Impacts of User Sentiment on Information Recall, Intrinsic Motivation, and Engagement in the Context of Intelligent Tutoring Systems
Open Access
Author:
Metaxas, Luke Richard
Graduate Program:
Information Sciences and Technology
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
March 21, 2018
Committee Members:
Frank Edward Ritter, Thesis Advisor/Co-Advisor
Keywords:
sentiment motivation engagement flow emotion and learning computer-based instruction intelligent tutoring systems affect
Abstract:
Socrates once stated: “Education is the kindling of a flame, not the filling of a vessel.” Intelligent tutoring systems use computational models to efficiently “fill the vessel.” However, research is limited on how these systems can enable “kindling of a flame.” To explore motivation-adaptive learning, this study assesses the learning impact of an individual’s sentiments (emotional associations) on three learning outcomes: information recall, intrinsic motivation, and engagement. Seventy volunteers took two computer-based tutors and provided self-report measures throughout their learning. The learning impacts of topic sentiment and learning-medium sentiment were measured separately and compared. For both topic and learning-medium sentiment, results showed positive linear relationships between net sentiments and intrinsic motivation and net sentiments and engagement. A negative linear relationship between negative sentiments and information recall was also identified. Findings were summarized, and four computer-based instruction design recommendations were provided. Recommendations include: to use learner emotion data in the form of sentiments to better understand learning outcomes, to align sentiment measurement strategies with the tutor’s purpose, to account for the impact of prior sentiments, and to monitor sentiment change.
