Promoting and Assessing Collaborative Learning using Learning Analytics in Higher Education– Overview of Drivers and Wheels
DOI:
https://doi.org/10.37134/ejsmt.vol12.1.6.2025Keywords:
learning analytics, collaborative learning, higher educationAbstract
Learning analytics leverages the use of technology to gather and analyse data about student performance, engagement, and learning behaviours, which can help educators make informed decisions about how to improve learning outcomes. In higher education, learning analytics can provide insights into student engagement, performance, and learning pathways. Collaborative learning in higher education involves a group of students working together towards a common goal or task. Collaborative learning encourages students to work together to solve problems, analyse information, and make decisions, especially in self-directed learning environment. Through discussion and debate, students develop critical thinking skills and learn to approach problems from different perspectives to enhance problem-solving skills. Collaborative learning provides opportunities for students to work with others from diverse backgrounds, helping them develop interpersonal skills such as communication, teamwork, and leadership. While collaborative learning helps students to foster critical thinking and develop interpersonal skills, students’ activities and engagement in collaborative learning are not properly assessed and measured. Student performance indicators are highly dependent on the learning activities and resources used in the learning management system based on individual basis. The ability and potential of learning analytics to track students’ behaviour and performance in team, and to monitor the effectiveness of their sharing and communication is not fully utilised in higher education. This paper addresses this issue and aims to provide an overview of collaborative learning analytics. The overview elaborates essential elements in collaborative learning and defines features in analytics to support collaborative learning. The overview is expected to guide to educators and developers in promoting and assessing students’ performance based on collaborative works.
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References
J. Naujokaitiene, G. Tamoliune, A. Volungeviciene, and J. M. Duart, “Using learning analytics to engage students: Improving teaching practices through informed interactions,” J. New Approaches Educ. Res., vol. 9, no. 2, pp. 231–244, 2020, doi: 10.7821/naer.2020.7.561.
P. Fisk, “Education 4.0 … the future of learning will be dramatically different, in school and throughout life.,” http://www.thege¬niusworks.com/2017/01/future-education-young-ev¬eryone-taught-together, 2017. .
S. Dawson, N. Mirriahi, and D. Gasevic, “Importance of Theory in Learning Analytics in Formal and Workplace Settings,” J. Learn. Anal., vol. 2, no. 2, pp. 1–4, 2015, doi: 10.18608/jla.2015.22.1.
K. Karatas and I. Arpaci, “The role of self-directed learning, metacognition, and 21st century skills predicting the readiness for online learning,” Contemp. Educ. Technol., vol. 13, no. 3, 2021, doi: 10.30935/cedtech/10786.
Z. Muhammad Izzat Izzuddin and M. J. Hairulliza, “Designing and Incorporating Personalized Learning Analytics: Examining Self-Regulated Meaningful Learning,” Int. J. Acad. Res. Bus. Soc. Sci., vol. 11, no. 12, pp. 2274–2284, 2021, doi: 10.6007/ijarbss/v11-i12/11327.
C. Vásquez, I. García‐alonso, M. J. Seckel, and Á. Alsina, “Education for sustainable development in primary education textbooks—an educational approach from statistical and probabilistic literacy,” Sustain., vol. 13, no. 6, 2021, doi: 10.3390/su13063115.
M. J. Davis, J. A. Raines, C. L. Benson, C. H. McDonald, and R. A. Altizer, “Toward a framework for developing virtual reality skills training in human services,” J. Technol. Hum. Serv., vol. 39, no. 3, pp. 295–313, 2021, doi: 10.1080/15228835.2021.1915928.
W. Ho, K. N. Pham, and D. L. Bui, “Development of a mind map system integrating full moodle function,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 8, no. 4–2, pp. 1501–1506, 2018.
B. T. M. Wong, “Learning analytics in higher education: an analysis of case studies,” Asian Assoc. Open Univ. J., vol. 12, no. 1, pp. 21–40, 2017, doi: 10.1108/aaouj-01-2017-0009.
M. M. Abed and S. Dalbir, “Clickstream data schema for learning analytics to understand learner behaviour,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 5, pp. 7223–7229, 2020, doi: 10.30534/ijatcse/2020/49952020.
