Towards Personalized Human Learning At Scale: A Machine Learning Approach

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dc.contributor.advisorBaraniuk, Richard Gen_US
dc.contributor.committeeMemberMozer, Michael Cen_US
dc.creatorWang, Zichaoen_US
dc.date.accessioned2023-08-09T18:29:24Zen_US
dc.date.available2023-08-09T18:29:24Zen_US
dc.date.created2023-05en_US
dc.date.issued2023-04-20en_US
dc.date.submittedMay 2023en_US
dc.date.updated2023-08-09T18:29:25Zen_US
dc.description.abstractThis thesis focuses on personalized learning in education, a promising and effective means of learning where the instructions, educational materials, learning paths, analytics, and reports are tailored to each learner to best support their individual learning paths and improve learning outcomes. Current personalized learning relies heavily on expert instructors and is costly, has limited availability, and is unable to scale to meet the massive demand of learning today. This thesis takes a machine-learning approach to address the aforementioned issues by developing computational models that learn from educational big data to perform the activities central to personalized learning in education. First, I will present a series of works for learning content customization, including methods and systems to generate, evaluate, represent, and analyze different types of learning content such as math word problems, factual quizzes, and scientific formulae. Second, I will present two frameworks for learning analytics, which enable the understanding and tracking of the progress of large numbers of learners effectively and efficiently. Finally, I will present methodologies for trustworthy machine learning, a necessity for deploying machine learning systems in real-world educational scenarios. These methodologies include theoretical tools for understanding recurrent neural networks, the powerhouse underlying modern knowledge tracing models, and controllable data generation, enabling machines to behave more precisely according to human instructions.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationWang, Zichao. "Towards Personalized Human Learning At Scale: A Machine Learning Approach." (2023) Diss., Rice University. <a href="https://hdl.handle.net/1911/115137">https://hdl.handle.net/1911/115137</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/115137en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectmachine learningen_US
dc.subjectnatural language processingen_US
dc.subjectgenerative modelingen_US
dc.subjectpersonalized learningen_US
dc.subjectlearning analyticsen_US
dc.subjectartificial intelligence in educationen_US
dc.titleTowards Personalized Human Learning At Scale: A Machine Learning Approachen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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