Predicting Performance Based on the Analysis of Reading Behavior: A Data Challenge

Brendan Flanagan, Atsushi Shimada, Stephen Yang, Bae-Ling Chen, Yang-Chia Shih, Hiroaki Ogata

Research output: Contribution to journalArticlepeer-review

Abstract

As the adoption of digital learning materials in modern education systems is increasing, the analysis of reading behavior and their effect on student performance gains attention. The main motivation of this workshop is to foster research into the analysis of students' interaction with digital textbooks, and find new ways in which it can be used to inform and provide meaningful feedback to stakeholders, such as: teachers, students and researchers. In this workshop, participants analyzed the event logs from three different universities datasets with information on over 1000 students reading behaviors. Additional information on lecture schedules were also provided to enable the analysis of learning context for further insights into the preview, in-class, and review reading strategies that learners employ. Finally, workshop contributors were encouraged to implement their research results as a feature of an open LA dashboard.
Original languageEnglish
Pages (from-to)1-4
Number of pages4
JournalCompanion Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK'19)
Publication statusPublished - Mar 2019

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