. To be considered to be part of the sample, students must allow for the incubation for three months, must have an email address and Facebook accounts.
In eac.
Running head METHODOLOGY 1METHODOLOGY 5Method.docxtodd581
Running head: METHODOLOGY 1
METHODOLOGY 5
Methodology
Linda Holmes
Capella University
CHAPTER 3. METHODOLOGY
Purpose of the Study
In the reviewed literature, cyberbullying and school bullying have been identified as the risk factors of social media (Underwood & Ehrenreich, 2017). However, even with the identified risks of social media, there has been increasing in penetration of internet access and growing use of the telephones and social media networking sites exposing teenagers to more danger. Schools have and others are adopting e-learning which creates space for students to interact on social media.
Considering the environment that we are living, in some cases, teachers are not aware of the impact of the cyberbullying and school bullying to academic performance. In other cases, students do not report incidences of the cyberbullying and school bullying which means they continue to suffer in silence. It is within this basis that this study is conducted which will seek to identify the connection of the cyberbullying and school bullying and their effects to the academic performance for grade 8 and 9 students.
Research question and hypothesis
RQ: Is there a positive relationship between cyberbullying and school bullying for experimental school and control school for girls aged 13-14 years and how they influence academic performance?
Ho1: There is no significant positive relationship between cyberbullying and school bullying for experimental school and control school for girls aged 13-14 years and how they influence academic performance.
Ha1: There is a significant positive relationship between cyberbullying and school bullying for experimental school and control school for girls aged 13-14 years and how they influence academic performance.
According to the research question cyberbullying and school bullying are dependent variables while academic performance will be an independent variable.
Research Design
A quasi-experimental design will be used for this study. This is because the research project entails the control and treatment group. For the control group, the sample of the students will be extracted and will not be exposed to cyberbullying but for the treatment groups, the selected sample will be exposed to the cyberbullying. Therefore, considering the nature of the information that will be required and types of the samples that will be needed, the quasi-experimental design is most appropriate and specifically when control and treatment experiments are carried out (Aussems, Boomsma & Snijders, 2011).
Target Population and Sample
In the study, will include only girls students aged 13 -14 years selected from both public school and private schools. There will be a total sample size of 100 students with 50 from public school and 50 from the private school. To be considered to be part of the sample, students must allow for the incubation for three months, must have an email address and Facebook accounts.
In eac.
Final Project ScenarioA researcher has administered an anxiety.docxAKHIL969626
Final Project Scenario
A researcher has administered an anxiety survey to students enrolled in graduate level statistics courses. The survey included three subscales related to statistics anxiety: (a) interpretation anxiety, (b) test anxiety, and (c) fear of asking for help. For the items that comprised the scales, students were asked to respond using a 5 point likert-type scale ranging from (1) No Anxiety to (5) High Anxiety. Therefore, higher scores on the anxiety subscales implied higher levels of anxiety.
In addition to the statistics anxiety subscales, the survey contained a subscale related to the use of statistical software and a subscale related to self-perceived confidence concerning general computer use. Students responded to items on the statistical software subscale using a response range from (1) Strongly Disagree to (7) Strongly Agree. For the computer confidence subscale, students responded to items using a range from (1) Strongly Disagree to (5) Strongly Agree. For each of these subscales, higher scores implied higher levels of confidence.
The researcher determined the score for each subscale by computing the mean response for the items associated with the subscale. This technique resulted in subscales that had the same possible range and the items that made up the subscale.
A subsample of the researcher’s dataset contains the following variables that should be used for completing the four final projects. The variables included in the dataset are:
Variable name:
Label:
Values:
gender
1: Female
2: Male
race
1: White
2: Non-White
age
courses
Number of online courses completed
1: 0-2 courses
2: 3-7 courses
3: 8 or more courses
interpret
Anxiety associated with reading and interpreting output from analyses
test
Anxiety associated with taking a test in a statistics course
help
Anxiety associated with asking for help during a statistics course
software
Self-reported level of confidence is using statistical software
computer
Self-reported confidence in general computer use
Final Project 1:
Use SPSS to conduct the necessary analysis of the Age variable and answer each of the following questions.
Questions:
1. What is the value of n?
2. What is the mean age?
3. What is the median age?
4. What was the youngest age?
5. What was the oldest age?
6. What is the range of ages?
7. What is the standard deviation of the ages?
8. What is the value of the skewness statistic?
9. What are the values of the 25th, 50th, and 75th percentiles?
10. Present the results as they might appear in an article. This must include a table and narrative statement that provides a thorough description of the central tendency and distribution of the ages.
