EduAware: using tablet-based navigation gestures to predict learning module performance. Issue 5 (4th July 2021)

• Record Type: Journal Article
• Authors: Ding, Xinyi; Larson, Eric C.; Doyle, Amanda; Donahoo, Kevin; Rajgopal, Radhika; Bing, Eric
• Is Part Of: Interactive learning environments. Volume 29:Issue 5(2021)
• Page Start: 720
• Page End: 732
• Publication Date: 2021-07-04
• Subjects: Personalized learning -- learning analytics -- educational data mining -- navigation gestures; Educational technology -- United States -- Periodicals; 371.33
• DOI: 10.1080/10494820.2019.1609524
• Languages: English
• ISSNs: 1049-4820

Abstract

ABSTRACT: In this paper, we develop a context-aware, tablet-based learning module for adult education. Specifically, we focus on adult education in healthcare-teaching learners to perform a medical screening procedure. Based upon how learners navigate through the learning module (e.g. swipe-speed and click duration, among others), we use machine learning to predict what comprehensive test questions a user will answer correctly or incorrectly. Compared with other context aware learning applications, this is the first time tablet-based navigation gestures have been used to support learning assessment. We conducted a 21 participant user study, showing the system can predict how users respond to test questions with 87.7% accuracy without user-specific calibration data. This is compared to item response theory methods which achieve about 70% accuracy. We also investigate which attributes are most responsible for making predictions.