The promise of learning analytics

foto-blog-bas-giesbersFoto: Beeldbank EUR / Michelle Muus.

Have you ever wondered what it would be like to have insights in the actual learning behaviour of your students? To see how often they log into the learning management system, which resources and activities they engage with, and for how long, and how this information connects to their performance? Wouldn’t it be helpful if students could have a dashboard with an insightful graphical overview of their learning activity, including comparison to their peers, and get an accurate prediction on the likelihood to pass a course given their current activity level and performance? These are but a few ideas triggered by applying Big Data ideas to education, of which learning analytics (LA) is a reflection. LA is a very young field, which hold many promises for the improvement of learning and instruction. As with any young field, the recognition of potential is accompanied by concerns as well.

As mentioned, LA is a reflection of Big Data applied to education, so let us start there. Because of the increased use of technology, a tremendous amount of data (digital footprints) becomes available on a daily basis that reflect the usage of, and interaction with, technology. The term ‘Big Data’ is a most appropriate umbrella term that concerns all large datasets that in fact are too large or too complex to be analysed using conventional data processing and analysis tools. Combined with the current increase in data quality, standardised data formats, processing power, and analytics tools, it has become easier to gain meaningful insights.

A translation of the above to education is not a far stretch.  After all, technology usage in education has greatly increased as well, leading to questions like those listed in the opening of this post. Though multiple definitions of LA exist, one of the most widely used is the following:  “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.”

I think there are many promising developments with respect to LA that have great potential to improve both learning and instruction. In this early stage however, it is not possible to provide rock solid evidence of proven LA practice, but the number of successful applications and studies is growing. Many studies and implementations regarding LA focus on the right (quality and combinations of) data to use, optimizing algorithms, creating student and teacher dashboards to display LA results, etc. In addition, the amount of available tooling also is growing. For example, most suppliers of learning management systems nowadays offer some form of integrated LA application and resulting case studies are very promising.

Next to promising research results and successful implementations, there are issues that require careful thought and discussion. These are nicely paraphrased here as: availability, accessibility, and interoperability of data, resistance from users, impact on professional roles, and the distinction between hype and proven practice. As already indicated, legislation and privacy are two elements that play a prominent role in these discussions.


Writing from my personal background as an educational psychologist and learning innovation consultant, I find LA a very interesting topic both from a practical as from a research perspective.

The interest in LA research at EUR also is growing, not in the least because of the development of online and blended learning initiatives. Within the Online Education Visioning Initiative, big data/LA approaches have been defined as one of the focal points for the coming years. In addition, an EUR workgroup on research on open and online education recently formed to define a research agenda for open and online education, which also includes LA as one of its foci.

One of the LA related studies I am involved in at RSM aims to gain insight in usage behaviour in terms of spacing and cramming of two types of video (worked assignment videos vs. lecture recordings), the best suitable method to analyse this, and the relation with student performance (Giesbers, van Dalen, van Baalen, & Flipsen, 2015). We found a clear relation between middle and low performance, choice of video type, and cramming behaviour in watching both types of video, by comparing three methods of analysis of which two showed to be most useful to analyse spacing and cramming.

Currently, a new study started that aims to investigate learner behaviour and performance in the context of a blended BSc 3 course in Innovation Management, which combined the campus-based course with the Innovation Management MOOC. In this study, MOOC data will be combined with performance data and survey-generated data on self-regulation. Data analysis is not complete at this point in time, so no findings can be presented yet.

Furthermore, several students (e.g., from the MSc Business Information Management) take a LA approach in their MSc thesis. Examples of findings are a positive relation between Facebook usage and academic and social integration, while Blackboard clickdata and survey data on self-regulation showed little predictive power (Amy Zhou, 2016). In addition, analysis of data derived from the Econometrics MOOC showed a positive relation between teacher activity and student performance, and a positive relation between self-assessment and feedback in a certain week on the level of study activity in the consecutive week (Job Deibel, 2016).

Closing remarks

In sum, from both a practical perspective as well as a research perspective, LA is highly interesting, and has a lot of potential to improve learning and instruction in general and at EUR. In my view, two things are essential when looking for application of LA: First, it is crucial to define clear goals and questions that LA may help to answer. Second, thorough discussion regarding the mentioned issues and constraints is needed, and policy regarding these issues needs to be developed.

If you are interested to learn more, I listed a number of projects and online communities where you can find research insights, examples of successful LA practices, and connect and discuss with other scholars and practitioners. A list of LA related MSc thesis that I know of also is included.


Giesbers, B., van Baalen, P., van Dalen, J., & Flipsen, M. (2015, August). Spacing and cramming in webcast usage: A learning analytics approach. Paper presented at the bi-annual conference of the European Association for Research in Learning and Instruction, Limassol, Cyprus. Abstract retrieved from here.

About Bas Giesbersbas-giesbers

Bas Giesbers is learning innovation consultant and researcher within Rotterdam School of Management’s Learning Innovation Team. After studying educational psychology, he gained experience in educational technology, teaching, teacher professionalisation and research in the context of both on-site and (remedial) distance education. In the learning innovation team of RSM, he works on the innovation of the whole educational portfolio in collaboration with teaching faculty and program directors. This includes educational (re)design toward blended models of delivery, providing advice to management and faculty, teacher professionalization, and participation in project teams that work on a variety of projects such as digital assessment and the development of multimedia learning material like video.

He obtained a PhD in educational psychology at Maastricht University (2013), on a dissertation that focused on the use of synchronous and asynchronous communication in distance education, and the role of motivation and learner interaction. His current research interests lie in the field of online collaborative learning, synchronous and asynchronous online communication, motivation, and the use of learning analytics to understand and improve learning.

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