Final Project 2
One of the researcher’s questions involved the difference in scores on the Interpretation Anxiety subscale between male and female respondents. Use SPSS to conduct the analysis that is appropriate for this research question and answer each o ...
Running head IMPACT OF SOCIAL MEDIA ON STUDENT’S PERFORMANCE1.docxwlynn1
Running head: IMPACT OF SOCIAL MEDIA ON STUDENT’S PERFORMANCE
1
IMPACT OF SOCIAL MEDIA ON STUDENT’S PERFORMANCE
8
Impact of social media on student’s performance
Rodriquez Mitchell
Northcentral University
Introduction
In my selection of an article which fits the assignment criteria I zeroed down to a peer-reviewed article entitled “Impact of social media on student’s performance”. The article was authored in 2013 by Sara Selvaraj of Vels University. This work posits to explain the issue being addressed in the research article, the purpose of the work, provide a summary of the research questions therein, describe both the null and alternative hypothesis used by the author, show application of the conceptual framework, and discuss the methodology used and the limitation of the article.
Describe the problem or issue addressed.
The main issue addressed in the article is the consequences of students using social networking platforms. This means the impact that social networking has on the education system. As evident from the literature part of the work social networking sites are not meant to have a negative effect on the education system. However, it has turned out that there is an array of negative effects of using social networking sites by students. One of these problems is prompted by social networking site addiction. Students with access to the internet and have social media networking sites accounts spend a significant time of their day on these sites. The impact of that is the students are left with little or no time for their personal studies hence cannot submit things like assignments in a timely fashion (Selvaraj, 2013). Secondly, the students are poised to fail their examinations or experience a decline in their academic scores. The article attempts to show the severity of this problem and provide proof that indeed the problems exist.
Describe the purpose or intent of the study.
The article has 3 main objectives or intents. The first objective is to determine the influence of various social networking sites on student’s academic performance. Young children or generation is one of the most affected by social networking sites. The study tries to investigate the difference between the performance of the students before starting to use the sites and the performance after starting to use the sites. The second objective of the study is to investigate how the education system in totality has been impacted by social networking sites. This objective arises from the knowledge that not only student use the sites. The websites are used nearly by everyone in the sector irrespective of age, position and professional. This use must have an effect and it’s this impact that the work tries to unravel. The third objective of the study is to determine the motivation behind the use of social networking sites. These are the uses which are prompting individuals to sign up of social media accounts. The work also tries to discover the uses of the si.
Do boys or girls have a larger growth spurt between the grades o.docxjacksnathalie
Do boys or girls have a larger growth spurt between the grades of two and six?
By Nerlande Monfort
CMATH 6114
Comparative Study
The Plan
Ask a Question: Do boys or girls have a larger growth spurt between the grades of two and six?
Observational Unit: The boys and girls
Variable: Heights at grades 2 and 6
Collect Appropriate Data:
Since school is closed. I will be collecting data from students at our local Elementary and Middle School. The information is housed in the nurse’s office and is accessible through her. I will take the student information which is in alphabetical order and choose every third student until I gather heights for 20 girls and 20 boys. There are a total of 100 sixth graders in the school to choose from.
Analyze the Data: My data will be organized by grade and by gender. I will use a double line plot for boys and a separate double line plot for girls. I will find the mean of each plot to determine who had the larger growth spurt over those two grades.
Interpret the Results:
My expectations are to find that at this level girls will have the larger growth spurt. I am basing this simply on past observations. Boys seem to have their growth spurt in Middle school. Possible biases may include convenience sampling since my data is only being taken from one school. I may also have a measurement bias since these student’s heights were taken by hand and then copied onto a medical card. This information was then put into the computer.
ABSTRACT
In the study, the design applied to get the data will be simple random sampling without replacement.
The data will be analyzed and conclusions made by comparison of the students total heights in their genders at the two different grades.
Background
The previous research on this topic have reported the general growth but failed to count on the ages between these two grades. The applications of the previous research reports have shown reliance and believe in the general growth pattern of growth in young school going children though they have not specified on these two ages to explain whether it’s a just a believe that girls grow faster between these grades or its true from practical research findings.
Design of the research and data collection techniques
To ensure the collection of high-quality data, the data will be obtained from the identified population to get reliable and make conclusions. There will be a proper way of designing the sampling strategy used to ensure that potential boys and girls are picked who will be drawn from a representative sample of the intended population.
Design of the research and data collection techniques cont…
The samples will be obtained in a scientifically rigorous manner to ensure the findings will be generalizable to the intended population. The analysis of the data will not be homogenously because the selected survey compares two different grades and in different genders significantly.
