2024-08-09-zhang24a.md

title	booktitle	year	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Learning to Compare Hints: Combining Insights from Student Logs and Large Language Models	Proceedings of the 2024 AAAI Conference on Artificial Intelligence	2024	We explore the general problem of learning to predict which teaching actions will result in the best learning outcomes for students in online courses. More specifically, we consider the problem of predicting which hint will most help a student who answers a practice question incorrectly, and who is about to make a second attempt to answer that question. In previous work we showed that log data from thousands of previous students could be used to learn empirically which of several pre-defined hints produces the best learning outcome. However, while that study utilized data from thousands of students submitting millions of responses, it did not consider the actual text of the question, the hint, or the answer. In this paper, we ask the follow-on question “Can we train a machine learned model to examine the text of the question, the answer, and the text of hints, to predict which hint will lead to better learning outcomes?” Our experimental results show that the answer is yes. This is important because the trained model can now be applied to new questions and hints covering related subject matter, to estimate which of the new hints will be most useful, even before testing it on students. Finally, we show that the pairs of hints for which the model makes most accurate predictions are the hint pairs where choosing the right hint has the biggest payoff (i.e., hint pairs for which the difference in learning outcomes is greatest).	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	zhang24a	0	Learning to Compare Hints: Combining Insights from Student Logs and Large Language Models	162	169	162-169	162	false	Zhang, Ted and Kumar, Harshith Arun and Schmucker, Robin and Azaria, Amos and Mitchell, Tom	given family Ted Zhang given family Harshith Arun Kumar given family Robin Schmucker given family Amos Azaria given family Tom Mitchell	2024-08-09		Proceedings of the 2024 AAAI Conference on Artificial Intelligence	257	inproceedings	date-parts 2024 8 9	https://raw.githubusercontent.com/mlresearch/v257/main/assets/zhang24a/zhang24a.pdf