Data-Driven Investigation of Curiosity in Student Text Responses

Abstract

He has published about 90 peer-reviewed technical articles in international conferences, journals, and book chapters. Dr. Seliya is proactive in computing sciences scholarship and pedagogy enhancement, including grants, undergraduate research, and curriculum and course development. His prior professional endeavors include:

Unleashing the Power of Data Analytics to Examine Engineering Students’ Experiences and Outcomes

“…For example, a study [38] captured students' facial keypoint data when they were reading the instructions of a task to assess their affective state, and used the assessment results to predict student performance. In another study [39], researchers used data mining techniques to capture the curiosity levels among students. In addition, fine learning behaviours, such as students' learning engagement [26] and design behaviors [36].…”

“…The design of these new tools often requires both technical skills and disciplinary knowledge. For example, in affective assessment, researchers need to validate the results from the new tool with the properties of a socio-cognitive construct, such as curiosity [39]. Along a similar line, a potential area of future research will be to draw upon the tools and techniques from cognitive psychology for learning analytics.…”