Design of the research and data collect ...
This study aims to gather the perspectives on collaborative learning of preservice teachers from the Computer Education and Instructional Technology department of Fırat University.
A HYBRID CLASSIFICATION ALGORITHM TO CLASSIFY ENGINEERING STUDENTS’ PROBLEMS ...IJDKP
The social networking sites have brought a new horizon for expressing views and opinions of individuals.
Moreover, they provide medium to students to share their sentiments including struggles and joy during the
learning process. Such informal information has a great venue for decision making. The large and growing
scale of information needs automatic classification techniques. Sentiment analysis is one of the automated
techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to
classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful
to draw decisions in education system since they classify the sentiments into merely three pre-set
categories: positive, negative and neutral. Moreover, classifying the students’ sentiments into positive or
negative category does not provide deeper insight into their problems and perks. In this paper, we propose
a novel Hybrid Classification Algorithm to classify engineering students’ sentiments. Unlike traditional
predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process
descriptive. Moreover, it classifies engineering students’ perks in addition to problems into several
categories to help future students and education system in decision making.
NMONFORTPART 1ALSO I would suggest finding the height difference f.docxhenrymartin15260
NMONFORT
PART 1ALSO I would suggest finding the height difference for males and females.COLLECT DATA BASED ON THE RESEARCH PLAN, THEN:· CREATE A summary THAT should include:· The results of your data collection efforts. Did your collection tool work as you expected? What surprises or challenges did you encounter? Are there are sources of bias in your data? What might you do differently “next time”?· An initial analysis of your data IN RED. it should give the group an idea of what you have done, what you intend to do, and any questions or concerns you have about the analysis.· Specific questions or topics you would like feedback on.Research Plan
Ask a Question: Do boys or girls have a larger growth spurt between the grades of two and six?
Observational Unit: The boys and girls
Variable: Heights at grades 2 and 6
Collect Appropriate Data: Since school is closed. I will be collecting data from students at our local Elementary and Middle School. The information is housed in the nurse’s office and is accessible through her. I will take the student information which is in alphabetical order and choose every third student until I gather heights for 20 girls and 20 boys. There are a total of 100 sixth graders in the school to choose from.
Analyze the Data: My data will be organized by grade and by gender. I will use a double line plot for boys and a separate double line plot for girls. I will find the mean of each plot to determine who had the larger growth spurt over those two grades.
Interpret the Results: My expectations are to find that at this level girls will have the larger growth spurt. I am basing this simply on past observations. Boys seem to have their growth spurt in Middle school. Possible biases may include convenience sampling since my data is only being taken from one school. I may also have a measurement bias since these student’s heights were taken by hand and then copied onto a medical card. This information was then put into the computer.
COMPARING GROWTH SPURT OF BOYS AND GIRLS BETWEEN TWO GRADESTABLE OF CONTENTS
ABSTRUCT
Chapter One
1.1 Background information………………….....................................................
1.2 Statement of the problem...........................................................................
1.3Objectives of the study................................................................................
1.4Significance of the study..............................................................................
Chapter Two: Review Of the Literature ……………………………………………………..
Chapter Three: Research Methodology
3.1 Research Design and data collection..............................................................
ABSTRACT
In the study, the design applied to get the data will be simple random sampling without replacement.
The data will be analyzed and conclusions made by comparison of the students total heights in their genders at the two different grades.
Introduction
1..
Dr. Nasrin Nazemzadeh, PhD Dissertation Defense, Dr. William Allan Kritsonis,...William Kritsonis
Dr. William Allan Kritsonis, PhD Dissertation Chair for Dr. Nasrin Nazemzadeh, PhD Program in Educational Leadership, PVAMU, Member of the Texas A&M University System.
Running head METHODOLOGY 1METHODOLOGY 5Method.docxglendar3
Running head: METHODOLOGY 1
METHODOLOGY 5
Methodology
Linda Holmes
Capella University
CHAPTER 3. METHODOLOGY
Purpose of the Study
In the reviewed literature, cyberbullying and school bullying have been identified as the risk factors of social media (Underwood & Ehrenreich, 2017). However, even with the identified risks of social media, there has been increasing in penetration of internet access and growing use of the telephones and social media networking sites exposing teenagers to more danger. Schools have and others are adopting e-learning which creates space for students to interact on social media.
Considering the environment that we are living, in some cases, teachers are not aware of the impact of the cyberbullying and school bullying to academic performance. In other cases, students do not report incidences of the cyberbullying and school bullying which means they continue to suffer in silence. It is within this basis that this study is conducted which will seek to identify the connection of the cyberbullying and school bullying and their effects to the academic performance for grade 8 and 9 students.
