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Learning Analytics in Distance Education: A Systematic Literature Review
Selcan Kilis, Yasemin Gülbahar
Conclusion
This systematic review study indicates the overall disposition of existing literature on learning
analytics in distance education and provides research insight into the conceptual basis of this
recent emerging field. Content analysis has shown that research focused on log data stored in
the background of e-learning platforms or modules. This data was then analysed using
learning analytics techniques and data mining approaches, as well as structural equation
modelling and regression analysis. Learning analytics techniques and data mining approaches
also provide information about the learners’ disposition in regard to their centrality,
activeness, participation, interaction, and so on. The more important issue is the usage of such
results about students’ network disposition in the learning community in order to increase the
learning quality and its effectiveness as expected in learning analytics. However, stored data
from student logs could be better interpreted with the inclusion of other data such as real logs,
actual time spent and real interactions rather than logs and clicks, etc., since users could have
spent time in the learning environment for other purposes, i.e., not for learning purposes, but
maybe for surfing, or other misleading data types.
Stored data could be improved with more details to serve to the needs of educators, students,
and researchers. In the same way, the analysis techniques could be more sophisticated with
the addition of network analysis and sentiment analysis etc. The issue of understanding the
deeper learning processes is more than analysing just user ‘clicks’ and trying to interpret them.
Learning process include students’ efforts and performances as well as their psychological,
behavioural, and emotional states. Overall, special attention to the development of learning
analytics as a whole including the methods, techniques, culture, policies etc., is required in
order to promote the quality of learning and teaching in addition to guiding learners and/or
instructors rather than focusing solely on the outcomes via trivial measures.
References
1. Bainbridge, J., Melitski, J., & Zahradnik, A. (2015). Using Learning Analytics to Predict
At-Risk Students in Online Graduate Public Affairs and Administration Education.
Journal of Public Affairs Education, 21(2), 247-262.
2. Cambruzzi, W., Rigo, S. J., & Barbosa, J. L. W. (2015). Dropout prediction and reduction
in distance education courses with the learning analytics multitrail approach. Journal of
Universal Computer Science, 21(1), 23-47.
3. Fidalgo-Blanco, Á., Sein-Echaluce, M. L., García-Peñalvo, F. J., & Conde, M. Á. (2015).
Using Learning Analytics to improve teamwork assessment. Computers in Human
Behavior, 47, 149-156.
4. Goggins, S. P., Galyen, K. D., Petakovic, E., & Laffey, J. M. (2016). Connecting
performance to social structure and pedagogy as a pathway to scaling learning analytics in
MOOCs: an exploratory study. Journal of Computer Assisted Learning, 32(3), 244-266.
Reaching from the roots – 9 th EDEN Research Workshop Proceedings, 2016, Oldenburg 315
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