This is a proposal of Research Topic ( Student performance prediction) . DUET CSE 15 Batch.
http://www.duet.ac.bd/department/department-of-computer-science-engineering/
Many students failed an introductory Java programming course. A study developed dashboards displaying students' online activity and predicted performance to provide weekly feedback. Students receiving dashboards completed more online tasks but did not have significantly higher pass rates or exam scores compared to the control group. The aim was to increase course success through personalized feedback on e-learning progress.
Predicting students performance in final examinationRashid Ansari
The document discusses predicting student performance in final examinations. It examines using linear regression and multilayer perceptron algorithms on attributes of student postings in discussion forums and attendance scores. The case study involved 50 students, and the multilayer perceptron model produced slightly more accurate results based on correlation coefficients and error rates. Specifically, the multilayer perceptron model had a higher correlation coefficient of 0.84 compared to 0.82 for linear regression, and lower mean absolute and root mean squared errors.
In this study, the effect of combining variables from the different data sources for student academic performance prediction was examined using three state-of-the–art classifiers: Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The study examined the use of heterogeneous multi-model ensemble techniques to predict student academic performance based on the combination of these classifiers and three different data sources. A quantitative approach was used to develop the various base classifier models while the ensemble models were developed using stacked generalisation ensemble method in order to overcome the individual weaknesses of the different models. Variables were extracted from the institution’s Student Record System and Learning Management System (Moodle) and from a structured student questionnaire. At present, negligible work has been done using this integrated approach and ensemble techniques especially with aggregated learner data in performance prediction in HE. The empirical results obtained show that the ensemble models.........................
Role of education is very critical for the development of any country. So it is the responsibility of each and every person to do something for the betterment of education. Taking this fact into consideration we start working on the education system. Education system ranging from basic to higher education. Now a day education system generates a lots of data related to student. If we cannot analyze that data properly then that data is useless. With the help of data mining techniques we can find the hidden information from the data collected for the different educational setting. With the help of that information we can review our educational process or make improvement in our education system. Here in this article we are considering a case of an engineering college student and try to predict the final result in advance. The result of the prediction provides timely help to those students who are on risk of failure in the final examination. There are different techniques of data mining are available and we are using J48, RandomForest, and ADTree to predict the performance of the student in their final examination. On the basis of this predication we can make a decision whether the student will be promoted to next year or not. We the help of the result we can improve the performance of the student who are on risk of fail or promoted. After the declaration of the final result of the student, result is fed into the system and hence the result will analysed for the next semester. The comparative result shows that, prediction help in the improvement of overall result of the weaker students.
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.
A Nobel Approach On Educational Data Miningijircee
This document discusses educational data mining and its applications. It begins with introducing data mining and its goal of extracting useful information from large databases. Educational data mining is then discussed as using data mining techniques to understand how students learn. The objectives of educational data mining are outlined as supporting educational research, effective learning, prediction, and feedback. Common data mining techniques discussed include summarization, cluster analysis, classification and prediction, decision trees, and association. The document concludes with how these techniques can be applied in education for knowledge discovery and improving student success.
The Future of Online Testing with MOOCs: An Exploratory Analysis of Current P...Eamon Costello
The document summarizes a study that analyzed the quality of multiple choice questions (MCQs) used in online tests in Massive Open Online Courses (MOOCs). The study found that 14.78% of the 115 MCQs analyzed across 12 MOOCs and platforms contained defined item writing flaws. Common issues included having more than one correct answer, the longest answer being the correct one, and flaws in question structure. The study concludes that the validity and reliability of online testing in MOOCs could be improved by avoiding these flawed question structures and following best practices for writing high-quality MCQs.
This is a proposal of Research Topic ( Student performance prediction) . DUET CSE 15 Batch.
http://www.duet.ac.bd/department/department-of-computer-science-engineering/
Many students failed an introductory Java programming course. A study developed dashboards displaying students' online activity and predicted performance to provide weekly feedback. Students receiving dashboards completed more online tasks but did not have significantly higher pass rates or exam scores compared to the control group. The aim was to increase course success through personalized feedback on e-learning progress.
