Question Generation for Adaptive Education

• URL: http://arxiv.org/abs/2106.04262v1
• Date: Tue, 8 Jun 2021 11:46:59 GMT
• Title: Question Generation for Adaptive Education
• Authors: Megha Srivastava and Noah Goodman
• Abstract summary: We show how to fine-tune pre-trained language models for deep knowledge tracing (LM-KT) This model accurately predicts the probability of a student answering a question correctly, and generalizes to questions not seen in training. We then use LM-KT to specify the objective and data for training a model to generate questions conditioned on the student and target difficulty.
• Score: 7.23389716633927
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intelligent and adaptive online education systems aim to make high-quality education available for a diverse range of students. However, existing systems usually depend on a pool of hand-made questions, limiting how fine-grained and open-ended they can be in adapting to individual students. We explore targeted question generation as a controllable sequence generation task. We first show how to fine-tune pre-trained language models for deep knowledge tracing (LM-KT). This model accurately predicts the probability of a student answering a question correctly, and generalizes to questions not seen in training. We then use LM-KT to specify the objective and data for training a model to generate questions conditioned on the student and target difficulty. Our results show we succeed at generating novel, well-calibrated language translation questions for second language learners from a real online education platform.

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