Personalized Explanation
Different students are likely to get different levels of learning outcome even if they receive the same prompt or message. For example, although there is a large body of academic literature showing that students learn better when they write explanations of the concepts they are learning, some of them might not be affected in the real world because they rush to finish their problem set without time to reflect.
In the personalized explanations project, we design randomized experiments to examine what educators should tell students in what context to improve their learning and engagement. We then apply contextual bandits to tailor students’ experience on online education systems. We embed this system in the University of Toronto’s PCRS online learning system.
Relevant Papers
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Conference Paper
Yuya Asano, Madhurima Dutta, Trisha Thakur, Jaemarie Solyst, Stephanie Cristea, Helena Jovic, Andrew Petersen, Joseph Jay Williams
(2021).
Exploring Additional Personalized Support While Attempting Exercise Problems in Online Learning Platforms.
In Proceedings of the Eighth ACM Conference on Learning@ Scale (L@S 2021).
[pdf]
- Conference Paper
Jaemarie Solyst, Trisha Thakur, Madhurima Dutta, Yuya Asano, Andrew Petersen, Joseph Jay Williams
(2021).
Procrastination and Gaming in an Online Homework System of an Inverted CS1.
In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (SIGCSE 2021).
[pdf]
- Conference Paper
Yuya Asano, Jaemarie Solyst, Joseph Jay Williams
(2020).
Characterizing and Influencing Students' Tendency to Write Self-explanations in Online Homework.
In Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK 2020).
[pdf]
- Extended Abstract
Jaemarie Solyst, Yuya Asano, Joseph Jay Williams
(2019).
The instructor reads what you write: Encouraging introductory programming students to engage in self-explanation online.
The 6th Annual Conference on Digital Experimentation @ MIT (CODE@MIT 2019).
[pdf]