
Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems
We explore creating automated, personalized feedback in an intelligent t...
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Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions
While computer and communication technologies have provided effective me...
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Automated Feedback Generation for Introductory Programming Assignments
We present a new method for automatically providing feedback for introdu...
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Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference
In modern computer science education, massive open online courses (MOOCs...
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ProtoTransformer: A MetaLearning Approach to Providing Student Feedback
Highquality computer science education is limited by the difficulty of ...
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Interpretable Cognitive Diagnosis with Neural Network
In intelligent education systems, one key issue is to discover students'...
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Toward SemiAutomatic Misconception Discovery Using Code Embeddings
Understanding students' misconceptions is important for effective teachi...
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Math Operation Embeddings for Openended Solution Analysis and Feedback
Feedback on student answers and even during intermediate steps in their solutions to openended questions is an important element in math education. Such feedback can help students correct their errors and ultimately lead to improved learning outcomes. Most existing approaches for automated student solution analysis and feedback require manually constructing cognitive models and anticipating student errors for each question. This process requires significant human effort and does not scale to most questions used in homework and practices that do not come with this information. In this paper, we analyze students' stepbystep solution processes to equation solving questions in an attempt to scale up error diagnostics and feedback mechanisms developed for a small number of questions to a much larger number of questions. Leveraging a recent math expression encoding method, we represent each math operation applied in solution steps as a transition in the math embedding vector space. We use a dataset that contains student solution steps in the Cognitive Tutor system to learn implicit and explicit representations of math operations. We explore whether these representations can i) identify math operations a student intends to perform in each solution step, regardless of whether they did it correctly or not, and ii) select the appropriate feedback type for incorrect steps. Experimental results show that our learned math operation representations generalize well across different data distributions.
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