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This dissertation examines three educational data mining tasks: learner behavior analysis, essay structure analysis and feedback generation, and dropout prediction. One study uses latent semantic analysis and machine learning to relate MOOC forum participation trajectories to performance. Another builds an argumentative structure feedback tool using deep language models to support automatic essay scoring. A third employs hidden Markov models and machine learning on forum states to predict dropout and assess learner status, advancing research on improving learning experiences.","University of South Carolina  \nScholar Commons  \nTheses and Dissertations  \nSpring 2023  \nLearning Analytics Through Machine Learning and Natural Language Processing  \nBokai Yang  \nFollow this and additional works at: [https://scholarcommons.sc.edu/etd](https://scholarcommons.sc.edu/etd)  \n Part of the Computer Sciences Commons  \nRecommended Citation  \nYang, B. (2023) . Learning Analytics Through Machine Learning and Natural Language Processing.(Doctoral dissertation) . Retrieved from [https://scholarcommons.sc.edu/etd/7291](https://scholarcommons.sc.edu/etd/7291)  \n[This Open Access Dissertation is brought to you by Scholar Commons. It has been accepted for inclusion in](This Open Access Dissertation is brought to you by Scholar Commons. It has been accepted for inclusion in)[ ](This Open Access Dissertation is brought to you by Scholar Commons. It has been accepted for inclusion in)[Theses and Dissertations by an authorized administrator of Scholar Commons. For more information](Theses and Dissertations by an authorized administrator of Scholar Commons. For more information), please [contact digres@mailbox.sc.edu](contact digres@mailbox.sc.edu).  \nLEARNING ANALYTICS THROUGH MACHINE LEARNING AND NATURAL  \nLANGUAGE PROCESSING  \nby  \nBokai Yang  \nMaster of Science  \nUniversity of Florida, 2018  \nBachelor of Engineering  \nUniversity of Electronic Science and Technology of China, 2016  \nSubmitted in Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy in Computer Science College of Engineering & Computing University of South Carolina  \n2023  \nAccepted by:  \nJohn R. Rose, Major Professor  \nMarco Valtorta, Committee Member  \nCsilla Farkas, Committee Member  \nJijun Tang, Committee Member  \nHengtao Tang, Committee Member  \nCheryl L. Addy, Interim Vice Provost and Dean of the Graduate School  \n© Copyright by Bokai Yang, 2023 All Rights Reserved.  \nABSTRACT  \nThe increase of computing power and the ability to log students’ data with the help of the computer-assisted learning systems has led to an increased interest in developing and applying computer science techniques for analyzing learning data. To understand and investigate how learning-generated data can be used to improve student success, data mining techniques have been applied to several educational tasks. This dissertation investigates three important tasks in various domains of educational data mining: learners’ behavior analysis, essay structure analysis and feedback providing, and learners’ dropout prediction. The first project applied latent semantic analysis and machine learning approaches to investigate how MOOC learners’ longitudinal trajectory of meaningful forum participation facilitated learner performance. The findings have implications on refining the courses’ facilitation methods and forum design, helping improve learners’ performance, and assessing learners’ academic performance in MOOCs. The second project aims to analyze the organizational structures used in previous ACT test essays and provide an argumentative structure feedback tool driven by deep learning language models to better support the current automatic essay scoring systems and classroom settings. The third project applied MOOC learners’ forum participation states to predict dropouts with the help of hidden Markov models and other machine learning techniques. The results of this project show that forum behavior can be applied to predict dropout and evaluate the learners’ status. Overall, the results of this  \ndissertation expand current research and shed light on how computer science techniques could further improve students’ learning experience.  \nTABLE OF CONTENTS  \nAbstract .............................................................................................................................. iii  \nList of Tables .................................................................................................................... vii  \nList of Figures .................","cbCaiqoHdp6w3Ygn","https://ap.wps.com/l/cbCaiqoHdp6w3Ygn","pdf",2257131,1,110,"English","en",105,"# Chapter 1 Introduction\n## 1.1 Introduction\n## 1.2 Literature Review\n# Chapter 2 Temporal Analysis of MOOC Learners’ Forum Participation\n## 2.1 Introduction\n## 2.2 Related Works\n## 2.3 Methodology\n## 2.4 Results\n## 2.5 Discussion and Implications\n## 2.6 Conclusion\n# Chapter 3 Argumentative Essay Structure Analysis\n## 3.1 Introduction\n## 3.2 Literature Review\n## 3.3 Data\n## 3.4 System Design\n## 3.5 Results","[{\"question\":\"What three tasks does the dissertation focus on in educational data mining?\",\"answer\":\"It investigates learner behavior analysis, essay structure analysis with feedback providing, and learners’ dropout prediction.\"},{\"question\":\"How does the first project analyze MOOC learners’ forum behavior?\",\"answer\":\"It applies latent semantic analysis and machine learning to study how MOOC forum participation trajectories relate to learner performance.\"},{\"question\":\"What approach supports essay scoring and feedback in the second project?\",\"answer\":\"It analyzes argumentative structures in prior ACT test essays and uses deep learning language models to generate structure feedback that complements automatic essay scoring systems.\"}]","Learning Analytics Through Machine Learning and Natural Language Processing | 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