There are five key differences between Learning Analytics & Knowledge (#LAK) and Educational Data Mining (#EDM). Let’s start with some definitions:
The International Educational Data Mining Society defines educational data mining as: “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 in which they learn in” (Siemens & Baker, 2010).
The Society for Learning Analytics Research defines learning analytics as: “… 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” (Siemens & Baker, 2010).
LAK: Leverages human judgment is key; uses automated discovery as a tool to accomplish this goal. EDM: Automated discovery is key; leverages human judgment as a tool to accomplish this goal.
2. Reductionism / Holism
LAK: Emphasis on systemic understanding. EDM: Emphasis on analysis of components and relationships between components.
LAK: Stronger origins in semantic web, outcome prediction & system interventions. EDM: Stronger origins in educational software, student modeling & outcome predictions.
4. Adaptation & Personalization
LAK: Greater focus on informing & empowering instructors and learners. EDM: Greater focus on automated adaptation (computer w/o humans in the loop).
5. Techniques & Methods
LAK: Social network analysis, sentiment analysis, influence analytics, discourse analysis, learning success prediction, concept analysis, sense-making models. EDM: Classification, clustering, Bayesian modeling, relationship mining, discovery with models, visualization.
(Siemens & Baker, 2010).
A Call for Communication & Collaboration:
Despite these differences, there are also many similarities such as the researchers skills sets, their research areas overlap and there is much to be gained from one another’s approaches. Siemens & Baker (2010) call for the two communities to collaborate to bring the “greatest possible benefits to educational practice and the science of learning.”
Siemens, G., & Baker, R. S. (2010). Learning analytics and educational data mining: Towards communication and collaboration. Conference ’10. Retrieved from: http://www.columbia.edu/~rsb2162/LAKs%20reformatting%20v2.pdf