Similarities & Differences of LAK, EDM using Ontology Learning Tools

I would like to summarize a recent analysis by Zouaq, Joksimović, & Gasević (2013) who used ontology learning tools to identify the similarities and differences between Learning Analytics (LAK) and Educational Data Mining (EDM).  I highly recommend the article as it describes, in detail, the conceptual analysis methodology.  Here is a summary of the data used and the results found.

The data included the following (as described by Taibi & Dietze, 2013):

Results

Similarities:
"... both the LAK and EDM conferences have students, data and models as shared concepts."


Differences:
"LAK papers also focus on teachers/instructors, informal learning, and social, networked, and group learning."
"EDM papers focus on (data mining) methods and approaches, intelligent tutoring systems, features (extraction), and various types of parameters."

"LAK papers are more focused on teachers in order to empower them with learning analytics and to help them guide students. Moreover, there is an emphasis on (promoting) reflection of both students and instructors. Various aspects of social learning such as role playing and impact of communities appear to be highly popular topics in the LAK papers."

"EDM papers are much more focused on intelligent tutoring systems, accuracy of different types of (predictive) models, and revealing unexpected patterns."


Similarities:
"focus on data is shared by both the LAK and EDM communities"

Differences:
"LAK also seems to be focused on data collected by and for instructors, not only for students. This probably indicates a trend that the LAK community has so far acknowledged the role of instructors in the learning process and aimed at supporting them as much as learners."

"The EDM community has however focused more on measuring and predicting specific types of skills.  This is consistent with their focus on intelligent tutoring systems in which automated assessment of learners’ skills is of paramount importance." 

Finally:
"[L]earning analytics is an integral part of teaching profession, is an important step for teachers of tomorrow and learners, and offers a new approach. This figure reveals also the nature of learning analytics to promote qualitative understanding of context of information. Learning analytics is also (strongly) related to discourse analytics, which seems to be consistent with the strong emphasis of learning analytics on social learning and which is further confirmed by extracted relationships of discourse learning analytics with sense-making argumentation and social, all of which are types of skills recognized as important for the modern society."

What I found uniquely interesting in this summary is the distinction between LAK's focus on a teacher perspective both in terms of data and in terms of purpose while the EDM focus is primarily on learner skill.  I think both of these perspectives are useful in the classroom and in pushing the boundaries of our understanding in teaching and and learning.  I'm not ready yet to make the decision that one is more important that the other or one is more useful than the other.  It may be that each is appropriate for a different audience or a different set of stakeholders or scale.  LAK may be a better fit for instructors or others who interact frequently with learners.  EDM's analysis, conclusions and recommendations may find a better fit with the needs and interests of instructional designers, institutional entities, and others with a role outside of direct instructional delivery. 


References

 

Taibi, D., & Dietze, S. (2013). Fostering analytics on learning analytics research: the LAK dataset. Retrieved from http://ceur-ws.org/Vol-974/lakdatachallenge2013_preface.pdf

Zouaq, A., Joksimović, S., & Gasević, D. (2013). Ontology Learning to Analyze Research Trends in Learning Analytics Publications. Retrieved from http://ceur-ws.org/Vol-974/lakdatachallenge2013_08.pdf