Predicting students performance in final examinationRashid Ansari
The document discusses predicting student performance in final examinations. It examines using linear regression and multilayer perceptron algorithms on attributes of student postings in discussion forums and attendance scores. The case study involved 50 students, and the multilayer perceptron model produced slightly more accurate results based on correlation coefficients and error rates. Specifically, the multilayer perceptron model had a higher correlation coefficient of 0.84 compared to 0.82 for linear regression, and lower mean absolute and root mean squared errors.
In this study, the effect of combining variables from the different data sources for student academic performance prediction was examined using three state-of-the–art classifiers: Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The study examined the use of heterogeneous multi-model ensemble techniques to predict student academic performance based on the combination of these classifiers and three different data sources. A quantitative approach was used to develop the various base classifier models while the ensemble models were developed using stacked generalisation ensemble method in order to overcome the individual weaknesses of the different models. Variables were extracted from the institution’s Student Record System and Learning Management System (Moodle) and from a structured student questionnaire. At present, negligible work has been done using this integrated approach and ensemble techniques especially with aggregated learner data in performance prediction in HE. The empirical results obtained show that the ensemble models.........................
Role of education is very critical for the development of any country. So it is the responsibility of each and every person to do something for the betterment of education. Taking this fact into consideration we start working on the education system. Education system ranging from basic to higher education. Now a day education system generates a lots of data related to student. If we cannot analyze that data properly then that data is useless. With the help of data mining techniques we can find the hidden information from the data collected for the different educational setting. With the help of that information we can review our educational process or make improvement in our education system. Here in this article we are considering a case of an engineering college student and try to predict the final result in advance. The result of the prediction provides timely help to those students who are on risk of failure in the final examination. There are different techniques of data mining are available and we are using J48, RandomForest, and ADTree to predict the performance of the student in their final examination. On the basis of this predication we can make a decision whether the student will be promoted to next year or not. We the help of the result we can improve the performance of the student who are on risk of fail or promoted. After the declaration of the final result of the student, result is fed into the system and hence the result will analysed for the next semester. The comparative result shows that, prediction help in the improvement of overall result of the weaker students.
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.
A Nobel Approach On Educational Data Miningijircee
This document discusses educational data mining and its applications. It begins with introducing data mining and its goal of extracting useful information from large databases. Educational data mining is then discussed as using data mining techniques to understand how students learn. The objectives of educational data mining are outlined as supporting educational research, effective learning, prediction, and feedback. Common data mining techniques discussed include summarization, cluster analysis, classification and prediction, decision trees, and association. The document concludes with how these techniques can be applied in education for knowledge discovery and improving student success.
The Future of Online Testing with MOOCs: An Exploratory Analysis of Current P...Eamon Costello
The document summarizes a study that analyzed the quality of multiple choice questions (MCQs) used in online tests in Massive Open Online Courses (MOOCs). The study found that 14.78% of the 115 MCQs analyzed across 12 MOOCs and platforms contained defined item writing flaws. Common issues included having more than one correct answer, the longest answer being the correct one, and flaws in question structure. The study concludes that the validity and reliability of online testing in MOOCs could be improved by avoiding these flawed question structures and following best practices for writing high-quality MCQs.
Predictive models are quasi experimental structures used to determine the future
patterns in data. These meaningful data patterns form the building block of any
decision support system. Researchers all over the world have built many prediction
models for major industries. Research works in the educational sector has increased
steeply. This steep increase may be due to the high availability of data in the
educational domain. This survey tries to comprehend a few literary works on
academic performance prediction of engineering students with the focus on grade
predictions. Meaningful interpretations have been made and inferences are presented
at the end of this paper
An insight into Educational Data Mining at Muğla Sıtkı Koçman University, Turkeystrehlst
This presentation discusses educational data mining research conducted at Muğla Sıtkı Koçman University to analyze student performance and develop models to predict success or failure. The research team analyzed data from university systems to build decision tree models relating factors like family income, English language preparation, and registration preferences to GPA. The models aimed to help identify factors influencing student outcomes and guide improvements. Issues addressed included limited data availability and usability of results for practitioners.
