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Dr. Jiun-Yu Wu is a Professor in the Department of Teaching and Learning at 91��Ƭ�S�ϼ�, specializing in Learning Analytics. He possesses a transdisciplinary academic background that bridges educational psychology and engineering. Dr. Wu obtained his Ph.D. in Research, Measurement, and Statistics, focusing on Learning Science from Texas A&M University (2007-2010). He held both his Master of Science (2001-2003) and Bachelor of Science (1997-2001) degrees in Electrical Engineering, specializing in Telecommunication Engineering, at National Chiao Tung University in Taiwan.

His research is at the intersection of Learning Science and Data Science, including Machine Learning (ML), Generative AI (GenAI), Multilevel Modeling, and Network Analysis. Through this transdisciplinary approach, Dr. Wu analyzes vast amounts of multimodal data to uncover significant insights, supporting evidence-based decision-making and forming a synergy between theory and practice. Leveraging AI-enhanced Learning Analytics (LA), he explores learners’ engagement, tracks their educational progress, and supports their cognitive and socio-emotional growth in today's digital environment.

As the psychological mechanisms that underpin online learning remain to be fully explored and understood, Dr. Wu set out to use AI-enhanced learning analytics to study discussion posts and other online student interactions. In doing so, he successfully revealed several strong early predictors of academic achievement with these posts and, conversely, indicators of students at risk of failure and requiring academic intervention, such as Digital Distraction, Media Multitasking, and Help-Seeking Avoidance. He is a recognized scholar in the academic community, evidenced by his inclusion in the World’s Top 2% of most-cited scientists from 2021 to 2024.

He serves as the Associate Editor for Educational Psychology: An International Journal of Experimental Educational Psychology and as the consulting editor for Journal of Clinical Psychology and guest-edited a special issue of GenAI uses in education in Educational Technology & Society. He is also a member of the advisory board for the Journal of School Psychology and serves on the editorial board of the International Journal of STEM Education. His editorial work underscores his commitment to fostering scholarly discourse and supporting the dissemination of impactful research. He previously chaired the American Educational Research Association (AERA) Multilevel Modeling Special Interest Group (MLM-SIG) from 2014 to 2015. His contributions to academia have been recognized with numerous awards, including the 2023 Outstanding Research Award and the 2019 Wu Da-You Memorial Award in Taiwan.

As a Principal Investigator (PI), he secured over 2.2 million USD in grant funding from government agencies, including the National Science and Technology Council and the Ministry of Education in Taiwan, reflecting his unwavering dedication to advancing research excellence in AI-enhanced LA and digital learning. He served as the co-PI for the Programme for International Student Assessment (PISA) 2015 and the International Computer and Information Literacy Study (ICILS) 2025 in Taiwan, as well as a consultant for PISA 2022 and 2025.

Dr. Wu continues to advance comprehensive human-machine symbiotic learning ecological frameworks, utilizing AI-enhanced learning analytics to create seamless learning experiences. This work empowers students and educators to make informed decisions and optimizes educational progress and outcomes on a global scale. His research prioritizes educational equity, leveraging data-informed insights to tackle learning disparities and provide equal opportunities to achieve success. Dr. Wu is currently expanding his team and actively recruiting students and researchers to collaborate on these transformative projects, aiming to further innovate and expand the impact.

Selected Publications (*the corresponding author; †postdoc or advisee)

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Wu, J.-Y.*, Liao, C.-H.†, Tsai, C.-C., & Kwok, O.-M. (2024). Using learning analytics with temporal modeling to uncover the interplay of before-class video viewing engagement motivation, and performance in an active learning context. Computers & Education, 212, 104975. .

Weng, M.-C.†, Kwok, O.-M., & Wu, J.-Y.* (2024). Breaking the Law of Inertia for Students with Poor Grit and Achievement: The Predictive Mechanism of Grit on the Short-Term and Long-Term Achievement. The Asia-Pacific Education Researcher. .

Lin, H.-M., Wu, J.-Y., Liang, J.-C.*, Lee, Y.-H., Huang, P.-C., Kwok, O.-M., & Tsai, C.-C. (2023). A review of using multilevel modeling in e-learning research. Computers & Education, 198, 104762. .

Weng, M.-C.†, Liao, C.-H.†, Kwok, O., & Wu, J.-Y.* (2023). What is the best model of grit among junior high students: Model selection, measurement invariance, and group difference. Social Development, 1-17.

