“Differentiated instruction can become an experienced reality for students, with purposefully-designed LA serving to compress, rather than exacerbate, the learning and achievement gap between thriving and struggling students.” – Tan, Yang, Koh, & Jonathan (2016).
Recently, with the rise of learning analytics implementations, more and more student-facing learning analytics tools, or systems were devised and implemented with a goal to support learning and instruction (e.g., Chen, Chang, Ouyang, & Zhou, 2018; Dado & Bodemer, 2018). However, a literature review (Jivet, Scheffel, Drachsler, & Specht, 2017) indicated that a majority of current LA tools or platforms enhanced social comparison and competition rather than collaboration, inquiry and mastery. One of my previous study (Ouyang & Chang, 2018) also indicated that LA tools or reports might discourage student engagement. This study results showed that socially peripheral students did contribute to deep-level knowledge inquiry but did not get enough peer responses to further build up knowledge construction. I imagine that when those students were provided with their low-level social interaction information via LA tools, their motivation for taking actions on further engagement may be discouraged. Therefore, the design and implementation of LA tools should build strong connections to learning science theories and pedagogical strategies.
One potential LA tool design principle Jivet et al. (2017) proposed was that LA tools could be designed with different motivating factors that better fit learners with different needs and performance levels. LA implementations should serve as a tool grounded upon learning theories and sided with pedagogical strategies, rather than analytics of data at hand for its own sake.
I recently designed an interactive social learning analytics tool called IntVisRep to demonstrate three types of representation of online discussion data: interaction networks, keyword flows, and temporal online presences. This tool aimed to help learners become aware of their interaction, discourse and cognition processes, and further adjust their participation and collaboration accordingly during online collaborative discussions. This is tool is at the initial stage. As I mentioned before, next step is to revise this tool to better support students’ learning rather than exacerbate comparison among students. Also information about students’ progress – changes of their engagement – should be integrated in this tool. In the future research, I will use Canvas or Moodle API to capture real-time data from learners and generate interactive representations directly. Moreover, I will examine whether and how the use of IntVisRep would influence learners’ learning processes such as social interaction, topic contribution, and online presence.
Chen, B., Chang, Y. H., Ouyang, F., & Zhou, W. Y. (2018). Fostering discussion engagement through social learning analytics. The Internet and Higher Education, 37, 21–30.
Dado, M., & Bodemer, D. (2018). Social and cognitive group awareness to aid argumentation about socially acute questions on social media. In J., Kay, and R. Luckin (Eds.) Rethinking Learning in the Digital Age: Making the Learning Sciences Count, 13th International Conference of the Learning Sciences (ICLS) 2018, Volume 1. (pp. 456-463). London, UK: International Society of the Learning Sciences.
Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness is not enough: Pitfalls of learning analytics dashboards in the educational practice. In E. Lavoué, H. Drachsler, K. Verbert, J. Broisin, & M. Pérez-Sanagustin (Eds.), EC-TEL 2017: Data Driven Approaches in Digital Education (Vol. 10474, pp. 82–96). Tallinn, Estonia: Springer.
Ouyang, F. & Chang, Y. H. (2018). The relationship between social participatory role and cognitive engagement level in online discussions. British Journal of Educational Technology.
Tan, J.P.L., Yang, S., Koh, E., Jonathan, C. (2016). Fostering 21st century literacies through a collaborative critical reading and learning analytics environment: user perceived benefits and problematics. In Proceedings of LAK 2016, pp. 430–434. ACM