Learning analytics to unveil learning strategies in a flipped classroom

Prior education studies have consistently emphasized the importance of sustained and active student engagement to aid academic performance and achievement of learning outcomes (e.g., Hockings, Cooke, Yamashita, McGinty, & Bowl, 2008; Michael, 2006). The positive impact of such active learning models on academic outcomes has been well established, particularly, in the STEM (Science, Technology, Engineering andMathematics) disciplines. For example, Freeman et al. (2014) demonstrated that students undertaking STEM courses incorporating active learning models received (on average) higher academic grades and were less likely to fail in comparison to peers in more traditional and lecture based modes of teaching. While active learning has clear benefits for student learning outcomes, the process of implementation is often more complex than first anticipated (Gillies & Boyle, 2010; Hung, 2011). For instance, student engagement in active learning does not occur spontaneously and educators must employ careful consideration of the curriculum design, activity sequencing and progression as well as the diversity of learners, including learners' prior experience and motivation, background and knowledge. Flipped learning (FL) is a form of blended learning that requires students' active participation in learning activities both before and during face-to-face sessions with the teacher (Lage, Platt, & Tregua, 2000). However, students frequently lack the necessary skills, time, and/ormotivation to fully participate in pre-class activities and therefore do not commit to the level of involvement in the learning process that effectively complements the intended design (Lai & Hwang, 2016; Mason, Shuman, & Cook, 2013). Clearly, the reasoning for why students may or may not engage in pre-class activities is complex and multi-dimensional. However, if provided with a deeper insight into the types of learning strategies students employ in such active learning models, teaching staff can make better informed decisions regarding student support and course design processes (Stief & Dollar, 2009). Despite the increasing popularity of FL and similar active learning models, there has been limited attention devoted to understanding