Abstract:

Learning management system (LMS) software is employed widely by higher education institutions to deliver courses of different formats, including online courses based on the collaborative learning approach. One of the functionalities that can provide the course instructor with valuable information about student learning online is learning analytics (LA) (Poon, Kong, Yau, Wong, & Ling, 2017), which can be defined as “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” (Ferguson, 2012, p. 305).

This becomes especially relevant in the context of online learning, where the course instructor may not receive any visual cues and must assess the learning process based on learners' written contributions in the discussion forum. Yet, even though LA have an immense potential to assist educators in student assessment, both in terms of participation and the level of cognitive engagement, this functionality often remain underutilized.

Therefore, the aim of this conceptual paper is threefold. Firstly, building upon earlier research, the paper offers a theoretical discussion on the potential of LA to assist the course instructor in gaining a better understanding of the different student roles emerging in a computer-supported collaborative learning (CSCL) course. It is demonstrated why a clear understanding of student roles and distinguishing between free-riders/lurkers and the so-called “passive members of the peripheral learning community" (Gasson & Waters, 2013) may be crucial for the instructor to provide effective scaffolding and ensure valid student assessment.

However, the LA functionality should be approached strategically. LA are data, and the task of interpreting what these data mean is left to individual educators. Therefore, the second aim of the paper is to discuss potential challenges the instructor may face when interpreting the LA data from a CSCL course. This discussion is supported by the data collected through a focus group interview with 14 (eight female and six male) Ugandan participants of an international online CSCL course. The discussion focuses on such typical challenges as students having lack of experience with the platform, access issues, and use of alternative platforms for informal learning. All of these may cause a risk of misinterpreting the data by the instructor.

Finally, the paper concludes with a reflective discussion on the strategic use of the LA data in the context of a CSCL course, emphasizing the importance of contextualizing the LA data and combining them with other information available to the instructor. Moreover, this discussion also raises the issue of ethics in the use of LA.

References:
[1] Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304-317.
[2] Gasson, S. & Waters, J. (2013). Using a grounded theory approach to study online collaboration behaviors. European Journal of Information Systems, 22, 95-118.
[3] Poon, L. K., Kong, S. C., Yau, T. S., Wong, M., & Ling, M. H. (2017). Learning analytics for monitoring students’ participation online: Visualizing navigational patterns on learning management system. In International Conference on Blended Learning (pp. 166-176). Springer, Cham.

Keywords:

Learning Analytics, Computer-supported Collaborative Learning (CSCL), Learning Management Systems (LMS), Student Roles, Student Assessment, Ethics.