Collaboration Analytics in Higher Education and Professional Contexts: A Systematic Review of Approaches and Theoretical Foundations

A. I. Kutuzov; A. V. Bogdanova

doi:10.31992/0869-3617-2025-34-11-81-107

Collaboration Analytics in Higher Education and Professional Contexts: A Systematic Review of Approaches and Theoretical Foundations

A. I. Kutuzov, A. V. Bogdanova

https://doi.org/10.31992/0869-3617-2025-34-11-81-107

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Abstract

The article presents a review of studies in the field of Collaboration Analytics and their application to the analysis of collaborative activities in higher education. Based on a systematic search and comparison of works, key constructs of collaboration we identified – participation, coordination, and cognitive regulation – as well as approaches to their theoretical interpretation. The findings show that, in university practice, the most in-demand data include records of student activity on digital platforms (message logs, timestamps, relational links), results of content analysis of communication (discourse coding, semantic mapping), and multimodal indicators of interaction (e.g., time series of actions and distribution of roles within groups). To process these data, methods of network analysis, epistemic network analysis (ENA), machine learning, and digital trace visualization are actively employed. These approaches enable instructors to diagnose team dynamics, provide more accurate feedback, and support the development of students’ metacompetencies. As a comparative context, studies of corporate analytics were examined, which made it possible to identify models of monitoring and visualization of participants’ contributions that can potentially be transferred to the university setting. The practical significance of the results lies in providing educators and educational researchers with tools for the informed selection of analytical methods and indicators, as well as in outlining the possibilities of adapting corporate practices to university models of collaborative learning. For readers, the value of the article consists in systematizing fragmented research and identifying promising approaches that may enhance the quality of student group work and its assessment.

Keywords

collaboration analytics, cooperative analytics, computer-supported collaborative learning, CSCL, computer-supported cooperative work, CSCW, feedback

About the Authors

A. I. Kutuzov

National Research University Higher School of Economics; Togliatti State University
Russian Federation

Anton I. Kutuzov – Ph.D. Student; Institute of Education, Director of the Center for Educational Marketing

20 Myasnitskaya str., Moscow, 101000

14 Belorusskaya str., Samara region, Togliatti, 445020

A. V. Bogdanova

Togliatti State University
Russian Federation

Anna V. Bogdanova – Ph.D. (Pedagogical Sciences), Head of Online Education Technologies Department

Reseacher ID: GRO-7042-2022

14 Belorusskaya str., Samara region, Togliatti, 445020

References

1. Schneider B., Dowell, N., Thompson K. Collaboration analytics – current state and potential futures // Journal of Learning Analytics. 2021. Vol. 8. No. 1. P. 1–12. DOI: 10.18608/jla.2021.7447

2. Martinez-Maldonado R., Kay J., Shum S.B., Yacef K. Collocated collaboration analytics: Principles and dilemmas for mining multimodal interaction data // Human–Computer Interaction. 2017. Vol. 34. No. 1. P. 1–50. DOI: 10.1080/07370024.2017.1338956

3. Martinez-Maldonado R., Gašević D., Echeverria V., Fernandez Nieto G., Swiecki Z., Shum S.B. What Do You Mean by Collaboration Analytics? A Conceptual Model // Journal of Learning Analytics. 2021. Vol. 8. No. 1. P. 126–153. DOI: 10.18608/jla.2021.7227

4. Balyshev, P.A. (2023). Universal Psychological and Pedagogical Mechanisms of Developing Teamwork Competence in University Students. Mir nauki. Pedagogika i psikhologiya = World of Science. Pedagogy and Psychology. Vol. 11, no. 6. Available at: https://elibrary.ru/download/elibrary_63490007_16375863.pdf (accessed 14.10.2025). (In Russ., abstract in Eng.).