Research question and hypothesis
RQ: Is there a positive relationship between cyberbullying and school bullying for experimental school and control school for girls aged 13-14 years and how they influence academic performance?
Ho1: There is no significant positive relationship between cyberbullying and school bullying for experimental school and control school for girls aged 13-14 years and how they influence academic performance.
Ha1: There is a significant positive relationship between cyberbullying and school bullying for experimental school and control school for girls aged 13-14 years and how they influence academic performance.
According to the research question cyberbullying and school bullying are dependent variables while academic performance will be an independent variable.
Research Design
A quasi-experimental design will be used for this study. This is because the research project entails the control and treatment group. For the control group, the sample of the students will be extracted and will not be exposed to cyberbullying but for the treatment groups, the selected sample will be exposed to the cyberbullying. Therefore, considering the nature of the information that will be required and types of the samples that will be needed, the quasi-experimental design is most appropriate and specifically when control and treatment experiments are carried out (Aussems, Boomsma & Snijders, 2011).
Target Population and Sample
In the study, will include only girls students aged 13 -14 years selected from both public school and private schools. There will be a total sample size of 100 students with 50 from public school and 50 from the private school. To be considered to be part of the sample, students must allow for the incubation for three months, must have an email address and Facebook accounts.
In eac.
Running head METHODOLOGY 1METHODOLOGY 5Method.docxtodd581
Running head: METHODOLOGY 1
METHODOLOGY 5
Methodology
Linda Holmes
Capella University
CHAPTER 3. METHODOLOGY
Purpose of the Study
In the reviewed literature, cyberbullying and school bullying have been identified as the risk factors of social media (Underwood & Ehrenreich, 2017). However, even with the identified risks of social media, there has been increasing in penetration of internet access and growing use of the telephones and social media networking sites exposing teenagers to more danger. Schools have and others are adopting e-learning which creates space for students to interact on social media.
Considering the environment that we are living, in some cases, teachers are not aware of the impact of the cyberbullying and school bullying to academic performance. In other cases, students do not report incidences of the cyberbullying and school bullying which means they continue to suffer in silence. It is within this basis that this study is conducted which will seek to identify the connection of the cyberbullying and school bullying and their effects to the academic performance for grade 8 and 9 students.
Research question and hypothesis
RQ: Is there a positive relationship between cyberbullying and school bullying for experimental school and control school for girls aged 13-14 years and how they influence academic performance?
Ho1: There is no significant positive relationship between cyberbullying and school bullying for experimental school and control school for girls aged 13-14 years and how they influence academic performance.
Ha1: There is a significant positive relationship between cyberbullying and school bullying for experimental school and control school for girls aged 13-14 years and how they influence academic performance.
According to the research question cyberbullying and school bullying are dependent variables while academic performance will be an independent variable.
Research Design
A quasi-experimental design will be used for this study. This is because the research project entails the control and treatment group. For the control group, the sample of the students will be extracted and will not be exposed to cyberbullying but for the treatment groups, the selected sample will be exposed to the cyberbullying. Therefore, considering the nature of the information that will be required and types of the samples that will be needed, the quasi-experimental design is most appropriate and specifically when control and treatment experiments are carried out (Aussems, Boomsma & Snijders, 2011).
Target Population and Sample
In the study, will include only girls students aged 13 -14 years selected from both public school and private schools. There will be a total sample size of 100 students with 50 from public school and 50 from the private school. To be considered to be part of the sample, students must allow for the incubation for three months, must have an email address and Facebook accounts.
In eac.
Final Project ScenarioA researcher has administered an anxiety.docxAKHIL969626
Final Project Scenario
A researcher has administered an anxiety survey to students enrolled in graduate level statistics courses. The survey included three subscales related to statistics anxiety: (a) interpretation anxiety, (b) test anxiety, and (c) fear of asking for help. For the items that comprised the scales, students were asked to respond using a 5 point likert-type scale ranging from (1) No Anxiety to (5) High Anxiety. Therefore, higher scores on the anxiety subscales implied higher levels of anxiety.
In addition to the statistics anxiety subscales, the survey contained a subscale related to the use of statistical software and a subscale related to self-perceived confidence concerning general computer use. Students responded to items on the statistical software subscale using a response range from (1) Strongly Disagree to (7) Strongly Agree. For the computer confidence subscale, students responded to items using a range from (1) Strongly Disagree to (5) Strongly Agree. For each of these subscales, higher scores implied higher levels of confidence.
