Jason M. Harley

TL;DR: In this paper, the synchronization of three modalities (automatic facial expression recognition, self-report, and electrodermal activity) and their agreement regarding learners' emotions was evaluated.

TL;DR: MetaTutor is described and how it can be used to facilitate learning of complex biological topics and as a research tool to examine the role of CAM processes used by learners to understand the complexity of cognitive, affective, and metacognitive processes and their relation to learning.

TL;DR: This paper investigated the usefulness of eye tracking data for predicting emotions relevant to learning, specifically boredom and curiosity, and used a variety of machine learning and feature selection techniques to predict students' self-reported emotions from gaze data features.

TL;DR: In this article, the authors compared undergraduate students' emotions and learning outcomes during a guided historical tour using mobile AR applications, and found that learners were able to effectively and enjoyably learn about historical differences between past and present historical locations by contextualizing their visual representations, and that two mobile AR apps were effective both in and outside of the laboratory.

TL;DR: The authors proposed an integrated model of emotion regulation in achievement situations (ERAS) that integrates propositions about the generation of emotions from control-value theory with propositions about how emotions are regulated and types of ER strategies from PMER.