ABOUT
The Learning Analytics (LA) Lab of the University of Zagreb's Faculty of Organization and Informatics comprises an interdisciplinary group of researchers and practitioners dealing with learning analytics, academic analytics, and related areas such as learning design. The team continuously publishes the results of their competitive research, develops cooperation and works on projects at both national and international level. The Lab is the central place for learning analytics research in Croatia, with its key researchers also acclaimed in the international learning analytics community.
LAB'S ACTIVITIES
ABOUT LEARNING ANALYTICS
Learning analytics (LA) is a relatively new, but already propulsive and influential interdisciplinary field (Divjak & Maretić, 2017) bringing together research and practice in education, data science and psychology (Tsai et al., 2018). According to the definition adopted by the Society for LA Research (SOLAR), LA is “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs” (SOLAR, n. d.). As such, LA provides valuable insights for teachers and students, but also for instructional designers, institutional leaders and administration, and researchers.
While evidence has been used to enhance teaching and learning for many years, LA brings novelty by making use of new digital data on students’ learning activity and analysis techniques from data science and AI (SOLAR, n. d.). LA analyses data produced predominantly (though not exclusively) in students’ interactions with ICT, primarily with learning management systems (LMS) (e.g. Moodle) (Divjak & Maretić, 2017). New developments are related to using multimodal data from different sources, including data from digital, but also physical environments (Yan et al., 2022). LA are increasingly used to support sound learning design (Rienties et al., 2017), in particular in ensuring constructive alignment between learning outcomes, teaching and learning activities and assessment (Divjak et al., 2023b). LA turns data into “insights, decisions, and actions to improve learning and teaching” (Chatti et al., 2020). It is applicable in formal, but also non-formal education, and informal learning (Divjak & Maretić, 2017), including industrial training.
With online teaching and learning more widespread than ever, new opportunities for analyzing and directing students’ learning behavior have been emerging. Higher education institutions (HEI) are setting up learning analytics (LA) systems, to better understand and support students’ learning (...) by analyzing their learning data. Insights into students’ learning processes provided by LA can be helpful for various stakeholders, including teachers, instructional designers, institutional leaders, administrators, researchers, and most importantly, students. (Divjak et al., 2023a) There is a growing interest in coordinating learning design with LA, as the two can mutually provide valuable input. (Divjak et al., 2022)
PROJECTS
TRUELA
Trustworthy Learning Analytics and Artificial Intelligence for Sound Learning Design
iLed
Innovating Learning Design in Higher Education
RAPIDE
Relevant Assessment and Pedagogies for Inclusive Digital Education
eDESK
Digital and Entrepreneurial Skills for European Teachers
HigherDecision
Development of a Methodological Framework for Strategic Decision-Making in Higher Education
e-Schools
Establishing a System for Developing Digitally Mature Schools
HELA
Improving HEI Maturity to Implement Learning Analytics
MERIA
Mathematics Education: Relevant, Interesting and Applicable
MEMBERSHIP IN ORGANIZATIONS
SOLAR
Society for Learning Analytics Research
GRAILE
Global Research Alliance for AI in Learning and Education
References:
- Chatti, M. A., Muslim, A., Guesmi, M., Richtscheid, F., Nasimi, D., Shahin, A., & Damera, R. (2020). How to Design Effective Learning Analytics Indicators? A Human-Centered Design Approach (pp. 303–317). https://doi.org/10.1007/978-3-030-57717-9_22
- Divjak, B., & Maretić, M. (2017). Learning Analytics for Peer-assessment. Journal of Information and Organizational Sciences, 41(1), 21–34. https://doi.org/10.31341/jios.41.1.2
- Divjak, B., Grabar, D., Svetec, B., & Vondra, P. (2022). Balanced Learning Design Planning: Concept and Tool. Journal of Information and Organizational Sciences, 46(2). https://doi.org/10.31341/jios.46.2.6
- Divjak, B., Svetec, B., & Horvat, D. (2023a). Learning analytics dashboards: What do students actually ask for? LAK23: 13th International Learning Analytics and Knowledge Conference, 44–56. https://doi.org/10.1145/3576050.3576141
- Divjak, B., Svetec, B., Horvat, D., & Kadoić, N. (2023b). Assessment validity and learning analytics as prerequisites for ensuring student‐centred learning design. British Journal of Educational Technology, 54(1), 313–334. https://doi.org/10.1111/bjet.13290
- Rienties B., Nguyen Q., Holmes W., et al. (2017) A review of ten years of implementation and research in aligning learning design with learning analytics at the Open University UK. Interaction Design and Architecture(s) Journal, 33, 134-154.
- Society for Learning Analytics Research. (n.d.). What is learning analytics? Retreived on 7 April 2022 from https://www.solaresearch.org/about/what-is-learning-analytics/
- Tsai, Y.-S., Moreno-Marcos, P. M., Jivet, I., Scheffel, M., Tammets, K., Kollom, K., & Gašević, D. (2018). The SHEILA Framework: Informing Institutional Strategies and Policy Processes of Learning Analytics. Journal of Learning Analytics, 5(3). https://doi.org/10.18608/jla.2018.53.2
- Yan, L., Zhao, L., Gasevic, D., & Martinez-Maldonado, R. (2022). Scalability, Sustainability, and Ethicality of Multimodal Learning Analytics. LAK22: 12th International Learning Analytics and Knowledge Conference, 13–23. https://doi.org/10.1145/3506860.3506862
