Relevant themes: learning analytics in general and its development in higher education
Learning analytics is a topic I am very interested in; mainly because it heavily relates to my MA thesis and because it’s a growing field in online learning so it has caught my attention a while back. I’ve had a few discussions about the limitations of learning analytics explaining meaningful learning with my colleagues throughout the past few months, in addition to the discussion we had in class, and here are some key points that were highlighted, along with my reflections.
The topic seems to be growing very rapidly to the point that people are in the field are giving it massive importance. Understandable, because of the huge benefit that it offers in terms of showing patterns of online learning behavior. They could be really good for early intervention and taking action if a student is identified to be progressing slowly or is behind on any of the activities. This is one of the benefits that I believe to be absolutely fascinating, that wouldn’t necessarily be present in a traditional face to face classroom, or even one that is web-enhanced but does not use an LMS or online platform that tracks student learning, completion of activities or assessments.
An important angle to highlight is the benefit of learning analytics in showing patterns of certain behavior online. This is one of the ways it could show an instructor indications of real or meaningful learning, or lack thereof. For example, if one student is shown to take a long time to take a quiz than is necessary or simply hovers over a couple of questions for a long time, then that’s not necessarily an indication of anything; the student could have been interrupted, went to grab something to eat, or simply just lost focus at that given point in time. If, however, the entire or the majority of the class appear to take longer time than necessary or hover for a few minutes over the same questions, then that could be an indication that these concepts were either not clearly explained or the students have something in common that is stopping them for answering the questions or grasping the content well enough. It is not the one or two students, it’s the common and pattern-indicating behaviors that could really explain what is going on. This is where we can say “co-incidences don’t happen.” It is the instructor’s or the instructional designer’s job to look at these numbers and attempt to find these patterns. But not only that, it goes a step beyond that; it’s their job to appropriately interpret these results. It takes someone who knows the students, the course content, how everything is progressing to be able to make these connections and explain these patterns in a proper and meaningful (and contextual) interpretation of that phenomenon.
An aspect to consider (which is an advantage as well as a disadvantage in my opinion) is the relative novelty of learning analytics as an idea and as a practical solution for some of the higher education problems (oh, this reminds me of another thing, which I’ll mention right after this point). The fact that it’s this new of a field, means that innovations and research is quickly growing to form a good base for the idea to take off and explained. But the fact that it’s new also means there aren’t that many people familiar with how to collect and appropriately analyze the data for meaningful interpretations (this is also mentioned in the readings we did this week, referenced below). So this novelty also means there isn’t enough training on it like there is on other educational practices that have been around for years, which is normal because these things take time to understand and master. We just need to be careful of that.
Last but not least, (I have so much to say about this particular topic but don’t want to elongate this post that much), is this misconception, or rather over-estimated conception, that learning analytics can solve issues in higher education. Definitely and to an extent, it can solve some issues in higher education both administrative and instructional (mentioned also in the references below), but it would be too utopian to think of it as a tool that could solve massive issues or tackle problems that have been in educational systems for years. We cannot simply magnify the benefits of learning analytics like this, we should be rational in thinking the benefit scope and potential.
That’s it for now, next week is all about mindfulness in education, which I am super excited to both read and write about, so stay tuned 🙂
Picciano, A. (2012). The evolution of big data and learning analytics in American higher education. Journal of Asynchronous Learning Networks, 16(3), 9-20
Tulasi, B. (2014). Learning analytics and big data in higher education. International Journal of Engineering Research and Technology (IJERT), 3(1), 3377-3383