Tell Me Who Your Students Are: GPT Can Generate Valid Multiple-Choice Questions When Students' (Mis)Understanding Is Hinted

no code implementations • 9 May 2025 • Machi Shimmei, Masaki Uto, Yuichiroh Matsubayashi, Kentaro Inui, Aditi Mallavarapu, Noboru Matsuda

To evaluate the validity of the generated MCQs, Item Response Theory (IRT) was applied to compare item characteristics between MCQs generated by AnaQuest, a baseline ChatGPT prompt, and human-crafted items.

Language Modeling Language Modelling +4

Students' Perceptions and Preferences of Generative Artificial Intelligence Feedback for Programming

no code implementations • 17 Dec 2023 • Zhengdong Zhang, Zihan Dong, Yang Shi, Noboru Matsuda, Thomas Price, Dongkuan Xu

This study demonstrated that ChatGPT could generate Java programming assignment feedback that students perceived as formative.

Specificity

Assertion Enhanced Few-Shot Learning: Instructive Technique for Large Language Models to Generate Educational Explanations

no code implementations • 5 Dec 2023 • Tasmia Shahriar, Kelly Ramos, Noboru Matsuda

Human educators possess an intrinsic ability to anticipate and seek educational explanations from students, which drives them to pose thought-provoking questions when students cannot articulate these explanations independently.

Explanation Generation Few-Shot Learning

Entity Aware Syntax Tree Based Data Augmentation for Natural Language Understanding

no code implementations • 6 Sep 2022 • Jiaxing Xu, Jianbin Cui, Jiangneng Li, Wenge Rong, Noboru Matsuda

One of the main challenges is to collect a sufficient amount of annotated data to train a model.

Intent Detection Natural Language Understanding +3