Question Generation for Adaptive Education
This addresses the limitation of hand-made question pools in adaptive education systems, enabling more fine-grained and open-ended adaptation for diverse students, though it is incremental as it builds on existing language models and knowledge tracing methods.
The paper tackled the problem of generating adaptive questions for online education by using a fine-tuned language model for knowledge tracing to predict student performance and condition question generation on student and difficulty, resulting in novel, well-calibrated language translation questions for second language learners from a real platform.
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.