Machine Learning in Educational Sciences : Approaches, Applications and Advances

• Autore: Khine Myint Swe
• Pubblicazione: Singapore : , : Springer, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (389 pages)
• Altri autori: Khine
• Nota di contenuto: Intro -- Preface -- Contents -- Editor and Contributors -- Introduction -- Exploring the Potential of Machine Learning in Educational Research -- 1 Introduction -- 2 Foundations of Machine Learning -- 3 Predicting Student Performance -- 4 Machine Learning in Assessment Processes -- 5 Machine Learning in Educational Research -- 6 Conclusion -- References -- Foundations of Machine Learning -- An Introduction to Machine Learning for Educational Researchers -- 1 Defining Terms -- 2 Steps to Conduct ML -- 3 Evaluating Model Fit -- 4 Applied Example -- 5 Future Research -- References -- Machine Learning Applications in Higher Education Services: Perspectives of Student Academic Performance -- 1 Introduction -- 2 Background of the Research -- 3 Research Methodology -- 4 ML Application Trend in HE -- 5 Findings -- 6 Conclusion -- References -- Camelot: A Council of Machine Learning Strategies to Enhance Teaching -- 1 Introduction -- 2 Machine Learning Models: Theory -- 2.1 Supervised Model: Na¨ıve Bayes Classifier -- 2.2 Supervised Model: K Nearest Neighbors -- 2.3 Supervised Model: Support Vector Machines -- 2.4 Supervised Model: Logistic Regression -- 2.5 Supervised Model: Decision Trees -- 2.6 Supervised Model: Neural Networks -- 3 The Camelot Framework -- 3.1 Phase-1: Data Preprocessing -- 3.2 Phase-2: Model Training -- 3.3 Phase-3: Model Testing and Inference -- 4 Conclusion -- Appendix A -- References -- Penalized Regression in Large-Scale Data Analysis -- 1 Introduction -- 2 Predictive Modeling -- 3 Basics of Penalized Regression -- 3.1 OLS Regression -- 3.2 Penalized Regression Using Convex Penalty Functions -- 4 Model Assessment -- 4.1 Cross-Validation -- 4.2 Akaike Information Criterion and Bayesian Information Criterion -- 4.3 Prediction Errors -- 4.4 Selection Counts -- 5 Extensions of Penalized Regression -- 5.1 Group Penalized Regression. ; 5.2 Penalized Regression Using Concave Penalty Functions -- 5.3 GlmmLasso -- 5.4 Post-selection Inference (PSI) -- 6 Concluding Remarks -- 7 Coding Examples in R -- 7.1 LASSO and Enet Using glmnet() -- 7.2 MCP and Mnet Using grpreg() -- References -- Predicting Student Performance -- Schools Students Performance with Artificial Intelligence Machine Learning: Features Taxonomy, Methods and Evaluation -- 1 Introduction -- 2 Taxonomy of Students' Performance Factors -- 3 Related Work -- 4 Students Performance Features Exploration -- 5 Machine Learning Models for Students Performance Prediction -- 5.1 Support Vector Machine -- 5.2 Random Forest -- 6 Performance Analysis -- 6.1 Experimental Environment -- 6.2 Experiments -- 6.3 Experimental Results Analysis -- 6.4 Study of Data Without Previous Grades -- 7 Conclusions -- References -- Predicting Response Latencies on Test Questions Based on Features of the Questions -- 1 Introduction -- 2 Method -- 3 Data Source -- 3.1 Response Time -- 3.2 Cognitive Complexity of an Item -- 3.3 Linguistic Features -- 3.4 Question Design -- 4 Correlation Analysis -- 5 Traditional Regression Analysis -- 6 Machine Learning Methods -- 7 Results -- 8 Conclusions and Discussion -- References -- Predicting Student Attrition in University Courses -- 1 Addressing Student Drop-Out in Universities: Understanding the Underlying Causes and Implementing Multifaceted Solutions -- 2 How Can Machine Learning Help in this Dropout Problem for Both the Universities and the Students? -- 3 Categories of Factors that Influence Dropout -- 4 Models with Time-Invariant and Time-Variant Predictors -- 5 Models at Different Levels -- 6 Specialties of Educational Machine Learning Models -- 7 The Predictive Power of Models -- 8 Model Building Actors, Competences, and Exploitation of Results -- 9 Model Building. ; 9.1 Embedded Machine Learning Models in an LMS -- 9.2 Levels of Indicators -- 9.3 Training-Prediction Schemes -- 9.4 Predictor Matrices and Target Vectors in the Different Schemes -- 9.5 Searching for the Optimal Model -- 10 Supervised Learning Algorithms -- 11 Performance Metrics and Their Use for Checking Model Bias and Variance -- 12 The Use of Performance Metrics in Model Training, Validation, and Testing Phase -- 13 Learning Curve of the Model -- 14 Conclusion -- References -- Improving Students' Achievement Prediction in Blended Learning Environments with Integrated Machine Learning Methods -- 1 Introduction -- 2 Data Collection -- 3 Research Method and Results -- 3.1 Data Preprocessing and Model Debugging -- 3.2 Semantic Completeness Analysis -- 3.3 Semantic Matching Degree Analysis -- 3.4 Recognize Student Learning Pattern -- 3.5 Prediction Results Feedback to the Instruction Process -- 4 Conclusion -- 5 Limitations -- Appendix A -- References -- Enhancing Predictive Performance in Identifying At-Risk Students: Integration of Topological Features, Node Embeddings in Machine Learning Models -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Graph Representation -- 3.2 Topological Features -- 3.3 Node Embeddings -- 3.4 Feature Selection -- 3.5 Model Training and Evaluation -- 4 Results and Discussions -- 5 Conclusion -- References -- Machine Learning in Assessment Processes -- Applying Topic Modeling to Understand Assessment Practices of U.S. College Instructors in Response to the COVID-19 Pandemic -- 1 Introduction -- 1.1 Assessment Amidst Emergency Remote Teaching During the Pandemic -- 2 Research Aims -- 3 Methods -- 3.1 Participants -- 3.2 Data Collection Procedure -- 3.3 Analytic Procedure -- 4 Results -- 4.1 Perceived Changes to Assessment Practices -- 4.2 Challenges Administering Assessments Online -- 5 Discussion. ; 5.1 Implications and Recommendations for Practice -- 5.2 Limitations -- 5.3 Conclusion -- Supplemental Materials -- Understanding the Context of Teaching and Assessment -- Effects of the Pandemic on Instructional Practices -- Pre-pandemic Professional Development -- References -- Applying Machine Learning to Augment the Design and Assessment of Immersive Learning Experience -- 1 Introduction -- 2 Machine Learning Approaches in Educational Sciences Research -- 2.1 What is Machine Learning? -- 2.2 Application of Machine Learning in Educational Sciences -- 3 The Design and Assessment of Immersive Learning Experience -- 3.1 Immersive Learning experience-How Learning Occurs? -- 3.2 Augmenting the Design of Immersive Learning Experience with Machine Learning -- 3.3 Natural Language Processing and Conversational Artificial Intelligence -- 3.4 Assessment for Immersive Learning Experience -- 4 Discussion and Future Directions -- 5 Conclusion -- References -- Machine Learning in Educational Research -- Machine Learning for Analyzing the Relationship Between Well-Being, Academic Performance with Large-Scale Assessment Data -- 1 Purposes -- 2 Theoretical Framework -- 3 Background of Well-Being in the Educational Context -- 3.1 Global Education Measurement -- 3.2 Well-Being and Academic Achievement -- 3.3 A Holistic Approach to Well-Being -- 3.4 Student Well-Being in the Context of Education 4.0 -- 4 A Machine Learning Approach to Big Data -- 5 Method -- 5.1 Participants -- 5.2 Items of Concrete Scenarios as Independent Variables -- 5.3 Survey Items of Momentary Feeling -- 5.4 Plausible Value as Dependent Variable -- 5.5 Big Data Analytics -- 6 Results -- 6.1 Best Model: Boosting -- 6.2 Math Scores: Boosting Results -- 6.3 Science Scores: Boosting Results -- 6.4 Data Visualization: Median Smoothing -- 7 Discussion of Well-Being and Academic Performance. ; 7.1 Peer Engagement and Academic Success -- 7.2 Negative Emotion -- 7.3 Limitations and Future Directions -- 8 Discussion of Machine Learning and Educational Research -- Appendix A -- References -- Using Large Language Models to Probe Cognitive Constructs, Augment Data, and Design Instructional Materials -- 1 Motivation -- 2 Using AI to Assess Physics Problem-Solving -- 3 Research Questions -- 4 Method -- 4.1 ChatGPT as a Resource -- 4.2 A Textbook-Style Physics Problem -- 5 Findings -- 5.1 RQ1-ChatGPT's Problem-Solving -- 5.2 RQ2-Prompting ChatGPT to Use Different Strategies -- 5.3 RQ3-Utilizing ChatGPT to Generate Instructional Materials -- 6 Discussion -- Appendices -- Appendix 1: Evaluation of the Solution -- Appendix 2: Chat History on the Means-Ends Strategy -- Appendix 3: Chat History on the "Working Backwards " Strategy -- Appendix 4a: Chat History on the "Plug and Chug"-Strategy -- Appendix 4b: Chat History on the "Plug and Chug"-Strategy -- References -- Machine Learning Applications for Early and Real-Time Warning Systems in Education -- 1 Early Beginnings -- 2 Theoretical Underpinnings of the Scientific Framework for Predictive Analysis in Education -- 2.1 Model-Dependent Realism -- 2.2 Information Theory Perspective -- 3 Conceptual Bases for Developing Neural Networks. -- 3.1 Structure Neural Network (SNN) -- 3.2 Theoretical Framework of Academic Performance -- 4 A Systematic Procedure -- 5 Results -- 6 Discussion -- References -- Text Identification for Questions Generation According to Bloom's Taxonomy Using Natural Language Processing -- 1 Introduction -- 2 Automatic Generation of Concept Map -- 3 Natural Language Processing (NLP) for Automated Concept Map Generation -- 4 Experiment and Methodology -- 4.1 Framing of Questions Based on Bloom's Taxonomy -- 4.2 Mapping of Course Learning Outcomes with Bloom's Levels. ; 4.3 Significance of Course Expert for Framing Questions.
• Titolo autorizzato: Machine Learning in Educational Sciences
• ISBN: 981-9993-79-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910841857103321
• Lo trovi qui: Univ. Federico II