Data mining to predict academic performance. Ranjith Gowda
This document proposes using data warehousing and data mining techniques to predict student academic performance in schools. It describes collecting student data like scores, attendance, discipline, and assignments into a data warehouse. Data mining methods are then used to analyze the student data and identify relationships between variables to predict performance, such as whether students are progressing, being retained, or conditionally progressing. The results could help schools identify students at risk of failing and take actions to help them succeed.
What questions are MOOCs asking? An evidence based investigationEamon Costello
This document summarizes a presentation on analyzing the quality of multiple choice questions (MCQs) used in Massive Open Online Courses (MOOCs). The presentation:
1) Used a diagnostic tool to systematically analyze 204 MCQs from 18 MOOC courses and found at least one item writing flaw in over half of the questions. The most common flaws were using absolute terms, true/false questions, and negatively worded stems.
2) Found that the longest response option was most likely to be correct, suggesting logical flaws.
3) Suggested peer reviewing questions before students take tests, conducting post-facto item analysis, and developing better MCQ authoring tools to improve quality in M
Land of The Learning Giants: The Rise of MOOCsEamon Costello
Massive Open Online Courses (MOOCs) have been heralded and decried in something of equal measure over the last four years. Their ultimate purpose and the effect they are having are still uncertain but given the level of maturity that has now reached we ought now to be able to attempt to answer some questions of this phenomenon. Following an overview of key issues for educational research on the topic of MOOCs this paper presents findings from studies we have conducted into
* Representations of MOOCs in the Irish Print Media: What are the narratives, who is telling it and why?
* Quality of education in MOOCs in particular regarding online testing
* The strategic drivers for higher education institutions in Ireland to develop MOOCs
20080223 Lasvegas Conference PresentationJong-Ki Lee
The document proposes a model for factors influencing e-learner performance in an online learning environment. It suggests that e-learner satisfaction with the learning management system (LMS), use of self-regulated learning strategies, and self-regulatory efficacy positively influence expected performance. It also hypothesizes that empathy positively affects self-regulatory efficacy. An empirical study was conducted to test this model using surveys of 341 students in online courses in South Korea. Preliminary analysis found support for all hypotheses. The proposed model contributes to understanding factors influencing online learning success from both theoretical and practical perspectives.
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.
Exploring Motivational Aspects and User Experience of Mobile Mathematics Lea...Mindtrek
1. The study explored the motivational aspects and user experience of South African students using a mobile mathematics learning service called Microsoft Math.
2. Surveys and log data from 53 students found that interest in their future, liking mathematics, and improving math skills motivated use. High-quality content and improving skills motivated using the service.
3. While user experience of the service was positive, correlations between experience and motivation were minor. Novice and expert users differed in external motivation, and math ability linked to interest.
Technology Integration in Mathematics Instruction in Urban Public SchoolsPhyllis Harvey-Buschel
This study examined factors that impact technology integration in urban public secondary mathematics classrooms. The researcher conducted a quantitative study using an existing dataset from 3654 urban public secondary mathematics teachers. The results showed that while access to technology in classrooms correlated with greater technology integration, participation in professional development had the strongest relationship. The study concluded that for effective technology integration, schools need to provide both access to technology and ongoing professional development for teachers on using technology to meet curricular goals.
Technology in Mathematics and Science IDT285psych369
Technology can enhance mathematics and science education in several ways. Spreadsheets, graphing calculators, and interactive geometry software give students hands-on experience solving problems. Reasoning and skill-building software help increase sub-skills while developing logic and comprehension. Digital tools like simulations and imaging allow experiments to be observed more easily. Communication between students and teachers is improved through tools like interactive whiteboards, class websites, and email. Various instructional software, simulations, and online resources provide interactive learning experiences across math and science topics.
The document proposes a new framework called Quasi Framework to detect disengagement in online learning. It analyzes log file data from an online learning system to identify attributes related to disengagement. The framework merges log file information with student database information and uses it to predict disengagement. Experimental results on a real student dataset show the Quasi Framework achieves higher accuracy than an existing system called iHelp, particularly for predicting disengaged students. The study suggests considering both reading and assessment attributes are important for accurate disengagement detection.
