Research Projects
Research Projects
My research brings an innovative approach to the intersection of technology and education, combining expertise in learning sciences, learning analytics, and human-centered design. I explore how AI and learning analytics can foster more agentic and actionable collaborative discourse, bridging analytical measurement and practical implementation in higher education to ensure that advanced analytics lead to tangible improvements in teaching and learning. My work focuses on three interconnected areas:
AI as an Analytical Lens – Using learning analytics techniques to uncover the semantic and temporal patterns that shape collaborative discourse in technology-enhanced learning environments.
AI as a Learning Partner – Designing and implementing AI-powered tools (e.g., personalized student messages, generative AI chatbots) that support meaningful dialogues in authentic educational contexts.
AI as Curriculum Content – Developing scaffolded, project-based curricula that build AI literacy and epistemic agency, particularly in data science education.
Explore examples of this work below, and feel free to reach out if you’d like to learn more, especially about projects currently under review.
Using learning analytics techniques (e.g., natural language processing, network analysis, and machine learning) to uncover the dynamic semantic and temporal patterns in collaborative discourse in technology-enhanced learning environments.
Jung, Y., Zhu, X., Oshima, J., Chen, B., Moon, J., Puntambekar, S., & Dey, I. (2025, June). Towards Actionable Collaborative Discourse Analysis: Bridging Advanced Computational Analysis to Practical Implementation. The International Society of the Learning Sciences Annual Meeting 2025 (ISLS’25). [Symposium Co-Chair]
Zhu, X., Jung, Y., Chen, B., Hickey, D., Chartrand, G., Kalir, J., Hodgson, J., Andrew, C., Wise, A., Hong, S., Chen, P., Avadhanam, R. M. (2024, June). Bridging Social Annotation Practice with Perspectives from the Learning Sciences and CSCL. In Proceedings of the International Society of the Learning Sciences Annual Meeting 2024 (ISLS’24). [Symposium Co-chair]
Jung, Y. & Zhu, X. (2025, April). Unveiling dynamics of student engagement in collaborative discourse: A temporal clustering approach. In Proceedings of 2025 Annual Meeting of the American Educational Research Association (AERA’25), Denver, CO, United States. [Poster]
Li, F., Jung, Y., & Wise, A. F. (2024, March). “I Didn’t Pass the Exam Because ...”: Testing the Viability of Conceptual Features for Actionable Analytics in the Context of Competency Exam Failure Reflection. In Companion Proceedings of 14th International Conference on Learning Analytics & Knowledge (LAK’24). [Poster]
Jung, Y. & Wise, A. F. (2022). Examining qualities and development of dental student reflections using theory-informed data science methods. Advances in Health Sciences Education, 27(1), 23-48. https://doi.org/10.1007/s10459-021-10067-6. [SSCI-indexed; IF = 3.3]
Jung, Y. & Wise, A. F. (2020, March). How and how well do students reflect?: Multi-dimensional automated reflection assessment in health professions education. In Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK’20) (pp. 595-604). ACM. Acceptance rate: 30%
Designing and implementing AI-powered learning tools that foster richer, more meaningful dialogues, situating their use within authentic contexts and the realities of educational ecosystems.
Jung, Y. & Wise, A. F. (2025). How students engage with learning analytics: Access, action-taking, and learning routines with message-based information to support collaborative annotation. Computers & Education. https://doi.org/10.1016/j.compedu.2025.105280 [SSCI-indexed; IF = 10.5]
Jung, Y., Wise, A. F., Dimitriadis, Y., & Amarasinghe, I. (2024, March). Advancing Actionability in Learning Analytics by Uniting Diverse Stakeholder Perspectives. In Companion Proceedings of 14th International Conference on Learning Analytics & Knowledge (LAK’24). [Workshop Co-Chair]
Jung, Y., & Wise, A. F. (2024, March). Probing Actionability in Learning Analytics: The Role of Routines, Timing, and Pathways. In Proceedings of the 14th International Conference on Learning Analytics & Knowledge (pp. 871-877). ACM. Acceptance rate: 30%
Jung, Y., Wise, A. F., Sarmiento, J. P., & Li, F. (Under Abstract Review). Building Learning Analytics for Impact: Empirical Insights into Agency and Actionability as Core Human-Centered Design Stances. British Journal of Educational Technology. [SSCI-indexed; IF = 8.1]
Jung, Y., Lee, S., & Wise, A. F. (Under Review). Short-Term Gains, Long-Term Gaps: Unpacking the Temporal Role of Analytics-Based Feedback on Student Multi-dimensional Engagement in Social Annotation. The Internet and Higher Education. [SSCI-indexed]
Jung, Y., & Lee, S. (Under Review). The Ephemeral Power of Analytics: Tracing Multi-Dimensional Engagement in Social Annotation Over Time. 2026 Annual Meeting of the American Educational Research Association (AERA’26).
