Title: Individual investigation: Examples of learning analytics
Course: Learning Analytics > Section: Learning analytics: definition, characteristics, purpose

Participants will receive a list of approximately 10 relevant scientific papers from the field of learning analytics, with concrete examples of learning analytics. Using AI tools (e.g., NotebookLM, Perplexity), they should review the abstracts of the papers. From the list, they will select 2 papers to read in full. Alternatively, they may independently explore additional papers to read.

Moodle resource: Page/Folder

External tools: NotebookLM, Perplexity, or other similar AI tools

Below is a list with links to 10 recent scientific papers from the field of learning analytics, featuring different examples of learning analytics.

- Review the abstracts of the papers, using artificial intelligence tools such as Notebook LM.
- Based on this, select two papers and read them in full.
- Instead of the listed papers, you may also independently explore additional papers to read.
- You may also visit the blog, which publishes learning analytics research in a way that is accessible to a broader audience.
- The estimated time required for this activity is 4 hours.

1. Albuquerque, J.; Rienties, B.; Divjak, B. (2025). Decoding Learning Design Decisions: A Cluster Analysis of 12,749 Teaching and Learning Activities. LAK '25: Proceedings of the 15th International Learning Analytics and Knowledge Conference. New York: Association for Computing Machinery; 407–417.
2. Divjak, B.; Svetec, B.; Horvat, D.; Kadoić, N. (2023). Assessment validity and learning analytics as prerequisites for ensuring student-centred learning design. British Journal of Educational Technology, 54(1), 313–334.
3. Divjak, B.; Svetec, B.; Horvat, D. (2024). How can valid and reliable automatic formative assessment predict the acquisition of learning outcomes?. Journal of Computer Assisted Learning, 40(6), 2616–2632.
4. Divjak, B.; Barthakur, A.; Kovanović, V.; Svetec, B. (2025). The Impact of Learning Design on the Mastery of Learning Outcomes in Higher Education. LAK '25: Proceedings of the 15th International Learning Analytics and Knowledge Conference. New York: Association for Computing Machinery; 726–737.
5. Gedrimiene, E., Celik, I., Mäkitalo, K., & Muukkonen, H. (2023). Transparency and Trustworthiness in User Intentions to Follow Career Recommendations from a Learning Analytics Tool. Journal of Learning Analytics, 10(1), 54–70.
6. Lim, L.; Bannert, M.; van der Graaf, J.; Fan, Y.; Rakovic, M.; Singh, S.; Molenaar, I.; Gašević, D. (2024). How do students learn with real-time personalized scaffolds?. British Journal of Educational Technology, 55, 1309–1327.
7. Ochoa, X.; Huang, X.; Shao, Y. (2025). Exploring the Potential of Generative AI to Support Non-experts in Learning Analytics Practice. Journal of Learning Analytics, 12(1), 65–90.
8. Rodrigues, L.; Xavier, C.; Costy, N.; Gašević, D.; Ferreira Mello, R. (2025). Is GPT-4 fair? An empirical analysis in automatic short answer grading. Computers and Education: Artificial Intelligence, 8, 100428.
9. Raković, M.; Li, Y.; Mohammadi Foumani, N.; Salehi, M.; Kuhlmann, L.; Mackellar, G.; Martinez-Maldonado, R.; Haffari, G.; Swiecki, Z.; Li, X.; Chen, G.; Gašević, D. (2024). Measuring Affective and Motivational States as Conditions for Cognitive and Metacognitive Processing in Self-Regulated Learning. LAK '24: Proceedings of the 14th Learning Analytics and Knowledge Conference. New York: Association for Computing Machinery; 701–712.
10. Svetec, B.; Divjak, B.; Horvat, D.; Pažur Aničić, K. (2024). Predicting the Outcomes of PBL: Comparison of Two Methods. 35th International Scientific Conference CECIIS 2024. Varaždin: University of Zagreb, Faculty of Organization and Informatics; 325–332.