This document describes a Computer Aided Testing System (CATS) designed to provide insight into students' reasoning patterns. CATS administers online tests and tracks students' responses, including response times and notes made on questions. It aims to emulate paper test-taking strategies. Test questions are randomly selected from pools of various difficulty levels. Student and teacher reports link performance to patterns in students' reasoning to support reflection and improve instruction.
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
Educational Data Mining (EDM) is one of the crucial application areas of data mining which helps in predicting educational dropout and hence provides timely help to students. In Indian context, predicting educational dropouts is a major problem. By implementing EDM, we can predict the learning habits of the student. At present EDM has not been introduced at higher education level. Due to this we cannot recognize the genuine problems of students during their education. The objective of this analysis is to find the existing gaps in predicting educational dropout and find the missing attributes if any, which my further contribute for better prediction. After that we try to find the best attributes and DM techniques which are frequently used for dropout prediction. Based on the combination of missing attribute and best attribute of student data thus far, a new algorithm can be tested which may overcome the shortcomings of previous work done.
- Learning analytics is concerned with measuring, collecting, analyzing, and reporting data about learners and learning environments in order to understand and optimize the learning process.
- It is an interdisciplinary field that draws from computer science, learning sciences, educational data mining, and other areas.
- Early work in learning analytics included using social network analysis and visualization tools to provide insights into learning processes from online data. Current areas of focus include predictive analytics, formative assessment, and generating real-time feedback for students.
- Challenges remain in developing tools that can generate meaningful feedback for students to support learning while balancing different methodologies and theories across the field.
A reflection on where we are with learning analytics as a new multi-discipline research area. Reflections from the Learning Analytics Conference 2013 with respect to Assessment.
Speakers:
David Lewis, senior analytics consultant, Jisc
Martin Lynch, learning systems manager, University of South Wales
An opportunity to find out about how an institution has been implementing learning analytics to support the student journey with and opportunity to discuss issues and possibilities that the use of learning analytics may create.
Effect of Makerspace Professional Development Activities on Elementary & Midd...STEAM Learning Lab
1. The study explored the effect of a professional development program incorporating STEM makerspace activities on educators' perceptions of STEM and technology integration.
2. Survey results showed that participation increased educators' confidence in using emerging technologies for student learning and improved attitudes toward STEM subjects like math and science.
3. While attitudes toward technology integration increased overall, statistical significance was not achieved. Future research is needed to further study the impact of makerspace environments on educators' adoption of instructional technologies.
This document summarizes a study on designing and delivering a continuing professional development (CPD) course on mathematics for A-level Biology. It involved biology teachers in designing the course content on exponentials/logarithms and statistics. The day was delivered to 20-30 teachers. Data was collected on the impact on teachers' math confidence and teaching practice. Preliminary findings showed the design process improved math teaching confidence and the day further increased statistics confidence while providing pedagogical support ideas. Challenges included time away from school and differing research/practice cultures.
Jaime McQueen Virtual lab SERA presentation Jaime McQueen
The document summarizes a proposed study on the effect of virtual laboratory investigations on student achievement in biology. The study would use a mixed methods concurrent triangulation design with quantitative and qualitative components. Quantitatively, it would compare test scores of students using virtual labs in face-to-face, blended, and online course formats. Qualitatively, it would examine how student technology attitudes affect perceptions of virtual lab efficacy via surveys and focus groups. The goal is to explore how college students learn and construct knowledge using virtual biology labs to inform higher education practices. The work is still in progress and the methodology may be modified based on committee feedback.
An Expert System For Improving Web-Based Problem-Solving Ability Of StudentsJennifer Roman
The document describes an expert system developed to improve students' web-based problem solving abilities. It analyzes the online problem solving behaviors of teachers to build the knowledge base. Quantitative indicators are used to describe teachers' web searching behaviors, which are then categorized and analyzed using factor analysis. Experimental results showed the expert system was able to provide accurate suggestions to students for improving their problem solving skills.