Li, Q., Jung, Y., Wise, A., Sommer, S., & Alexandra, V. (2021). Designing analytics to support team learning. In L. O. Campbell, R. Hartshorne and R. F. DeMara (Eds). Perspectives on Digitally-Mediated Team Learning (pp. 147-165). Springer.
Jung, Y. & Jin, S. (2025). Questioning the role of AI as collaborator: A Systematic Literature Review of students’ discourse with generative AI for knowledge co-construction. Interactive Learning Environments. https://doi.org/10.1080/10494820.2025.2556808 [SSCI-indexed; IF = 5.3
Moon, J., Jung, Y., Bae, H., Kim, K., & Lee, U. (2025). Socio-material interactions: A multi-case study on AI chatbot integration in asynchronous online learning. Innovations in Education and Teaching International. https://doi.org/10.1080/14703297.2025.2534189 [SSCI-indexed; IF = 4.9]
Jung, Y. (2025, April). From Exploration to Co-construction: Unpacking Temporal Dynamics of AI Chatbot-Supported Knowledge Construction in Graduate Education. 2025 Annual Meeting of the Korean-American Educational Research Association (KAERA’25), Denver, CO, United States. [Paper] [Best Conference Paper Award]
Jung, Y. & Yang, E. (2025, March). Does Proactiveness Matter?: Role of Different Levels of Proactiveness for AI Tutors in Enhancing On-the-Spot Self-Paced Online Learning. In Companion Proceedings of 15th International Conference on Learning Analytics & Knowledge (LAK’25). [Poster]
Jung, Y., Mun, S., & Jeong, G. (Under Review). Unpacking Student-AI Engagement: The Role of Argumentation in Shaping Behavioral and Cognitive Learning Processes. Behaviour & Information Technology. [SSCI-indexed; IF = 3.1]
Jung, Y., Lee, U., Lee, Y., Kim, D., Bae, J., Kang, M., & Choi, H. (Under Review). Rethinking Automated Feedback in Programming Education: A Systematic Literature Review Across Design, Pedagogy, and Implementation. Educational Research Review. [SSCI-indexed; IF = 10.6]
Dadhich, S., Chekati, A., Booth, B., Windsor, A., Cook, A., Jung, Y., & Phan, V. (Under Review). PRISM: A Proactive Review and Insight via Snapshot-based Monitoring Tool for Programming Education. SIGCSE-TS 2026.
Li, Q., Jung, Y.*, & Wise, A. F. (2025). How instructors use learning analytics: The pivotal role of pedagogy. Journal of Computing in Higher Education. https://doi.org/10.1007/s12528-025-09432-w [SSCI-indexed; IF = 4.9]
Wise, A. F. & Jung, Y. (2019). Teaching with analytics: Towards a situated model of instructional decision-making. Journal of Learning Analytics, 6(2), 53-69. https://doi.org/10.18608/jla.2019.62.4 [Scopus-indexed; IF = 3.9]
Li, Q., Jung, Y., D’anjou, B., & Wise, A. F. (2022, March). Unpacking instructors’ analytics use: Two distinct profiles for informing teaching. In Proceedings of the 12th International Conference on Learning Analytics & Knowledge (LAK’22). ACM. Acceptance rate: 29% [Awarded Best Research Paper]
Li, Q., Jung, Y., & Wise, A. F. (2021, March). Beyond first encounters with analytics: Questions, techniques and challenges in instructors’ sensemaking. In Proceedings of the 11th International Conference on Learning Analytics & Knowledge (LAK’21) (pp. 344-353). ACM. Acceptance rate: 31%
Creating scaffolded, project-based curricula that develop AI literacy and epistemic agency for learners in Data Sciences, with a particular focus on preparing higher education learners to engage critically and constructively with AI.
Olney, A., Barboza, L†., Farzan, F†., Fleming, S†., Jung, Y†., Mashrique, H†., Rus, V†., & Tawfik, A†., (Under Review). Data science is different: Effects of blocks programming, self-explanations, and subgoal labels on learning data science. International Journal of Artificial Intelligence in Education. [SSCI-indexed; IF = 8.5]
Tawfik, A., Olney, A., Barboza, L., & Jung, Y. (Under Review). Data Science for K6-12: A Design-Based Research Study. Educational Technology Research & Development. [SSCI-indexed; IF = 4.2]
Tawfik, A., Jung, Y., & Ketter, H. (Under Review). Educating AI/Data Literacy in Higher Education: A Systematic Literature Review. Innovations in Education and Teaching International. [SSCI-indexed; IF = 4.9]
Barboza, L., & Jung, Y. (Under Review). Blockly-AI into Marketing Education: Teaching AI to Non-STEM Students. 2026 Annual Meeting of the American Educational Research Association (AERA’26)