The researcher determined the score for each subscale by computing the mean response for the items associated with the subscale. This technique resulted in subscales that had the same possible range and the items that made up the subscale.
A subsample of the researcher’s dataset contains the following variables that should be used for completing the four final projects. The variables included in the dataset are:
Variable name:
Label:
Values:
gender
1: Female
2: Male
race
1: White
2: Non-White
age
courses
Number of online courses completed
1: 0-2 courses
2: 3-7 courses
3: 8 or more courses
interpret
Anxiety associated with reading and interpreting output from analyses
test
Anxiety associated with taking a test in a statistics course
help
Anxiety associated with asking for help during a statistics course
software
Self-reported level of confidence is using statistical software
computer
Self-reported confidence in general computer use
Final Project 1:
Use SPSS to conduct the necessary analysis of the Age variable and answer each of the following questions.
Questions:
1. What is the value of n?
2. What is the mean age?
3. What is the median age?
4. What was the youngest age?
5. What was the oldest age?
6. What is the range of ages?
7. What is the standard deviation of the ages?
8. What is the value of the skewness statistic?
9. What are the values of the 25th, 50th, and 75th percentiles?
10. Present the results as they might appear in an article. This must include a table and narrative statement that provides a thorough description of the central tendency and distribution of the ages.
Final Project 2
One of the researcher’s questions involved the difference in scores on the Interpretation Anxiety subscale between male and female respondents. Use SPSS to conduct the analysis that is appropriate for this research question and answer each o ...
Running head IMPACT OF SOCIAL MEDIA ON STUDENT’S PERFORMANCE1.docxwlynn1
Running head: IMPACT OF SOCIAL MEDIA ON STUDENT’S PERFORMANCE
1
IMPACT OF SOCIAL MEDIA ON STUDENT’S PERFORMANCE
8
Impact of social media on student’s performance
Rodriquez Mitchell
Northcentral University
Introduction
In my selection of an article which fits the assignment criteria I zeroed down to a peer-reviewed article entitled “Impact of social media on student’s performance”. The article was authored in 2013 by Sara Selvaraj of Vels University. This work posits to explain the issue being addressed in the research article, the purpose of the work, provide a summary of the research questions therein, describe both the null and alternative hypothesis used by the author, show application of the conceptual framework, and discuss the methodology used and the limitation of the article.
Describe the problem or issue addressed.
The main issue addressed in the article is the consequences of students using social networking platforms. This means the impact that social networking has on the education system. As evident from the literature part of the work social networking sites are not meant to have a negative effect on the education system. However, it has turned out that there is an array of negative effects of using social networking sites by students. One of these problems is prompted by social networking site addiction. Students with access to the internet and have social media networking sites accounts spend a significant time of their day on these sites. The impact of that is the students are left with little or no time for their personal studies hence cannot submit things like assignments in a timely fashion (Selvaraj, 2013). Secondly, the students are poised to fail their examinations or experience a decline in their academic scores. The article attempts to show the severity of this problem and provide proof that indeed the problems exist.
Describe the purpose or intent of the study.
The article has 3 main objectives or intents. The first objective is to determine the influence of various social networking sites on student’s academic performance. Young children or generation is one of the most affected by social networking sites. The study tries to investigate the difference between the performance of the students before starting to use the sites and the performance after starting to use the sites. The second objective of the study is to investigate how the education system in totality has been impacted by social networking sites. This objective arises from the knowledge that not only student use the sites. The websites are used nearly by everyone in the sector irrespective of age, position and professional. This use must have an effect and it’s this impact that the work tries to unravel. The third objective of the study is to determine the motivation behind the use of social networking sites. These are the uses which are prompting individuals to sign up of social media accounts. The work also tries to discover the uses of the si.
Do boys or girls have a larger growth spurt between the grades o.docxjacksnathalie
Do boys or girls have a larger growth spurt between the grades of two and six?
By Nerlande Monfort
CMATH 6114
Comparative Study
The Plan
Ask a Question: Do boys or girls have a larger growth spurt between the grades of two and six?
Observational Unit: The boys and girls
Variable: Heights at grades 2 and 6
Collect Appropriate Data:
Since school is closed. I will be collecting data from students at our local Elementary and Middle School. The information is housed in the nurse’s office and is accessible through her. I will take the student information which is in alphabetical order and choose every third student until I gather heights for 20 girls and 20 boys. There are a total of 100 sixth graders in the school to choose from.
Analyze the Data: My data will be organized by grade and by gender. I will use a double line plot for boys and a separate double line plot for girls. I will find the mean of each plot to determine who had the larger growth spurt over those two grades.
