College of Education and Human Development

Department of Educational Psychology

Heeryung Choi

  • Pronouns: she/her/hers

  • Post-doctoral associate

Heeryung Choi Headshot

Areas of interest

  • Self-regulated learning to enhance students’ agency
  • Metacognition in student-AI interactions (e.g., enhancing students’ AI literacy to help them make informed decisions)
  • Design and validation of trace data in measuring learning (e.g., using computational data to capture motivation) 
Degrees

PhD, University of Michigan, 2022 (Information)
MS, Seoul National University, 2016 (Cognitive Science)
BA, Seoul National University, 2014 (English Education)

Biography

As a learning analytics researcher, I am committed to fostering self-regulated learning (SRL) skills, with and within technology. These skills are key for empowering students to proactively navigate dynamic environments with their own agency, rather than delegating learning opportunities to computational models, including AI.

Specifically, my research focuses on (1) investigating the impacts of interventions on learner-AI interactions (e.g., Jupyter Notebook extension, AI hint chatbot), and (2) reconstructing learning traces using multimodal data, (e.g., computational data, transcripts). The primary methods I apply include mixed methods, machine learning, and experimental approaches.

Publications

Complete list available at Google Scholar

Choi, H., Winne, P. H., Brooks, C., Li, W., & Shedden, K. (2023, March). Logs or self-reports? Misalignment between behavioral trace data and surveys when modeling learner achievement goal orientation. In LAK23: 13th international learning analytics and knowledge conference (pp. 11-21).

Choi, H., Jovanovic, J., Poquet, O., Brooks, C., Joksimović, S., & Williams, J. J. (2023). The benefit of reflection prompts for encouraging learning with hints in an online programming course. The Internet and Higher Education, 58, 100903.

Choi, H., Winne, P. H., & Brooks, C. (2023). Reconfiguring Measures of Motivational Constructs Using State-Revealing Trace Data. In Unobtrusive Observations of Learning in Digital Environments: Examining Behavior, Cognition, Emotion, Metacognition and Social Processes Using Learning Analytics (pp. 73-89). Cham: Springer International Publishing.