Personalization and Incentive Design in E-Learning Systems.
Avi Segal · 2014
My thesis focuses on the design of systems to augment exist-ing e-learning software in a way that supports both teachers and students. It addresses three central challenges: per-sonalization of educational content to students, techniques for machine-generated interventions, and incentive designs to enhance students ’ learning. For each of these problems I will synthesizes approaches from informational retrieval and social choice theory. My results thus far have included a novel algorithm for sequencing content in e-learning sys-tem that uses collaborative filtering to generate a difficulty ranking over the test questions, without needing to predict students ’ performance directly on these questions. The al-gorithm was able to outperform state-of-the-art approaches from the literature on two different data sets containing mil-lions of records. My future efforts will be directed to ex-tending these results and to generalize my approach to the problems of intervention and incentive designs. 1.