Abstract
In this thesis, I investigate deep neural network based student response modeling, more specifically Knowledge Tracing (KT). Knowledge Tracing allows Intelligent Tutoring Systems to infer which topics or skills a student has mastered, thus adjusting curriculum accordingly. Deep neural network based knowledge tracing models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) have achieved significant improvements compared with conventional probabilistic models. There are mainly two goals in this thesis: 1) To have a better understanding of existing deep neural network based models and their predictions through visualization and through incorporating uncertainties. 2) To improve the performance of student response modeling with multimodality and attention mechanisms. In this thesis, I will first introduce the background and show why deep neural network based knowledge tracing models might have less depth than anticipated through visualization. Next, I propose a more practical way of alleviating the concerns of these deep models by incorporating uncertainty for each prediction. Then, I will discuss how adding more modalities and attention mechanisms might help improve model performance.
Degree Date
Fall 12-19-2020
Document Type
Dissertation
Degree Name
Ph.D.
Department
Computer Science and Engineering
Advisor
Eric C. Larson
Subject Area
Computer Science
Number of Pages
153
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Ding, Xinyi, "Deep Neural Network Based Student Response Modeling With Uncertainty, Multimodality and Attention" (2020). Computer Science and Engineering Theses and Dissertations. 18.
https://scholar.smu.edu/engineering_compsci_etds/18
