#LAK11 Learning Analytics – Week 1

The first week of the LAK11 course was spent on exploring the field of learning analytics.  Its relations with domains such as business intelligence and web analytics were outlined (Elias, 2011) and its close relationship with academic analytics and educational data mining (EDM).
Learning analytics could be defined as the use of educational data to improve learning.  Course facilitator George Siemens (TEKRI, Athabasca University) puts it this way:
Learning analytics is 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”

Online learning produces a wealth of student data, such as time spent on certain modules, number of logins, number of posts on forums and their social interactions with other students and tutors.  Academic institutions can use these data to improve learning.  For example, they can provide an “early warning system” and predict which students are likely to fail their exams.  These students could then get extra support (McFadden & Dawson, 2010).  By coupling learning data with socio-economic data and demographics, predictions about student success can be made.  Purdue University’s Signals Project is a flagship case in this regard.

Recommender systems, such as used by e-commerce firms as Amazon are regularly mentioned as one of the potential applications of learning analytics.  For example, based on the sources accessed or links in the social network, students could get recommendations about potentially interesting articles, blogs or people.

Looking for meaningful patterns in large sets of educational data is called educational data mining.  Learning analytics, however, includes using these data to intervene in the learning process, like altering the course content, the provision of support or the use of tools.

 Siemens, 2010

Learning analytics could (should) also be student-centered.  This means that students could be granted access to course data. For instance, they see how much time they’ve spent on various course activities and compare it with their peers.  The question what students want generated discussion on the course forums.  The idea, outlined by John Fritz in his presentation was that students take more responsibility for their own learning and strengthen their meta-cognitive abilities.  They could get access to the data, but it would be their responsibility to interpret it and act upon it.  However, most institutions are still in the phase of collecting heaps of data and analyzing them, without really predicting and modeling behavior, or using it to optimize learning.

“Institutions can’t “absolve” students from “at least partial responsibility for their own education. To do so denies both the right of the individual to refuse education and the right of the institution to be selective in its judgments as to who should be further educated. More importantly, it runs counter to the essential notion that effective education requires that individuals take responsibility for their own learning” (p. 144)  

Vincent Tinto, Leaving College: Rethinking the causes and cures of student attrition (1993)

Interesting discussion focused on the issue of distributed analytics.  It is easier to collect data when all student activity is concentrated in one platform or Learning Management System (LMS).  However, when students are stimulated to use a variety of tools, sources and interaction platforms, gathering meaningful data becomes more difficult.  A second issue is the discussion of privacy, in particular when other students also get access to the data.

My first impressions on the MOOC are overwhelming, chaos and quality.  The amount of e-mails and forum posts is staggering and different discussion are taking place simultaneously.  However, it is not really the purpose of a MOOC to participate in everything but rather to be selective.  In that way, a MOOC is a great way to get lectures, information and feedback of some of the leading researchers in the field.  You are stimulated to read the materials and try to make sense of it at your own pace and on your own knowledge level.  A next step is then to create something (like a forum post), share it and get into contact with “likeminded souls”.  We’ll see how that plays out.


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