Megha Srivastava, Noah Goodman
We have not lifted any functions out of this paper's repositories yet, so there is nothing we have run. If it links a repository, it is listed below.
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 finegrained 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, wellcalibrated language translation questions for second language learners from a real online education platform.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2106.04262")
get_code_for_paper("2106.04262")
have("2106.04262")
Connect an agent — have() is free.