Liao, C.-H.†, & Wu, J.-Y.* (2023). Learning analytics on video-viewing engagement in a flipped statistics course: Relating external video-viewing patterns to internal motivational dynamics and performance. Computers & Education, 197, 104754.

Liao, C.-H†, Wu, J.-Y.* (2022). Deploying multimodal learning analysis models to explore the impact of digital distraction and peer learning on student performance. Computers & Education, 190, 104599. .

Wu, J.-Y.., & Tsai, C. C.* (2022) Harnessing the power of promising technologies to transform science education: prospects and challenges to promote adaptive epistemic beliefs in science learning. International Journal of Science Education, 44(2), 346-353.

Wu, J.-Y.*, & Nain, M-W.†, (2021). The dynamics of an online learning community in hybrid statistics classroom over time: Implications for the question-orientated problem-solving course design with the social network analysis approach. Computers & Education, 166, 104120. doi: 10.1016/j.compedu.2020.104120.

Wu, J.-Y.*, Liao, C.-H.†, Nian, M.-W.† ,& Cheng, T.† (2021). Using data analytics to investigate attendees' behaviors and psychological states in a virtual academic conference. Educational Technology & Society, 24(1).

• Special Issue on "Online synchronous conference."

Wu, J.-Y.*, Yang, C.C.Y., Liao, C.-H.†, Nian, M.-W.† (2021). Analytics 2.0 for precision education: An integrative theoretical framework of the human and machine symbiotic learning. Educational Technology & Society, 24(1).

• Special issue on "Precision Education- A New Challenge for AI in Education."

Wu, J.-Y. (2021). Learning analytics on structured and unstructured heterogeneous data source: Perspectives from procrastination, help-seeking, and machine-learning defined cognitive engagement. Computers & Education, 163, 104066. doi:10.1016/j.compedu.2020.104066.

Wu, J.-Y. (2020). The predictive validities of individual working-memory capacity profiles and note-taking strategies on online search performance. Journal of Computer Assisted Learning, 36(6), 876-889. 

Wu, J.-Y.*, Hsiao, Y.-C.† (2020). Using supervised machine learning on large-scale, online forums to classify course-related Facebook messages in predicting learning achievement within the personal learning environment. Interactive Learning Environments, 28(10), 65-80. 

Wu, J.-Y.*, & Cheng, T.† (2019). Who is better adapted in learning online within the the personal learning environment? Relating gender differences in cognitive attention networks to digital distraction. Computers & Education, 128, 312-329.. 

Kwok, O.*, Cheung, M. W.-L., Jak, S., Ryu, E., & Wu, J.-Y. (2018) Editorial: Recent advancements in structural equation modeling (SEM): From both methodological and application perspectives. Frontiers in Psychology.

Wu, J.-Y.*, & Xie, C.† (2018). Using time pressure and note-taking to prevent digital distraction behavior and enhance online search performance: Perspectives from the load theory of attention and cognitive control. Computers in Human Behavior, 88, 244-254.. 

Wu, J.-Y.*, Lee, Y.-H., & Lin, J.J.H.† (2018). Using iMCFA to perform the CFA, multilevel CFA, and maximum model for analyzing complex survey data. Frontiers in Psychology, 9, 291. .  

Wu, J.-Y. (2017). The indirect relationship of media multitasking self-efficacy on learning performance within the personal learning environment: Implications from the mechanism of perceived attention problems and self-regulation strategies. Computers & Education, 106, 56-72. 

Wu, J-Y.* , Lin, J.J.H.†, Nian, M.-W.†, & Hsiao, Y.-C.† (2017). A solution to modeling multilevel confirmatory factor analysis with data from complex survey sampling to avoid conflated parameter estimates. Frontiers in Psychology, 8. .

Wu, J.-Y.*, & Peng, Y.-C.† (2017). The modality effect on reading literacy: Perspectives from students' online reading habits, cognitive and metacognitive strategies, and web navigation skills across regions. Interactive Learning Environments, 25(7), 859-876. . 

Clemens, N.H.*, Lai, M.H.C., Burke, M., & Wu, J.-Y. (2017). Interrelations of growth in letter naming and sound fluency in kindergarten and implications for subsequent reading fluency. School Psychology Review, 46(3), 272-287. .

Wu, J.-Y. (2015). University students' motivated attention and use of regulation strategies on social media. Computers & Education, 89, 75-90. .