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Title: Can textbook annotations serve as an early predictor of student learning?
When engaging with a textbook, students are inclined to highlight key content. Although students believe that highlighting and subsequent review of the highlights will further their educational goals, the psychological literature provides no evidence of benefits. Nonetheless, a student’s choice of text for highlighting may serve as a window into their mental state—their level of comprehension, grasp of the key ideas, reading goals, etc. We explore this hypothesis via an experiment in which 198 participants read sections from a college-level biology text, briefly reviewed the text, and then took a quiz on the material. During initial reading, participants were able to highlight words, phrases, and sentences, and these highlights were displayed along with the complete text during the subsequent review. Consistent with past research, the amount of highlighted material is unrelated to quiz performance. However, our main goal is to examine highlighting as a data source for inferring student understanding. We explored multiple representations of the highlighting patterns and tested Bayesian linear regression and neural network models, but we found little or no relationship between a student’s highlights and quiz performance. Our long-term goal is to design digital textbooks that serve not only as conduits of information into the mind of the reader, but also allow us to draw inferences about the reader at a point where interventions may increase the effectiveness of the material.
Authors:
; ; ; ;
Award ID(s):
1631428
Publication Date:
NSF-PAR ID:
10074162
Journal Name:
Proceedings of the 11th International Conference on Educational Data Mining
Volume:
11
Page Range or eLocation-ID:
431-437
Sponsoring Org:
National Science Foundation
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