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Option Tracing: Beyond Binary Knowledge Tracing

2020-12-11 · Aritra Ghosh, Andrew S. Lan

This paper details our solutions to Tasks 1&2 of the NeurIPS 2020 Education Challenge.1 Knowledge tracing, a family of methods to estimate each student’s mastery levels on skills/knowledge components from their past responses to assessment questions, is useful for progress monitoring, personalization, and helping teachers to deliver personalized and targeted feedback to students to improve their learning outcomes. One key limitation of current knowledge tracing methods is that they can only estimate an overall knowledge level of a student since they analyze only the binary-valued correctness of student responses. We adapt a series of popular knowledge tracing methods to the task of option prediction in multiple choice questions. Experimental results show that our method performs well on both option prediction and correctness prediction.

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Code (1)

arghosh/NeurIPSEducation2020 pytorch

Tasks

Knowledge TracingMultiple-choicePrediction

Methods 이 논문이 사용한 방법론

Tanh Activation 설명 없음
Sigmoid Activation 설명 없음
LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…

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