Educational Data Mining, Learning Analytics... and Academic Analytics!

This week starts off with a discussion about general similarities and differences in the research areas interested in large data sets, computational mathematics, and education.

For a while, I thought there were two research areas: Educational Data Mining and Learning Analytics. But, as I read Long and Siemens (2011), I saw there was another research area called Academic Analytics. In the Learning Analytics (LA) MOOC put out by the Society for Learning Analytics Research (SOLAR), Siemens explains that Academic Analytics is not as well known as the other two, but still has its niche.

Anyways, here's a breakdown of some of the comparisons and contrasts in these different, but related, research areas:

• Similarities
• Has a focus on improving education
• Makes interpretations of large data sets to enable educational planning, strategy, and decision making
• Has interest in developing methods of data analysis to enable these interpretations
• Differences (and perhaps more similarities)
• Learning Analytics
• Focus "is exclusively on the learning process" (Long & Siemens, 2011, p. 34).
• Discourse analysis, social network analysis, content analysis, personalization and adaptation, prediction and intervention
• Educational Data Mining
• Especially interested in how to collect and analyze data
• Data visualization, recommendations, predictions, student modeling, grouping, social network analysis, concept mapping, planning and scheduling (Romero & Ventura, 2010).
• Academic Analytics
• "The application of business intelligence in education" (Long & Siemens, 2011, p. 34).
• Has a wider focus, especially in institutional level data analysis (enrollment, grades, GPA, college entrance exam scores, etc).
• Recommendations, predictions, scheduling, planning (Campbell, Deblois, & Oblinger, 2007).
• Doesn't seem to have its own journal or conference as LA and EDM do.

Reading introductory articles to these areas of research really makes me think that they could all be lumped together. I think Learning Analytics tries to separate itself by focusing solely on learning, whereas educational data mining and learning analytics include work on things like predicting which prospective students will most likely be admitted, or how many textbooks to order. As of yet, I still need to better understand the differences between educational data mining and academic analytics, as these two seem to be the most similar.

Which area does my research fit? Personally, I could see it belonging to any of them. I've seen examples of research similar to my project being presented in conferences centered on Learning Analytics and Educational Data Mining. I believe the focus of my research is on learning, and personalizing and adapting learning pathways based on user behavior and learner characteristics, thus pushing me more towards the Learning Analytics area. I don't think I could go wrong, however, in publishing and presenting in any of these areas.

References

Campbell, B. J. P., Deblois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. Educause Review, (August 2007), 41–57.

Long, P., & Siemens, G. (2011). Penetrating the Fog: Analytics in Learning and Education. Educause Review, 46(5), 31–40.

Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, 40(6), 601–618.