Bio

My research is situated at the intersection of machine learning and human cognition. In my work, I apply learning analytics and data mining techniques to students’ interaction data in technology-based learning environments. The goal is to translate fine-grained behavioral data into meaningful evidence about students’ cognitive and metacognitive processes. These enhanced understandings of students’ mental processes and competencies are then used to guide the design of and evaluate instructional materials embedded in educational technology.

2021-22 Courses

All Publications

  • A systematic review of empirical studies using log data from open-ended learning environments to measure science and engineering practices BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY Wang, K. D., Cock, J., Kaeser, T., Bumbacher, E. 2022

    View details for DOI 10.1111/bjet.13289

    View details for Web of Science ID 000891715500001

  • Validated diagnostic test for introductory physics course placement PHYSICAL REVIEW PHYSICS EDUCATION RESEARCH Burkholder, E., Wang, K., Wieman, C. 2021; 17 (1)

    View details for DOI 10.1103/PhysRevPhysEducRes.17.010127

    View details for Web of Science ID 000643695100001

  • Examining the Links between Log Data and Reflective Problem-solving Practices in An Interactive Task Wang, K., Nair, K., Wieman, C., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2021: 525-532

    View details for DOI 10.1145/3448139.3448193

    View details for Web of Science ID 000883342500054

  • Can Majoring in Computer Science Improve General Problem-solving Skills? Salehi, S., Wang, K. D., Toorawa, R., Wieman, C., ASSOC COMP MACHINERY ASSOC COMPUTING MACHINERY. 2020: 156-161

    View details for DOI 10.1145/3328778.3366808

    View details for Web of Science ID 000810169400024

  • Factors Contributing to Anesthesia Residents' Learner Engagement and Learning Experience in a Mobile App: A Mixed-Method Design Study Traynor, A., Wang, K., Ngai, L., Chu, L. LIPPINCOTT WILLIAMS & WILKINS. 2017: 102–3

    View details for Web of Science ID 000404667000044