Title: Samostalno istraživanje: Primjeri analitike učenja
Course: Analitike učenja > Section: Analitika učenja: definicija, karakteristike, svrha primjene

Polaznici će dobiti popis od 10-ak relevantnih znanstvenih radova iz područja analitike učenja, s konkretnim primjerima analitike učenja. Koristeći se AI alatima (npr. Notebook LM, Perplexity), trebaju proučiti sažetke radova.  S popisa će odabrati 2 rada koja će u cijelosti pročitati. Umjesto toga mogu i samostalno istražiti dodatne radove koje će pročitati.

Moodle resurs: Stranica/Mapa

Vanjski alati: NotebookLM, Perplexity ili drugi slični AI alati

U nastavku se nalazi popis s poveznicama na 10 recentnih znanstvenih radova iz područja analitike učenja s različitim primjerima analitike učenja.

- Proučite sažetke radova, pri čemu se možete koristiti alatima umjetne inteligencije poput Notebook LM-a.
- Na temelju toga odaberite dva rada i u cijelosti ih pročitajte.
- Umjesto navedenih radova možete i samostalno istražiti dodatne radove koje ćete pročitati.
- Možete pogledati i blog na kojem se objavljuju istraživanja u analitici učenja na način razumljiv široj publici.
- Predviđeno potrebno vrijeme za ovu aktivnost je 4 sata.

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.

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.

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.

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.

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.

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.

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.

Rodrigues, L.; Xavier, C.; Costy, N.; Gasevic, D.; Ferreira Mello, R. (2025). Is GPT-4 fair? An empirical analysis in automatic short answer grading. Computers and Education: Artificial Intelligence, 8, 100428.

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.

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.