Question Generation for Adaptive Education

TL;DR

This paper presents a method for generating personalized educational questions using fine-tuned language models, enhancing adaptive online learning by creating diverse, student-specific questions.

Contribution

It introduces a novel approach to controllable question generation conditioned on student data, leveraging deep knowledge tracing with pre-trained language models.

Findings

Successfully generated novel questions for second language learners

Achieved accurate prediction of student answer correctness

Demonstrated generalization to unseen questions

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.