Open Collections

Zarkoob, Hedayat

University of British Columbia

In this thesis, we use various AI techniques to address several scalability challenges in two academic environments: large university classrooms and large peer-review conferences. In large university classrooms, two main challenges that instructors face are grading open-ended assignments and facilitating in-class discussions. To tackle the issue of grading open-ended assignments at scale, we use ideas from mechanism design and graphical models to design practical peer grading systems that provide strong incentives for students to be truthful and that accurately aggregate reported grades. To facilitate in-class discussions, we develop and analyze a new web-based participation tool designed to encourage active participation from students of different demographics. For large peer-reviewed conferences, we propose a novel reviewer-paper matching approach that uses machine learning and mixed-integer programming techniques to preserve the quality of reviews by finding better matches between reviewers and papers and using reviewer resources more efficiently. To demonstrate the effectiveness of the innovations introduced, we evaluate each innovation through analysis on both real and synthetic data, as well as through survey data.

Text

2024-08-28

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/89059

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/