Predictive models are quasi experimental structures used to determine the future
patterns in data. These meaningful data patterns form the building block of any
decision support system. Researchers all over the world have built many prediction
models for major industries. Research works in the educational sector has increased
steeply. This steep increase may be due to the high availability of data in the
educational domain. This survey tries to comprehend a few literary works on
academic performance prediction of engineering students with the focus on grade
predictions. Meaningful interpretations have been made and inferences are presented
at the end of this paper
An insight into Educational Data Mining at Muğla Sıtkı Koçman University, Turkeystrehlst
This presentation discusses educational data mining research conducted at Muğla Sıtkı Koçman University to analyze student performance and develop models to predict success or failure. The research team analyzed data from university systems to build decision tree models relating factors like family income, English language preparation, and registration preferences to GPA. The models aimed to help identify factors influencing student outcomes and guide improvements. Issues addressed included limited data availability and usability of results for practitioners.
Data mining to predict academic performance. Ranjith Gowda
This document proposes using data warehousing and data mining techniques to predict student academic performance in schools. It describes collecting student data like scores, attendance, discipline, and assignments into a data warehouse. Data mining methods are then used to analyze the student data and identify relationships between variables to predict performance, such as whether students are progressing, being retained, or conditionally progressing. The results could help schools identify students at risk of failing and take actions to help them succeed.
What questions are MOOCs asking? An evidence based investigationEamon Costello
This document summarizes a presentation on analyzing the quality of multiple choice questions (MCQs) used in Massive Open Online Courses (MOOCs). The presentation:
1) Used a diagnostic tool to systematically analyze 204 MCQs from 18 MOOC courses and found at least one item writing flaw in over half of the questions. The most common flaws were using absolute terms, true/false questions, and negatively worded stems.
2) Found that the longest response option was most likely to be correct, suggesting logical flaws.
3) Suggested peer reviewing questions before students take tests, conducting post-facto item analysis, and developing better MCQ authoring tools to improve quality in M
Land of The Learning Giants: The Rise of MOOCsEamon Costello
Massive Open Online Courses (MOOCs) have been heralded and decried in something of equal measure over the last four years. Their ultimate purpose and the effect they are having are still uncertain but given the level of maturity that has now reached we ought now to be able to attempt to answer some questions of this phenomenon. Following an overview of key issues for educational research on the topic of MOOCs this paper presents findings from studies we have conducted into
* Representations of MOOCs in the Irish Print Media: What are the narratives, who is telling it and why?
* Quality of education in MOOCs in particular regarding online testing
* The strategic drivers for higher education institutions in Ireland to develop MOOCs
20080223 Lasvegas Conference PresentationJong-Ki Lee
The document proposes a model for factors influencing e-learner performance in an online learning environment. It suggests that e-learner satisfaction with the learning management system (LMS), use of self-regulated learning strategies, and self-regulatory efficacy positively influence expected performance. It also hypothesizes that empathy positively affects self-regulatory efficacy. An empirical study was conducted to test this model using surveys of 341 students in online courses in South Korea. Preliminary analysis found support for all hypotheses. The proposed model contributes to understanding factors influencing online learning success from both theoretical and practical perspectives.
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.
Exploring Motivational Aspects and User Experience of Mobile Mathematics Lea...Mindtrek
1. The study explored the motivational aspects and user experience of South African students using a mobile mathematics learning service called Microsoft Math.
2. Surveys and log data from 53 students found that interest in their future, liking mathematics, and improving math skills motivated use. High-quality content and improving skills motivated using the service.
3. While user experience of the service was positive, correlations between experience and motivation were minor. Novice and expert users differed in external motivation, and math ability linked to interest.
Technology Integration in Mathematics Instruction in Urban Public SchoolsPhyllis Harvey-Buschel
This study examined factors that impact technology integration in urban public secondary mathematics classrooms. The researcher conducted a quantitative study using an existing dataset from 3654 urban public secondary mathematics teachers. The results showed that while access to technology in classrooms correlated with greater technology integration, participation in professional development had the strongest relationship. The study concluded that for effective technology integration, schools need to provide both access to technology and ongoing professional development for teachers on using technology to meet curricular goals.