Y. Chen, A. Saleh, C. E. Hmelo-Silver, K. Glazewski, B. W. Mott, and J. C. Lester, “Supporting collaboration: From learning analytics to teacher dashboards,” Comput. Collab. Learn. Conf. CSCL, vol. 3, no. 2019, pp. 1689–1692, 2020.
D. Spikol, “Using Multimodal Learning Analytics to Identify Aspects of Collaboration in Project-Based Learning Introduction PELARS system and context,” in International Conference on Computer Supported Collaborative Learning, 2017, pp. 263–270.
M. Saqr, “Using Learning Analytics to Understand and Support Collaborative Learning. Stockholm University PhD Thesis,” 2018.
Ni. Seidel, H. Karolyi, M. Burchart, and C. de Witt, “Approaching Adaptive Support for Self-Regulated Learning,” Adv. Intell. Syst. Comput., no. June, p. in print, 2021.
Y. A. S. Anwar, “The Multilevel Inquiry Approach to Achieving Meaningful Learning in Biochemistry Course,” Biochem. Mol. Biol. Educ., vol. 48, no. 1, pp. 28–37, 2020, doi: 10.1002/bmb.21309.
C. Schumacher, Linking Assessment and Learning Analytics to Support Learning Processes in Higher Education. 2020.
T. Srimadhaven, A. V. Chris Junni, N. Harshith, S. Jessenth Ebenezer, S. Shabari Girish, and M. Priyaadharshini, “Learning analytics: Virtual reality for programming course in higher education,” Procedia Comput. Sci., vol. 172, no. 2019, pp. 433–437, 2020, doi: 10.1016/j.procs.2020.05.095.
S. Urbina, “Self-Regulated Learning and Technology-Enhanced Learning Environments in Higher Education : A Scoping Review,” 2021.
E. B. G. De Sousa, B. Alexandre, and R. F. Mello, “Applications of Learning Analytics in High Schools : A Systematic Literature Review,” Front. Artif. Intell., vol. 4, no. September, pp. 1–14, 2021, doi: 10.3389/frai.2021.737891.
J. A. N. Ansari and N. A. Khan, “Exploring the role of social media in collaborative learning the new domain of learning,” Smart Learn. Environ., vol. 7, no. 1, 2020, doi: 10.1186/s40561-020-00118-7.
A. Chelvarayan, C. W. Min, and H. Hashim, “Social Media for Academic Purpose: The Influencing Factors,” Ann. Rom. Soc. Cell Biol., vol. 25, no. 3, pp. 4549–4562, 2021, [Online]. Available: https://login.lp.hscl.ufl.edu/login?url=https://www.proquest.com/scholarly-journals/social-media-academic-purpose-influencing-factors/docview/2565218035/se-2?accountid=10920%0Ahttps://media.proquest.com/media/hms/PFT/1/1c1PK?_a=ChgyMDIyMDgyMzExNDUyMTA4Njo.
L. Zheng, M. Long, J. Niu, and L. Zhong, “An automated group learning engagement analysis and feedback approach to promoting collaborative knowledge building, group performance, and socially shared regulation in CSCL,” Int. J. Comput. Collab. Learn., no. 19, pp. 101–133, 2023, doi: 10.1007/s11412-023-09386-0.
M. Korir, S. Slade, W. Holmes, Y. Héliot, and B. Rienties, “Investigating the dimensions of students’ privacy concern in the collection, use and sharing of data for learning analytics,” Comput. Hum. Behav. Reports, vol. 9, no. December 2021, 2023, doi: 10.1016/j.chbr.2022.100262.
R. Martinez-Maldonado, “A handheld classroom dashboard: Teachers’ perspectives on the use of real-time collaborative learning analytics,” Int. J. Comput. Collab. Learn., vol. 14, no. 3, pp. 383–411, 2019, doi: 10.1007/s11412-019-09308-z.
A. F. Wise, S. Knight, and S. B. Shum, “Collaborative Learning Analytics,” in Cress, U., Rosé C., Wise, A. & Oshima, J. (Eds.), International Handbook of Computer-Supported Collaborative Learning (Springer), 2021, pp. 1–19.
I. S. M. Ramli, S. M. Maat, and F. Khalid, “Learning Analytics in Mathematics: A Systematic Review,” Int. J. Acad. Res. Progress. Educ. Dev., vol. 8, no. 4, pp. 436–449, 2019, doi: 10.6007/ijarped/v8-i4/6563.