Interpret the Results:
My expectations are to find that at this level girls will have the larger growth spurt. I am basing this simply on past observations. Boys seem to have their growth spurt in Middle school. Possible biases may include convenience sampling since my data is only being taken from one school. I may also have a measurement bias since these student’s heights were taken by hand and then copied onto a medical card. This information was then put into the computer.
ABSTRACT
In the study, the design applied to get the data will be simple random sampling without replacement.
The data will be analyzed and conclusions made by comparison of the students total heights in their genders at the two different grades.
Background
The previous research on this topic have reported the general growth but failed to count on the ages between these two grades. The applications of the previous research reports have shown reliance and believe in the general growth pattern of growth in young school going children though they have not specified on these two ages to explain whether it’s a just a believe that girls grow faster between these grades or its true from practical research findings.
Design of the research and data collection techniques
To ensure the collection of high-quality data, the data will be obtained from the identified population to get reliable and make conclusions. There will be a proper way of designing the sampling strategy used to ensure that potential boys and girls are picked who will be drawn from a representative sample of the intended population.
Design of the research and data collection techniques cont…
The samples will be obtained in a scientifically rigorous manner to ensure the findings will be generalizable to the intended population. The analysis of the data will not be homogenously because the selected survey compares two different grades and in different genders significantly.
Design of the research and data collect ...
Women who choose Computer Science - what really mattersWBDC of Florida
Women who choose Computer Science - what really matters. The critical role of exposure and encouragement. Abstract
Google believes that a diverse workforce
leads to better products for diverse users,
and is especially committed to reversing
the negative trends around women in
Computer Science. To guide the company’s
outreach and investments in this space,
Google conducted a study to identify and
understand the factors that influence young
women’s decisions to pursue degrees in
Computer Science. It identified encouragement
and exposure as the leading factors
influencing this critical choice and learned
that anyone can help increase female
participation in Computer Science,
regardless of their technical abilities
or background.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Adjusting primitives for graph : SHORT REPORT / NOTES
Medium artical2
1. Do High School Students Favor Online or In-Person Instruction?
1. Introduction
The novel coronavirus has caused schools of all educational levels across the country
multiple issues regarding methods of providing education, grading, and more. With in-person
instruction being rendered impossible and unrealistic as an option for teaching, many schools
have switched to online instruction. At first, most students seemed ecstatic at the thought of not
having to wake up early anymore, but as time has passed, the few synchronous classes provided
at my high school proved tough to handle. In this study, I will be trying to compare the
preference between online and in-person learning of students at my high school and also examine
the association between the preference of online learning and the current grade level of said
students. I suspect that less high school students will prefer online instruction as opposed to in-
person instruction due to a reduced level of human interaction with learning online. I also suspect
that there is an association between preference of online person instruction and grade level.
Specifically, I think that juniors and seniors will prefer online learning more, as online learning
has made their final years before college applications less stressful.
2. Statistical Questions
The two statistical questions I am investigating are:
Q1: In general, do high school students at my high school prefer the new online instruction less
than the traditional in-person instruction?
Q2: Is there an association between the preference of online learning and the current grade level
of students?
2. 3. Sampling Procedure and Data Display
The best way of obtaining a sample is to obtain a random sample. However, my school
does not provide a list of student’s emails, making it impossible to select a sample. In addition, I
could not conduct an in-person survey on campus using systematic sampling, which could be
treated as a random sample under certain conditions, due to the government’s quarantine
guidelines. The best alternative was to conduct an online survey. With my two questions in mind,
it was necessary to be able to include members from all grade levels in my sample. Using our
school’s virtual learning platform, Schoology, I sent a google form survey with my questions so
that all members of the Upper School could see.
Figure 1: Survey Questions
All students who came across the survey viewed the same two questions. No statistics or
facts regarding the advantages or disadvantages of online or in-person instruction were included
in the questions so as to not induce any bias; students were simply asked to compare their
experiences from both methods of learning and choose which method they preferred and indicate
the level of preference. After 3 days from the initial release date of the survey, I did not count
3. any more responses. In the end, a total of 126 respondents across all grade levels replied to the
survey out of the population of roughly 500 high school students.
For my analysis, I decided to combine the number of people who strongly preferred
online instruction and the number of people who preferred online instruction into one category
named: “# of people who preferred online instruction overall.” It should be noted that there were
no students who filled out the choice “others,” therefore, this grouping is allowed. I also
combined the number of people who strongly preferred in-person instruction and the number of
people who preferred in-person instruction into one category named: “# of people who preferred
in-person instruction overall.” The reasoning behind including the different levels of preference
was for students who were leaning only slightly in favor of one of the two methods of instruction
to have an easier time selecting the option that best fit their preference. Then, the act of
combining the number of people who had a general preference towards one of the methods of
instruction allowed for me to answer the desired statistical question at hand. The raw data is
summarized in the table below, followed by a graphical representation of the data.
