Browsing by Author "Wang, Zichao"

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    Method for mathematical language processing via tree embeddings
    (2024-11-19) Wang, Zichao; Lan, Shiting; Baraniuk, Richard G.; Rice University; University of Massachusetts; United States Patent and Trademark Office
    A method for processing formulae includes encoding a formula by: training, with a server, a model by using a machine learning algorithm with a data set that includes a plurality of formulae; transforming, with a processor, a first formula into a tree format using the trained model; converting, with the processor, the tree format of the first formula into a plurality of lists; and encoding, with the processor, the plurality of lists into a fixed dimension vector by leveraging a stacked attention module; and generating one or more formula candidates by: obtaining, with the processor, input information; and generating, with the processor, one or more second formula candidates based on input information by using the stacked attention module with a tree beam search algorithm.
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    Towards Personalized Human Learning At Scale: A Machine Learning Approach
    (2023-04-20) Wang, Zichao; Baraniuk, Richard G; Mozer, Michael C
    This 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.