Discovering learning antecedents in learning analytics literature

We investigated various learning antecedents that have been the research subjects of Learning Analytics (LA) studies and explored the content and quantity of the LA literature with respect to each antecedent through text mining the LAK dataset. Our goal was to simultaneously reveal to what extent do LA researchers address learning antecedents and how they incorporated these in the implementation of LA solutions (e.g. models and software technologies) to facilitate and augment student learning. Instead of taking a pure text mining approach, we undertook a slightly different strategy by (i) identifying antecedents of student learning by examining extant literature on learning and educational theories and (ii) identifying which among the theoretically relevant antecedents are currently reported in LA studies. The analytical techniques we employed were a mix of domain-based analysis and corpus analytics which included association analysis and key-phrase extraction. The results showed that most LA studies are geared toward capturing and measuring student awareness and promoting social learning and less on goal-setting and self-efficacy. Through this work we hope to encourage the LA community to dedicate research efforts to also investigate other relatively neglected yet promising learning antecedents.