In a recent Future Trends Forum discussion with Bryan Alexander, George Siemens expressed concern about lock-in: a situation in which technology investments become so integrated with the business practices of an institution that disentanglement becomes all but impossible. Where hyper-rationalized approaches to data-driven decision-making come together with inflexible technological ecosystems characterized by a lack of interoperability, what we end up with is a dystopian future in which colleges and universities are unable to change their investments.
On 20 August 2014, Civitas Learning announced the formation of a ten-member national advisory board, featuring several big names in the learning analytics space, including George Siemens, Linda Baer, and Tristan Denley. The board was created in support of the larger “Million More Mission,” which supports innovation aimed a producing a million more successful students each year. Without a more concrete description of the group’s specific aims, tasks, and responsibilities, the announcement sounds more like hype than anything else.
The shift to e-learning and online education requires new and different approaches for tracking student performance and behavior. Moodle is currently the leading virtual learning environment in Spain, and a lot of different plugins have appeared to complement the lack of native tools to track the learning process in all its aspects. However, none of these take the temporal dimension as a possible behavior measurement into consideration. In this case study, a new technical and statistical approach to time tracking of students in Moodle will be discussed, so that time as a behavioral aspect can be considered in further assessments. Furthermore, this approach could not only be applied to Moodle, but to all other online virtual learning environments.
Big data in undergraduate medical education that consist the medical curriculum are beyond human abilities to be perceived and analyzed. The medical curriculum is the main tool used by teachers and directors to plan, design and deliver teaching activities, assessment methods and student evaluation in medical education in a continuous effort to improve it. It remains unexploited mainly for medical education improvement purposes. The emerging research field of Visual Analytics has the advantage to combine data analysis and manipulation techniques, information and knowledge representation, and human cognitive strength to perceive and recognize visual patterns. Nevertheless, there is lack of findings reporting use and benefits of Visual Analytics in medical education. […]
This week, Ryan Baker posted a link to a piece, co-written with George Siemens, that is meant to function as an introduction to the fields of Educational Data Mining (EDM) and Learning Analytics (LA). “Educational Data Mining and Learning Analytics” is book chapter primarily concerned with methods and tools, and does an excellent job of summarizing some of the key similarities and differences between the two fields in this regard. However, in spite of the fact that the authors make a point of explicitly stating that EDM and LA are distinctly marked by an emphasis on making connections to educational theory and philosophy, the theoretical content of the piece is unfortunately quite sparse.
The tone of this work actually brings up some concerns that I have about EDM/LA as a whole. The authors observe that EDM and LA have been made possible, and have in fact been fueled, by (1) increases in technological capacity and (2) advances in business analytics that are readily adaptable to educational environments.
“The use of analytics in education has grown in recent years for four primary reasons: a substantial increase in data quantity, improved data formats, advances in computing, and increased sophistication of tools available for analytics”
The authors also make a point of highlighting the centrality of theory and philosophy in informing methods and interpretation.
“Both EDM and LA have a strong emphasis on connection to theory in the learning sciences and education philosophy…The theory-oriented perspective marks a departure of EDM and LA from technical approaches that use data as their sole guiding point”
My fear, however, which seems justified in light of the imbalance between theory and method in this chapter (a work meant to introduce, summarize, and so represent the two fields), is that the tools and methods that the fields have adopted, along with the technological- and business-oriented assumptions (and language) that those methods imply, have actually had a tendency to drive their educational philosophy. From their past work, I get the sense that Baker and Siemens would both agree that the educational / learning space differs markedly from the kind of spaces we encounter in IT and business more generally. If this is the case, I would like to see more reflection on the nature of those differences, and then to see various statistical and machine learning methods evaluated in terms of their relevance to educational environments as educational environments.
As a set of tools for “understanding and optimizing learning and the environments in which it occurs” (solaresearch.org), learning analytics should be driven, first and foremost, by an interest in learning. This means that each EDM/LA project should begin with a strong conception of what learning is, and of the types of learning that it wants to ‘optimize’ (a term that is, itself, imported from technical and business environments into the education/learning space, and which is not at all neutral). To my mind, however, basic ideas like ‘learning’ and ‘education’ have not been sufficiently theorized or conceptualized by the field. In the absence of such critical reflection on the nature of education, and on the extent to which learning can in fact be measured, it is impossible to say exactly what it is that EDM/LA are taking as their object. How can we measure something if we do not know what it is? How can we optimize something unless we know what it is for? In the absence of critical reflection, and of maintaining a constant eye on our object, it becomes all too easy to consider our object as if its contours are the same as the limits of our methods, when in actual fact we need to be vigilant in our appreciation of just how much of the learning space our methods leave untouched.
If it is true that the field of learning analytics has emerged as a result of, and is driven by, advancements in machine learning methods, computing power, and business intelligence, then I worry about the risk of mistaking the cart for the horse and, in so doing, becoming blind to the possibility that our horse might actually be a mule—an infertile combination of business and education, which is also neither.