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Jingwen He

Title: Assistant Professor

Department: Learning Technologies

College: College of Information

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, Michigan State University, 2025
    Major: Educational Psychology and Educational Technology
    Dissertation: Designing AI-Facilitated Feedback Using Feedback Triangle Theory: Examining Instructor Implementation

Current Scheduled Teaching

LTEC 6800.461Special Topics in Learning TechnologiesSummer 10W 2026

Previous Scheduled Teaching

LTEC 5300.002Learning and CognitionSpring 8W2 2026 SPOT
LTEC 5300.001Learning and CognitionFall 8W2 2025 SPOT

Published Intellectual Contributions

    Abstracts and Proceedings

  • He, J., Jin, B., Xie, K., Zhang, D. (2025). Diagnose Academic Emotions from Facial Expressions: Relationship with Science Learning Performance in Web-Based Self-directed Learning. International Conference of the Learning Sciences (ICLS) 2025 Proceedings.
  • Book Chapter

  • He, J., Li, T., Xu, Z., Xie, K. (2025). Leveraging Generative AI in Designing and Delivering Individualized Responsive Feedback for Pre-service Teachers in Higher Education. Artificial Intelligence and Human Agency in Education: Volume Two: AI for Equity, Well-Being, and Innovation in Teaching and Learning. 2 267-293. Springer Nature Singapore. https://link.springer.com/chapter/10.1007/978-981-96-9251-4_11
  • He, J., Frazier, M., Xie, K. (2024). Student–Instructor Relationships in Online Learning in Higher Education. Innovation Trends and Educational Technology in Higher Education.
  • Hawk, N.A., He, J., Xie, K. (2023). A comprehensive framework of engagement in k-12 virtual learning: Examining communities of support. A comprehensive framework of engagement in k-12 virtual learning: Examining communities of support.
  • Journal Article

  • He, J., Liu, Y., Zhao, W., Xie, K., Zhang, D. (2026). The Impact of Social-Affective Support Feedback on Students’ Science Inquiry Learning. Learning and Motivation.
  • He, J., Xie, K. (2026). Who Wrote This? College Students’ Perceptions and Evaluations of Human and AI-Facilitated Feedback. Educational Psychology.
  • Xie, K., Jiang, Z., Men, Q., Pan, Z., He, J. (2025). Profiles of students’ behavioral engagement and their associations to academic motivation in online learning. Journal of Computing in Higher Education. 1-40. Springer US. https://link.springer.com/article/10.1007/s12528-025-09465-1
  • He, J., Jiang, Z., Pan, Z., Men, Q., Xie, K. (2025). The Transition Patterns of Learners’ Behavior and the Association with Motivation and Cognitive Engagement in Online Learning. Research and Practice in Technology Enhanced Learning.
  • He, J., Liu, Y., Ran, T., Zhang, D. (2023). How students’ perception of feedback influences self-regulated learning: The mediating role of self-efficacy and goal orientation. European Journal of Psychology of Education.
  • Jiang, Z., Xu, Z., Pan, Z., He, J., Xie, K. (2023). Exploring the Role of Artificial Intelligence in Facilitating Assessment of Writing Performance in Second Language Learning. Languages.
  • He, J., Simon, S., Chiang, F. (2022). A comparative study of pre-service teachers' perceptions on STEAM education in UK and China. STEM Education.
  • He, J., Jin, B., Xu, Z., Zhang, D. (2022). Measuring elementary students’ behavioral engagement in web-based science inquiry learning. Journal of Online Learning Research.
  • Zhang, D., He, J., Fu, D. (2021). How can we improve teacher’s work engagement? based on Chinese experiences. Frontiers in Psychology.
  • Hackman, S.T., He, J., Zhang, D. (2021). Secondary school science teachers’ attitudes towards STEM education in Liberia. International Journal of Science Education.

Contracts, Grants and Sponsored Research

    Grant - Research

  • Boettger, R.K. (Principal), Pellegrini, M. (Co-Principal), He, J. (Co-Principal), Ji, H. (Co-Principal), "Human-Guided AI for Curriculum Innovation: Transforming Faculty Expertise into Ethical AI-Enhanced Learning Resources," sponsored by Learning Institute, University of North Texas, $11000 Funded. (2026).
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Overall
Summative Rating
Challenge and
Engagement Index
Response Rate

out of 5

out of 7
%
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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