Technology in Mathematics and Science IDT285psych369
Technology can enhance mathematics and science education in several ways. Spreadsheets, graphing calculators, and interactive geometry software give students hands-on experience solving problems. Reasoning and skill-building software help increase sub-skills while developing logic and comprehension. Digital tools like simulations and imaging allow experiments to be observed more easily. Communication between students and teachers is improved through tools like interactive whiteboards, class websites, and email. Various instructional software, simulations, and online resources provide interactive learning experiences across math and science topics.
The document proposes a new framework called Quasi Framework to detect disengagement in online learning. It analyzes log file data from an online learning system to identify attributes related to disengagement. The framework merges log file information with student database information and uses it to predict disengagement. Experimental results on a real student dataset show the Quasi Framework achieves higher accuracy than an existing system called iHelp, particularly for predicting disengaged students. The study suggests considering both reading and assessment attributes are important for accurate disengagement detection.
This document describes a Computer Aided Testing System (CATS) designed to provide insight into students' reasoning patterns. CATS administers online tests and tracks students' responses, including response times and notes made on questions. It aims to emulate paper test-taking strategies. Test questions are randomly selected from pools of various difficulty levels. Student and teacher reports link performance to patterns in students' reasoning to support reflection and improve instruction.
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
Educational Data Mining (EDM) is one of the crucial application areas of data mining which helps in predicting educational dropout and hence provides timely help to students. In Indian context, predicting educational dropouts is a major problem. By implementing EDM, we can predict the learning habits of the student. At present EDM has not been introduced at higher education level. Due to this we cannot recognize the genuine problems of students during their education. The objective of this analysis is to find the existing gaps in predicting educational dropout and find the missing attributes if any, which my further contribute for better prediction. After that we try to find the best attributes and DM techniques which are frequently used for dropout prediction. Based on the combination of missing attribute and best attribute of student data thus far, a new algorithm can be tested which may overcome the shortcomings of previous work done.
- Learning analytics is concerned with measuring, collecting, analyzing, and reporting data about learners and learning environments in order to understand and optimize the learning process.
- It is an interdisciplinary field that draws from computer science, learning sciences, educational data mining, and other areas.
- Early work in learning analytics included using social network analysis and visualization tools to provide insights into learning processes from online data. Current areas of focus include predictive analytics, formative assessment, and generating real-time feedback for students.
- Challenges remain in developing tools that can generate meaningful feedback for students to support learning while balancing different methodologies and theories across the field.
A reflection on where we are with learning analytics as a new multi-discipline research area. Reflections from the Learning Analytics Conference 2013 with respect to Assessment.
Speakers:
David Lewis, senior analytics consultant, Jisc
Martin Lynch, learning systems manager, University of South Wales
An opportunity to find out about how an institution has been implementing learning analytics to support the student journey with and opportunity to discuss issues and possibilities that the use of learning analytics may create.
Effect of Makerspace Professional Development Activities on Elementary & Midd...STEAM Learning Lab
1. The study explored the effect of a professional development program incorporating STEM makerspace activities on educators' perceptions of STEM and technology integration.
2. Survey results showed that participation increased educators' confidence in using emerging technologies for student learning and improved attitudes toward STEM subjects like math and science.
3. While attitudes toward technology integration increased overall, statistical significance was not achieved. Future research is needed to further study the impact of makerspace environments on educators' adoption of instructional technologies.
This document summarizes a study on designing and delivering a continuing professional development (CPD) course on mathematics for A-level Biology. It involved biology teachers in designing the course content on exponentials/logarithms and statistics. The day was delivered to 20-30 teachers. Data was collected on the impact on teachers' math confidence and teaching practice. Preliminary findings showed the design process improved math teaching confidence and the day further increased statistics confidence while providing pedagogical support ideas. Challenges included time away from school and differing research/practice cultures.
Jaime McQueen Virtual lab SERA presentation Jaime McQueen
The document summarizes a proposed study on the effect of virtual laboratory investigations on student achievement in biology. The study would use a mixed methods concurrent triangulation design with quantitative and qualitative components. Quantitatively, it would compare test scores of students using virtual labs in face-to-face, blended, and online course formats. Qualitatively, it would examine how student technology attitudes affect perceptions of virtual lab efficacy via surveys and focus groups. The goal is to explore how college students learn and construct knowledge using virtual biology labs to inform higher education practices. The work is still in progress and the methodology may be modified based on committee feedback.