5. Guitert Catasús M., Romeu Fontanillas T., Raffaghelli J.E., Cerro Martínez J.P. Collaborative Learning Analytics: Centring the Ethical Implications around Teacher and Student Empowerment. A Systematic Review // Journal of Learning Analytics. 2025. Vol. 12. No. 1. P. 201–214. DOI: 10.18608/jla.2025.8489

6. Huang X., Ochoa X. Charting the Development of Collaboration Skills through Collaborative Learning Analytics Systems // Journal of Learning Analytics. 2025. Vol. 12. No. 1. P. 338–366. DOI: 10.18608/jla.2025.8523

7. Dillenbourg P., Järvelä S., Fischer F. The evolution of research on computer-supported collaborative learning: From design to orchestration // Technology-enhanced learning: Principles and products. Dordrecht: Springer Netherlands, 2009. P. 3–19. DOI: 10.1007/978-1-4020-9827-7_1

8. Schubert P. Areas of Collaboration Analytics // Procedia Computer Science. 2025. Vol. 256. P. 458–471. DOI: 10.1016/j.procs.2025.02.142

9. Gašević D., Dawson S., Siemens G. Let’s not forget: Learning analytics are about learning // TechTrends. 2015. Vol. 59. No. 1. P. 64–71. DOI: 10.1007/s11528-014-0822-x

10. Ochoa X., Worsley M. Augmenting learning analytics with multimodal sensory data // Journal of Learning Analytics. 2016. Vol. 3. No. 2. P. 213– 219. DOI: 10.18608/jla.2016.32.10

11. Wise A.F., Shaffer D.W. Why theory matters more than ever in the age of big data // Journal of Learning Analytics. 2015. Vol. 2. No. 2. P. 5–13. DOI: 10.18608/jla.2015.22.2

12. Praharaj S., Scheffel M., Drachsler H., Specht M. Literature review on co-located collaboration modeling using multimodal learning analytics – Can we go the whole nine yards? // IEEE Transactions on Learning Technologies. 2021. Vol. 14. No. 3. P. 367–385. DOI: 10.1109/TLT.2021.3097766

13. Schneider B., Sung G., Chng E., Yang S. How can high-frequency sensors capture collaboration? A review of the empirical links between multimodal metrics and collaborative constructs // Sensors. 2021. Vol. 21. No. 24. P. 81–85. DOI: 10.3390/s21248185

14. Datsun, N.N. (2015). Joint Assessment of Student Activities in Massive Open Online Courses: A Systematic Literature Review. Mir nauki (elektronnyi nauchnyi zhurnal) [World of Science (Electronic Scientific Journal)]. No. 3, p. 10. Available at: https://elibrary.ru/download/elibrary_25399409_91577279.pdf (accessed 14.10.2025). (In Russ., abstract in Eng.).

15. Ruziboev, S.K.U., Demidov, A.V. (2025). Assessment of Learners’ Academic Behavior in E-Learning Systems: A Systematic Literature Review. Akademicheskaya nauka = Academic Science. No. 1 (January–March), pp. 135-140, doi: 10.24412/3034-4042-2025-2-135-140 (In Russ., abstract in Eng.).

16. Sukhanova, T.V. (2021). Development of Digital Educational Resources in Foreign Countries (USA, UK, China, Brazil, Germany): A Review. Tsennosti i smysly [Values and Meanings]. Vol. 4, no. 74, pp. 38-73, doi: 10.24412/2071-6427-2021-4-38-73 (In Russ.).

17. Swiecki Z. Measuring the impact of interdependence on individuals during collaborative problem-solving // Journal of Learning Analytics. 2021. Vol. 8. No. 1. P. 75–94. DOI: 10.18608/JLA.2021.7240

18. Vujovic M., Tassani S., Hernández-Leo D. Motion capture as an instrument in multimodal collaborative learning analytics // European Conference on Technology Enhanced Learning. Cham : Springer International Publishing, 2019. P. 604– 608. DOI: 10.1007/978-3-030-29736-7_49

19. Just M., Schubert P., Blatt J., Delfmann P. Data Preprocessing for Cross-System Analysis: The DaProXSA Approach // Procedia Computer Science. 2024. Vol. 239. P. 1635–1644. DOI: 10.1016/j.procs.2024.06.340