Raw Data
Grade Level # of people who prefer
online instruction overall
# of people who prefer in-
person instruction overall
TOTAL
Freshman 6 13 19
Sophomore 18 40 58
Junior 8 17 25
Senior 10 14 24
TOTAL 42 84 126
Table 1: Two-way contingency-table (observed counts)
4. Data Display
Figure 2: Percentage Distribution of Responses to “What Grade Are You In?”
Figure 3: Segmented bar graph depicting the number of people who preferred online and in-
person instruction by grade
A concern from this sampling method might be that it might not be a random sample.
However, a deeper investigation of the data shows that this is close to a random sample. Non-
randomness comes mainly from selection bias and nonresponse bias. Firstly, there is no selection
5. bias since all high schoolers had access to the survey. Secondly, from Table 1 and figure 2, we
can see that sophomores accounted for nearly half of the responses while only taking up a quarter
of the population. I presume that since I am a sophomore, my sophomore peers responded more
actively than others. Therefore, nonresponse mainly arose from the other three grades. If the
response pattern in sophomores is no different than the response pattern in the other grades, then
nonresponse bias can be safely neglected. To test the significance of the nonresponse bias, I
conducted a test to see if the true proportion of sophomores who preferred online learning and
the true proportion of all the other grades who preferred online learning was the same, with
hypotheses:
𝐻 : 𝑝 = 𝑝
𝐻 : 𝑝 ≠ 𝑝
Where 𝑝 represents the true proportion of sophomores who preferred online learning and
𝑝 represents the true proportion of all the other grades who preferred online learning.
The significance level is 𝛼 = 0.05.
I conducted a two-sample z-test for proportions. In terms of conditions, for the sample of
sophomores, there were 58 × = 18 successes and 58 − 18 = 40 failures. For the other
sample, there were 68 × = 24 successes and 68 − 24 = 44 failures. Due to both samples
having more than 15 successes and failures, the large count condition is met. The resulting test
statistic is:
𝑍 =
𝑝̂ − 𝑝̂
𝑝̂𝑞(
1
𝑛
+
1
𝑛
)
=
18
58
−
24
68
42
126
×
84
126
× (
1
58
+
1
68
)
= −0.5055
With p-value, p = 2(P (Z < -0.5055)) = 0.6132.
6. Since the p-value of 0.6132 is greater than 𝛼 = 0.05, we fail to reject the null hypothesis,
meaning that there is lack of evidence to suggest that the true proportion of sophomores who
prefer online learning is different from the true proportion of all the other grades combined who
prefer online learning. Therefore, there is minimal nonresponse bias, because the response
patterns of the underrepresented non-sophomores are similar to that of the sophomores, who are
the overrepresented ones; the sample I ended up with can be safely treated as a random sample.
4. Data Analysis
To answer the first question, a one-sample z-test will be conducted. Recalling my
predictions, I suspect that less high school students at my high school truly prefer online
instruction as opposed to in-person instruction. The null and alternative hypotheses are stated as
𝐻 : 𝑝 = 0.5
𝐻 : 𝑝 < 0.5
Where 𝑝 represents the true proportion of high school students at my high school who prefer
online instruction. The significance level is 𝛼 = 0.05.
If both the conditions below are met, we may proceed with a test of inference.
Conditions:
(1) The sample was randomly selected. See discussion in section 3.
(2) 𝒏𝒑 ≥ 𝟏𝟓, 𝒏(𝟏 − 𝒑) ≥ 𝟏𝟓, where n represents the sample size and 𝑝̂ represents the
proportion of students within the sample who preferred online instruction. It is necessary to
satisfy this condition as it allows us to assume the sampling distribution of 𝑝̂ be approximately
normal, allowing us to calculate a p-value. After calculation, we have 126 × = 42 successes,
and 126 × 1 − = 84 failures, both of which are above 15. Therefore, this condition is
satisfied.
7. Test Statistic:
𝑍 =
𝑝̂ − 𝑝
𝑝 𝑞 /𝑛
=
42
126
− 0.5
0.5 × 0.5/126
= −3.7417
p-value:
𝑝 = 𝑃(𝑍 < −3.7417) = 0.00009
Moving forward to the second question to examination of the association of preference
between the two methods of instruction, a chi-square test of independence with the following
hypotheses:
𝐻 : There is no association between preference of online instruction and grade level
𝐻 : There is an association between preference of online instruction and grade level
The significance level is set to be 0.05.