Y. S. Mian, F. Khalid, A. W. C. Qun, and S. S. Ismail, “Learning Analytics in Education, Advantages and Issues: A Systematic Literature Review,” Creat. Educ., vol. 13, no. 09, pp. 2913–2920, 2022, doi: 10.4236/ce.2022.139183.
M. Liu, A. Pardo, I. Engineering, and L. Liu, “Using Learning Analytics to Support Engagement in Collaborative Writing,” Int. J. Distance Educ. Technol., vol. 15, no. 4, pp. 79–98, 2017, doi: 10.4018/IJDET.2017100105.
M.-C. Liu and Y.-M. Huang, “The use of data science for education: The case of social-emotional learning,” Smart Learn. Environ., vol. 4, no. 1, pp. 1–13, 2017, doi: 10.1186/s40561-016-0040-4.
O. Viberg, M. Khalil, and M. Baars, “Self-regulated learning and learning analytics in online learning environments: A review of empirical research,” ACM Int. Conf. Proceeding Ser., no. March, pp. 524–533, 2020, doi: 10.1145/3375462.3375483.
A. Nguyen, L. Gardner, and D. Sheridan, “Data Analytics in Higher Education : An Integrated View,” J. Inf. Syst. Educ., vol. 31, no. 1, pp. 2–13, 2020.
O. K. Xin and D. Singh, “Development of Learning Analytics Dashboard based on Moodle Learning Management System,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 7, pp. 838–843, 2021, doi: 10.14569/IJACSA.2021.0120793.
C. Vieira, P. Parsons, and V. Byrd, “Visual learning analytics of educational data: A systematic literature review and research agenda,” Comput. Educ., vol. 122, pp. 119–135, 2018, doi: 10.1016/j.compedu.2018.03.018.
M. Axelsen, P. Redmond, E. Heinrich, and M. Henderson, “The evolving field of learning analytics research in higher education: From data analysis to theory generation, an agenda for future research,” Australas. J. Educ. Technol., vol. 36, no. 2, pp. 1–7, 2020, doi: 10.14742/AJET.6266.
Y. Fan, W. Matcha, N. A. Uzir, Q. Wang, and D. Gašević, “Learning Analytics to Reveal Links Between Learning Design and Self-Regulated Learning,” Int. J. Artif. Intell. Educ., vol. 31, no. 4, pp. 980–1021, 2021, doi: 10.1007/s40593-021-00249-z.
T. M. Kuo, C. C. Tsai, and J. C. Wang, “Linking web-based learning self-efficacy and learning engagement in MOOCs: The role of online academic hardiness,” Internet High. Educ., vol. 51, no. June, p. 100819, 2021, doi: 10.1016/j.iheduc.2021.100819.
L. Corrin et al., Completing the Loop: Returning Meaningful Learning Analytic Data to Teachers. Sydney: Office for Learning and Teaching., 2016.
Z. Muhammad Izzat Izzuddin and M. J. Hairulliza, “Personalised Learning Analytics: Promoting Student’s Achievement and Enhancing Instructor’s Intervention in Self-regulated Meaningful Learning,” Int. J. Inf. Educ. Technol., vol. 12, no. 11, pp. 1243–1247, 2022, doi: 10.18178/ijiet.2022.12.11.1745.
E. Dixon-román, T. P. Nichols, and A. Nyame-mensah, “The racializing forces of / in AI educational technologies,” Learn. Media Technol., vol. 00, no. 0, pp. 1–15, 2020, doi: 10.1080/17439884.2020.1667825.
B. Quadir, M. Chang, and J. Chi, “Categorizing learning analytics models according to their goals and identifying their relevant components : A review of the learning analytics literature from 2011 to 2019,” Comput. Educ. Artif. Intell., vol. 2, p. 100034, 2021, doi: 10.1016/j.caeai.2021.100034.
A. P. Montgomery, D. V Hayward, W. Dunn, M. Carbonaro, and C. G. Amrhein, “Blending for student engagement : Lessons learned for MOOCs and beyond,” Australas. J. Educ. Technol., vol. 31, no. 6, pp. 657–670, 2015.
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