An Expert System For Improving Web-Based Problem-Solving Ability Of StudentsJennifer Roman
The document describes an expert system developed to improve students' web-based problem solving abilities. It analyzes the online problem solving behaviors of teachers to build the knowledge base. Quantitative indicators are used to describe teachers' web searching behaviors, which are then categorized and analyzed using factor analysis. Experimental results showed the expert system was able to provide accurate suggestions to students for improving their problem solving skills.
This annotated bibliography summarizes research on effective strategies for teaching elementary mathematics through technology. The author reviewed articles describing various technology tools that have been successfully used to enhance mathematics learning, such as Graph Club software, student response systems, and hands-on engineering programs. Two articles provided frameworks to help teachers select appropriate technologies based on their pedagogical and content goals. While the bibliography revealed many promising tools and strategies, the author notes that continued research is still needed due to the rapidly evolving nature of educational technology.
Students' Intention to Use Technology and E-learning probably Influenced by s...IRJET Journal
This document examines factors that influence students' intention to use technology and e-learning in Libyan higher education. It investigates the effects of computer-internet experience, computer self-efficacy, technology-internet quality, and attitudes toward use on intention to use technology and e-learning. It also examines potential differences based on gender and field of study. The document describes a study that distributed questionnaires to 217 students to test 14 hypotheses related to these factors and differences. The results found that computer-internet experience, computer self-efficacy, technology-internet quality, and attitudes toward use were all positively related to intention to use technology and e-learning. However, no significant differences were found based on gender or field of study.
This document summarizes a journal article that examines tutors' views on utilizing e-learning systems in architectural education. The study surveyed tutors from the architecture faculty at a university in Saudi Arabia. It found that many tutors had limited experience using online tools and a slightly better experience with communication tools. While tutors were against online design courses, a mix of traditional and online teaching could provide more student support. The study concluded that innovative tools and a strategy integrating professional training and education are needed. Further research should assess blended courses and develop new systems to overcome shortcomings and meet architectural education needs.
A Structural Equation Model To Analyse The Antecedents To Students Web-Based...Jill Brown
- The document describes a study that aimed to analyze factors affecting students' performance in web-based problem solving.
- The researchers developed a model with six factors (internet self-efficacy, task-technology fit, computer anxiety, technology readiness, web information seeking, intention) believed to influence students' web-based problem solving abilities.
- An experiment was conducted with 201 students who engaged in web-based problem solving activities. Structural equation modeling found that task-technology fit was a major determinant of students' intention to learn online and their web-based problem solving performance.
Role of digital gadgets in transformation of traditional learning to digital ...Dr. C.V. Suresh Babu
Indian Science Techno Festival ISTF-2021 (Virtual) organized by Raman Science & Technology Foundation, National Council of Teacher Scientist, India and APJ Abdul Kalam National Council of Young Scientist on 26-28 Feb 2021
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Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
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Chapter 3
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Online Homework- Seminar presentation
1. FACTORS INFLUENCING THE USE AND THE
ATTITUDE TOWARD MATHEMATICS ONLINE
HOMEWORK
IN SECONDARY SCHOOLS
Prepared by: Nour Al bilbeisi
noorbelbisi@gmail.com
3. Examine factors that influence
students’ attitude to use mathematic
online homework in secondary
schools.
LITERATURE REVIEW
4. Statement of the problem
TOPIC 59% of students who participated in PISA 2012 reported that they
often worry to do activities related to mathematics, 75% of Malaysian students
reported feeling worried (OCED, 2013). This results corresponds to lower
achievement.
Innovations in mathematics education are being explored which may offer
many advantages. Online homework (OHW), as a replacement for traditional
textbook homework, may offer a more effective alternative to help students
learn mathematics (Brewer, 2009).
RESEARCH PROBLEM The use of OHW is growing, largely based
on anecdotal reports of its utility. However, the research literature FAILS to
provide definite empirical evidence for or against the use of an online version of
homework (Carter, 2004; Hirsch & Weibel, 2003; Kodippili & Senaratne, 2008;
Zerr, 2007, Barnsley, 2014 ).