20. Zamecnik A., Kovanović V., Joksimović S., Grossmann G., Ladjal D., Pardo A. The perceptions of task cohesion in collaborative learning teams // International Journal of Computer-Supported Collaborative Learning. 2024. Vol. 19. P. 369–393. DOI: 10.1007/s11412-024-09424-5

21. Poquet O., Kitto K., Jovanović J., Dawson S., Siemens G., Markauskaite L. Transitions through lifelong learning: Implications for learning analytics // Computers and Education: Artificial Intelligence. 2021. Vol. 2. Article no. 100039. DOI: 10.1016/j.caeai.2021.100039

22. Liu Z., Liu H., Tian Y., Nie X., Liu M. Relationships between interactive network patterns and students’ cognitive and emotional processes: Evidence from the individual and group levels // Computers & Education. 2025. Vol. 232. P. 105– 109. DOI: 10.1016/j.compedu.2025.105309

23. Ouyang F., Zhang L., Wu M., Jiao P. Empowering collaborative knowledge construction through the implementation of a collaborative argument map tool // The Internet and Higher Education. 2024. Vol. 62. P. 100–146. DOI: 10.1016/j.iheduc.2024.100946

24. Saqr M., López-Pernas S. The curious case of centrality measures: A large-scale empirical investigation // Journal of Learning Analytics. 2022. Vol. 9. No. 1. P. 13–31. DOI: 10.18608/jla.2022.7415

25. Saqr M., López-Pernas S., Murphy K. How group structure, members’ interactions and teacher facilitation explain the emergence of roles in collaborative learning // Learning and Individual Differences. 2024. Vol. 112. Article no. 102463. DOI: 10.1016/j.lindif.2024.102463

26. Zhu X., Shui H., Chen B. Beyond reading together: Facilitating knowledge construction through participation roles and social annotation in college classrooms // The Internet and Higher Education. 2023. Vol. 59. Article no. 100919. DOI: 10.1016/j.iheduc.2023.100919

27. Ferreira M.A.D., Mello R.F., Kovanović V., Nascimento A., Lins R., Gašević D. NASC: Network analytics to uncover socio-cognitive discourse of student roles // LAK22: 12th International Learning Analytics and Knowledge Conference. 2022. P. 415–425. DOI: 10.1145/3506860.3506978

28. Bahles S., Schwade F, Schubert P. A Workspace Typology for Enterprise Collaboration Systems // Procedia Computer Science. 2022. Vol. 196. P. 296–304. DOI: 10.1016/j.procs.2021.12.017

29. Cornide-Reyes H., Riquelme F., Monsalves D., Noël R., Cechinel C. et al. A multimodal realtime feedback platform based on spoken interactions for remote active learning support //Sensors. 2020. Vol. 20. No. 21. Article no. 6337. DOI: 10.3390/s20216337

30. Worsley M., Anderson K., Melo N., Jang J.Y. Designing analytics for collaboration literacy and student empowerment // Journal of Learning Analytics. 2021. Vol. 8. No. 1. P. 30–48. DOI: 10.18608/JLA.2021.7242

31. Cornide-Reyes H., Noёl R., Riquelme F., Gajardo M., Cechinel C. et al. Introducing low-cost sensors into the classroom settings: Improving the assessment in agile practices with multimodal learning analytics // Sensors. 2019. Vol. 19. No. 15. Article no. 3291. DOI: 10.3390/s19153291

32. Charitopoulos A., Vassiliadis S., Rangoussi M., Koulouriotis D.E. E-learning and blended learning in textile engineering education: a closed feedback loop approach. IOP Conference Series: Materials Science and Engineering. 2017. Vol. 254. No. 22. Article no. 222002. DOI: 10.1088/1757-899X/254/22/222002

33. Alshawabkeh R.O., Abu Rumman A.R., Al-Abbadi L.H. The nexus between digital collaboration, analytics capability and supply chain resilience of the food processing industry in Jordan //Cogent Business & Management. 2024. Vol. 11. No. 1. Article no. 2296608. DOI: 10.1080/23311975.2023.2296608

