The following link is an aggregated bibliography on the literature on EDM with entries as recent as 2013.

Entry 236 was helpful for me on a couple accounts: clarification on the difference between Educational Data Mining (EDM) and Learning Analytics (LA), and an assignment I'm working on :-)

Here is the following from Romero and Ventura, p. 13, circa 2013, on the difference between EDM and LA:

Of all the aforementioned areas the field most related to EDM is LA, also known as academic analytics. LA is focused on data-driven decision-making and integrating the technical and the social/pedagogical dimensions of LA. However, although EDM is generally looking for new patterns in data and developing new algorithms and/or models, LA is applying known predictive models in instructional systems. In fact, LA can be defined 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. Although LA and EDM can share many attributes and have some similar goals and interests, the next key differences can be distinguished between both communities:

• Techniques: In LA, the most used techniques are statistics, visualization, SNA, sentiment analysis, influence analytics, discourse analysis, concept analysis, and sense-making models. In EDM, the most used techniques are classification, clustering, Bayesian modelling, relationship mining and discovery with models.
• Origins: LA has stronger origins in Semantic Web, intelligent curriculum, and systemic interventions. EDM has strong origins in educational software, student modeling, and predicting course outcomes.
• Emphasis: LA has more emphasis on the description of data and results; however, EDM has more emphasis on the description and comparison of the DM techniques used.
• Type of Discovery: In LA, leveraging human judgement is key; automated discovery is a tool used to accomplish this goal. In EDM, automated discovery is key; leveraging human judgment is a tool used to accomplish this goal.

References:

Romero, C., Ventura, S. (2013). Data Mining in Education. WIREs Data Mining and Knowledge Discovery, 3:12-27.