Monthly Archives: August 2011

Learning and Academic Analytics

Analytics in education take on at least three distinct terms:
1. educational datamining – this is a fairly developed community, having run conferences for over four years. They also run their own journal. From their website: Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in.
2. Learning analytics – after a successful conference this year, we are planning our second conference in Vancouver next year (call for papers is now open). This community is still developing, but interest in learning analytics is high in various government and educational reform settings. Learning analytics is defined as the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.
3. Academic analytics – this term has been around for about a decade, based on early work by Diana Oblinger and John Campbell. As initially presented, the concept addresses a mix of administrative and learning analytics. For clarity sake, this concept is now closest to what is called business intelligence in corporate settings.

How are these three items related? Educational data mining has a role in both learning analytics and academic analytics. The table below gets at that relationship. I don’t see the relationship as starkly or as clearly demarcated as the table indicates. I’m trying to get at the distinction between learning analytics as focusing on activity at the learner-educator level, academic analytics as focusing on organizational efficiency, and datamining as have some role in both spaces.

Type of Analytics

Level or Object of Analysis Who Benefits?
Learning Analytics  

 

Educational data mining

 

 

 

Course-level: social networks, conceptual development, discourse analysis, “intelligent curriculum” Learners, faculty
Departmental: predictive modeling, patterns of success/failure Learners, faculty
Academic Analytics Institutional: learner profiles, performance of academics, knowledge flow Administrators, funders, marketing
Regional (state/provincial): comparisons between systems Funders, administrators
National and International National governments, education authorities