Date of Award

Spring 6-3-2021

Document Type

Thesis (Undergraduate)

Department

Cognitive Science Program

First Advisor

Jeremy Manning

Second Advisor

Adina Roskies

Abstract

This research project investigates whether there exists an optimal way to structure topics in educational course content that results in higher levels of engagement among students. It is implemented by fitting topic models to transcripts of educational videos contained in the Khan Academy platform. The fitted models were used to extract topic trajectories across time for each video and subsequently clustered based on whether they have similar “shapes”. The differences in mean engagement metrics per cluster suggest that some course shapes are more palatable to students regardless of subject matter. Additionally, the topic trajectories suggest a constant progression of topics with little repetition is optimal for student engagement. The results from this project provide new methodologies to improve educational quality by focusing on the sequence of themes within instructional material.

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