JUSTIFICATION The mixed results from existing research suggest that
more research is needed and MORE VARIABLES need to be examined when
considering the use of OHW (Davidson, 2004). Measurement of other outcome
variables that are known to be related to student grades may help clarify
whether online homework is truly superior for students’ learning .(William
&amanda, 2012).
6. 2. What is the relationship between the
attitude toward the use of online homework
and the actual usage of online homework for
Mathematics learning?
2. To examine the relationship between
attitude toward the use of online homework
and the actual usage of online homework for
Mathematics learning.
2. To examine the relationship between
attitude toward the use of online homework
and the actual usage of online homework for
Mathematics learning.
4. What are student’s recommendations on
improving the ways to use online homework?
4. To study student’s recommendations on
improving the ways to use online homework.
4. To study student’s recommendations on
improving the ways to use online homework.
3. Which factors (math anxiety, math self-efficacy,
performance expectancy, effort expectancy, and
attitude toward the use of online homework) best
predict students’ actual use of online homework
for Mathematics learning?
3. To find out which factors (math anxiety, math
self-efficacy, performance expectancy, effort
expectancy and attitude toward the use of online
homework) best predict the actual use of online
homework for Mathematics learning.
3. To find out which factors (math anxiety, math
self-efficacy, performance expectancy, effort
expectancy and attitude toward the use of online
homework) best predict the actual use of online
homework for Mathematics learning.
1. To examine the relationships between
students’ (math anxiety, math self-efficacy,
performance expectancy, effort expectancy)
and attitude toward the use of online
homework for Mathematics learning.
1. To examine the relationships between
students’ (math anxiety, math self-efficacy,
performance expectancy, effort expectancy)
and attitude toward the use of online
homework for Mathematics learning.
1. What is the relationship between:
•Students’ math anxiety and attitude toward the use of
online homework for Mathematics learning?
•Students’ math self-efficacy and attitude toward the use
of online homework for Mathematics learning?
•Students’ performance expectancy and attitude toward
the use of online homework for Mathematics learning?
•Students’ effort expectancy and attitude toward the use
of online homework for Mathematics learning?
Research objectives Research questions
7. The Unified Theory of Acceptance and Use of
Technology (UTAUT) developed by
Venkatesh, Morris, Davis, and Davis (2003).
Theoretical framework
The UTAUT provide some variables that
explain a student’s acceptance and use of
technology.
Figure 1: UTAUT model
Figure 2: MSEAQ
8. Conceptual framework
Attitude toward the
use of online
homework
(UTAUT)
Attitude toward the
use of online
homework
(UTAUT)
Actual usage of
online homework
(UTAUT)
Actual usage of
online homework
(UTAUT)
Performance
Expectancy
(UTAUT)
Performance
Expectancy
(UTAUT)
Effort
Expectancy
(UTAUT)
Effort
Expectancy
(UTAUT)
Mathematics
Self-efficacy
(MSEAQ)
Mathematics
Self-efficacy
(MSEAQ)
Mathematics anxiety
(MSEAQ)
Mathematics anxiety
(MSEAQ)
Figure 3: proposed model
14. Data Analysis
Research questions Data analysis
1. What is the relationship between:
•Students’ math anxiety and attitude toward the use of online homework for
Mathematics learning?
•Students’ math self-efficacy and attitude toward the use of online homework for
Mathematics learning?
•Students’ performance expectancy and attitude toward the use of online homework
for Mathematics learning?
•Students’ effort expectancy and attitude toward the use of online homework for
Mathematics learning?
Correlation
coefficient
2. What is the relationship between the attitude toward the use of online homework
and the actual usage of online homework for Mathematics learning?
Correlation
coefficient
3. Which factors (math anxiety, math self-efficacy, performance expectancy, effort
expectancy, and attitude toward the use of online homework) best predict students’
actual use of online homework for Mathematics learning?
Multi regression
4. What are student’s recommendations on improving the ways to use online
homework?
Thematic
analysis
15. ““You never stop learning. If you have aYou never stop learning. If you have a
teacher, you never stop being a student”teacher, you never stop being a student”
ELIZABETH ROHMELIZABETH ROHM