Further Reading
Previous attempts to define grand challenges
Baker, R. S. (2019). Challenges for the future of educational data mining: The Baker learning analytics prizes. Journal of educational data mining, 11(1), 1-17. [replay/paper]
Buckingham Shum, S. (2023). Trust, Sustainability and Learning@Scale. Keynote Address, Proceedings of the Tenth ACM Conference on Learning@Scale (L@S ’23). Association for Computing Machinery, New York, NY, USA, pp. 1–2. [abstract/replay/slides]
Dede, C.J., Ho, A.D. and Mitros, P., 2016. Big data analysis in higher education: Promises and pitfalls. Educause Review.
Dede, C. and Lidwell, W., 2023. Developing a next-generation model for massive digital learning. Education Sciences, 13(8), p.845.
Ferguson, R., Hoel, T., Scheffel, M. and Drachsler, H. (2016). Guest Editorial: Ethics and Privacy in Learning Analytics. Journal of Learning Analytics, 3(1) pp. 5–15. [ethical goals of learning analytics]
Ihantola, P., Vihavainen, A., Ahadi, A., Butler, M., Börstler, J., Edwards, S.H., Isohanni, E., Korhonen, A., Petersen, A., Rivers, K. and Rubio, M.Á., 2015. Educational data mining and learning analytics in programming: Literature review and case studies. Proceedings of the 2015 ITiCSE on working group reports, pp.41-63.
Kay, J. (2012). AI and education: Grand challenges. IEEE Intelligent Systems, 27(5), 66-69.
Kay, J., 2022. Grand Challenges for Pervasive Technology to Transform Pervasive Education. IEEE Pervasive Computing, 21(3), pp.32-41.
Manderveld, J., Berg, A., Schuwer, R. and Drachsler, H., 2015. Grand Challenges Learning Analytics and Open Online Onderwijs.
National Academy of Engineering. (2008). Grand challenges for engineering in the 21st century.
Oviatt, S., Cohen, A. and Weibel, N., 2013, December. Multimodal learning analytics: Description of math data corpus for ICMI grand challenge workshop. In Proceedings of the 15th ACM on International conference on multimodal interaction (pp. 563-568).
Pea, R. and Jacks, D., 2014. Building the field of learning analytics for personalized learning at scale
Roll, I. and Winne, P.H., 2015. Understanding, evaluating, and supporting self-regulated learning using learning analytics. Journal of Learning Analytics, 2(1), pp.7-12.
Worsley, M., Chiluiza, K., Grafsgaard, J.F. and Ochoa, X., 2015. 2015 multimodal learning and analytics grand challenge. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (pp. 525-529).
Papers that point to important new directions for the field
These were identified via the pre-workshop survey
Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gaševic, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. Computers and Education: Artificial Intelligence, 6, 100215. doi:10.1016/j.caeai.2024.100215
Buckingham Shum, S. (In Press). AI for learner flourishing in the age of the polycrisis, on the edge of the metacrisis. The Blue Dot, Issue 18, UNESCO Mahatma Ghandi Institute of Education for Peace & Sustainable Development.
Casebourne, I., Shi, S., Hogan, M., Holmes, W., Hoel, T., Wegerif, R., & Yuan, L. (2024). Using AI to Support Education for Collective Intelligence. International Journal of Artificial Intelligence in Education.
Drachsler, H. (2023). Towards Highly Informative Learning Analytics. Open Universiteit, Netherlands, Heerlen.
Fan, Y., van der Graaf, J., Lim, L., Raković, M., Singh, S., Kilgour, J., ... & Gašević, D. (2022). Towards investigating the validity of measurement of self-regulated learning based on trace data. Metacognition and Learning, 17(3), 949-987.
Hilliger, I., Ceballos, H. G., Maldonado-Mahauad, J., & Ferreira, R. (2024). Applications of Learning Analytics in Latin America. Journal of Learning Analytics, 11(1), 1-5.
Kitto, K. , Hicks, B. , & Buckingham Shum, S. (2023). Using causal models to bridge the divide between big data and educational theory. British Journal of Educational Technology, 54, 1095–1124.
Kube, D., Weidlich, J., Kreijns, K., & Drachsler, H. (2024). Addressing gender in STEM classrooms: The impact of gender bias on women scientists’ experiences in higher education careers in Germany. Educ Inf Technol (2024).
Lang, C. & Davis, L. (2023). Learning analytics and stakeholder inclusion: What do we mean when we say "human-centered"? In Lak23: 13th international learning analytics and knowledge conference (p. 411–417). New York, NY, USA: Association for Computing Machinery.
Motz, B. A., Bergner, Y., Brooks, C. A., Gladden, A., Gray, G., Lang, C., ... & Quick, J. D. (2023). A LAK of Direction Misalignment Between the Goals of Learning Analytics and its Research Scholarship. Journal of Learning Analytics, 10(2), 1-13.
Prieto, L. P., Rodríguez-Triana, M. J., Odriozola-González, P. & Dimitriadis, Y. (2022). Single-Case Learning Analytics to Support Social-Emotional Learning: The Case of Doctoral Education. In Y. E. Wang, S. Joksimović, M. O. Z. San Pedro, J. D. Way, & J. Whitmer (Eds.), Social and Emotional Learning and Complex Skills Assessment: An Inclusive Learning Analytics Perspective (pp. 251-278). Springer International Publishing.
Saqr, M., Schreuder, M. J. & López-Pernas, S. (2024). Why educational research needs a complex system revolution that embraces individual differences, heterogeneity, and uncertainty. In M. Saqr & S. López-Pernas (Eds.), Learning analytics methods and tutorials: A practical guide using R (pp. 723-734).Springer, Cham. doi: 10.1007/978-3-031-54464-4_22
Su, H., Tong, Y., Zhang, X. & Fan, Y. (2024). Uncovering Students’ Processing Tactics Towards ChatGPT’s Feedback in EFL Education Using Learning Analytics. In: Ma, W.W.K., Li, C., Fan, C.W., U, L.H., Lu, A. (eds) Blended Learning. Intelligent Computing in Education. ICBL 2024. Lecture Notes in Computer Science, vol 14797. Springer, Singapore.
Tea, A.C. & Ladybugboss, D. (2025). Burnout from Humans: A Little Book About AI That Is Not Really About AI.
Weidlich, J., Fink, A., Jivet, I., Yau, J., Giorgashvili, T., Drachsler, H. & Frey, A. (2024). Emotional and motivational effects of automated and personalized formative feedback: The role of reference frames. Journal of Computer Assisted Learning. doi: 10.1111/jcal.13024
Other resources that point to important new directions for the field
These were identified via the pre-workshop survey