Conditions
(1) The sample was randomly selected. See discussion in section 3.
(2) Large Counts Condition. This is satisfied, as the expected counts are all greater all
than 5. All expected counts were rounded to two decimal places as summarized in the following
table.
Grade Level # of people who prefer
online instruction overall
# of people who prefer in-
person instruction overall
TOTAL
Freshman 6.33 12.67 19
Sophomore 19.33 38.67 58
Junior 8.33 16.67 25
Senior 8 16 24
TOTAL 42 84 126
Table 2: Expected Counts in the contingency table
8. Using both the values for the expected counts as well as the observed counts table from
before, a test statistic can be calculated.
Chi-Square Test Statistic
𝜒 =
(𝑜𝑏𝑠 − 𝑒𝑥𝑝)
𝑒𝑥𝑝
𝜒 =
(6 − 6.33)
6.33
+
(18 − 19.33)
19.33
+
(8 − 8.33)
8.33
+
(10 − 8)
8
+
(13 − 12.67)
12.67
+
(40 − 38.67)
38.67
+
(17 − 16.67)
16.67
+
(14 − 16)
16
= 0.9342
Degrees of Freedom: (4 − 1) × (2 − 1) = 3
p-value:
𝑝 = 𝑃 𝜒 > 0.9342 = 0.8172
5. Conclusion
The p-value for the first test of inference was p = 0.00009; since p = 0.00009 < 0.05, we
reject the null hypothesis. Therefore, there is convincing evidence that the true proportion of high
school students at my high school who prefer online instruction is less than 0.5, meaning that in
general, high school students at my high school prefer in-person instruction compared to online
instruction.
Furthermore, for our second test of association regarding the association between
preference of online instruction and grade level, the p-value given from the chi-square statistic
was p = 0.8172; since p = 0.8172 > 0.05, we fail to reject the null hypothesis. Therefore, there is
lack of convincing evidence that suggests that there is an association between preference of
online instruction and grade level, meaning that there is in fact no association at all between
preference of online instruction and grade level.
9. 6. Reflection
I felt that overall, the experiment process went well, as I had a relatively good idea about
how to conduct tests and explain my thought process throughout the experiment. However, I do
recognize that my study is not perfect and that there are some flaws.
I believe that the sampling method that I chose was the thing that could have caused the
most statistical error. I used an online survey, which is technically not a random sampling
method, but as stated before, with the sampling frame not available and the idea of conducting
in-person surveys out of the window, this was essentially the next best method of sampling. As a
result, it is undeniable that there is some amount of voluntary response and nonresponse bias
since people who wanted to voice their opinions were overrepresented while people who do not
check Schoology were underrepresented. If possible, I would ask the administration if they had a
list of all the student’s emails. Then, a more statistically valid sampling method would be to
randomly select a stratified sample with 130 student’s emails from the list and email them the
survey questions; the stratified variable would be the grade level. To do this, I would first
determine the sample size 𝑛 of each grade (𝑖 = 1, 2, 3, 4 for freshmen, sophomores, juniors, and
seniors respectively) based on the grade’s proportion to the total population of high school
students. Continuing forward, I would randomly assign a unique random number from 1 to
however many students there are for each grade. From there, the sample will be filled up with
students who have numbers 1 to 𝑛 in each grade. The problem with this method is that people
might feel less inclined to reply to a personal email with it being not anonymous, especially if
they are unfamiliar with who I am. It was possible that if I had used this sampling method that I
would not have had enough people respond to be able to answer the questions I had in mind. In
10. other words, nonresponse bias still exists. Nonetheless, the stratified sampling method is clearly
the more statistically valid approach, even if the risk of not enough people responding is present.
The conclusion that high school students preferred in-person instruction aligned with my
prediction. I was not particularly surprised by this, as I was constantly surrounded by friends
who complained about the benefits that they had from being able to interact in-person with
teachers were gone. Based on this, I believe that my high school should improve their current
online learning program, because it is possible that the virus may return and force us to go
through online learning again. As for the second chi-square test, I was surprised that there was no
association between preference for online instruction and grade level. I was surprised because I
thought that upperclassmen would enjoy online learning more as this relieves the stress on their
hardest years before college applications. On the other hand, the lack of association between
online learning across all grade levels means that upperclassmen are still passionate about
learning in-person, in speaks a lot of our community. All in all, I was pleasantly satisfied with
the results of this study.