34. Carvalho C.L., Pinto I.R., Costa-Lopes R., Páez D., Miranda M.P., Marques J.M. Social Dominance Orientation Boosts Collective Action Among Low-Status Groups // Frontiers in Psychology. 2021. Vol. 12. DOI: 10.3389/fp-syg.2021.681302

35. Guo Z., Barmaki R. Deep neural networks for collaborative learning analytics: Evaluating team collaborations using student gaze point prediction // Australasian Journal of Educational Technology. 2020. Vol. 36. No. 6. P. 53–71. DOI: 10.14742/AJET.6436

36. Spikol D., Li Z., Nolte A., Ohsaki A., Rapur K. Investigating Hackathons with Collaboration Analytics //Proceedings of the 8th International Conference on Game Jams, Hackathons and Game Creation Events. 2024. P. 1–8. DOI: 10.1145/3697789.3697797

37. Le Q., Hui B., Putri A., Phan K. Using GitHub Analytics to Assess the Quality of Collaboration in Software Engineering Teams // 2024 IEEE Frontiers in Education Conference (FIE). IEEE, 2024. P. 1–9. DOI: 10.1109/FIE61694.2024.10892887

38. Kutuzov, A.I., Bogdanova, A.V. (2025). Tools for Assessing Group and Teamwork of Students in Higher Education: A Systematic Literature Review. Vysshee obrazovanie v Rossii = Higher Education in Russia. No. 4, pp. 118-143, doi: 10.31992/0869-3617-2025-34-4-118-143 (In Russ., abstract In Eng.).

39. Oldeweme M., Konradt U., Brede M. The rhythm of teamwork: Discovering a complex temporal pattern of team processes // Group Dynamics: Theory, Research, and Practice. 2023. Vol. 27. No. 1. Article no. 50. DOI: 10.1037/gdn0000175

40. Lämsä J., Uribe P., Jiménez A., Caballero D. Deep Networks for Collaboration Analytics: Promoting Automatic Analysis of Face-to-Face Interaction in the Context of Inquiry-Based Learning // Journal of Learning Analytics. 2021. Vol. 8. No. 1. P. 113–125. DOI: 10.18608/jla.2021.7118

41. Lodge J.M., Knight S., Kitto K. Theory and Learning Analytics, a Historical Perspective // Bartimote K., Howard S.K., Gašević D. (eds) Theory Informing and Arising from Learning Analytics . Springer, Cham. 2024. P. 3–18. DOI: 10.1007/978-3-031-60571-0_1

42. Kitto K., Shum S., Gibson A. Embracing imperfection in learning analytics // Proceedings of the 8th international conference on learning analytics and knowledge. 2018. P. 451–460. DOI: 10.1145/3170358.3170413

43. Lockyer L., Heathcote E., Dawson S. Informing pedagogical action: Aligning learning analytics with learning design // American Behavioral Scientist. 2013. Vol. 57. No. 10. P. 1439–1459. DOI: 10.1177/0002764213479367

44. Тurvey K., Pachler N. Design principles for fostering pedagogical provenance through research in technology supported learning // Computers & Education. 2020. Vol. 146. Article no. 103736. DOI: 10.1016/j.compedu.2019.103736

45. Zhou Q., Suraworachet W., Pozdniakov S., Martinez-Maldonado R., Bartindale T. et al. Investigating students’ experiences with collaboration analytics for remote group meetings // International Conference on Artificial Intelligence in Education. Cham: Springer International Publishing, 2021. P. 472–485. DOI: 10.1007/978-3-030-78292-4_38

46. Patarakin, E.D., Vachkova, S.N. (2019). Network Analysis of Collective Actions on Digital Educational Objects. Vestnik MGPU. Seriya: Pedagogika i psikhologiya = Vestnik MGPU. Series: Pedagogy and Psychology. Vol. 4, no. 50, pp. 101-112, doi: 10.25688/2076-9121.2019.50.4.09