Decoding Learning Analytics to Determine the Relationship of Student-Instructor and Student-Student Interactions on Students Learning and Motivation in a Flipped Classroom

With the current shift in educational settings to blended and flipped classroom and the introduction of learning management systems (LMS) such as Moodle, it is no surprise big data has found its place in education and is predicted to be extensively implemented in institutions of higher education (Johnson et al., 2013). In a flipped classroom approach, it is instrumental that there are quality interactions between student-to-instructor as well as student-to-student. Altogether, derived from business intelligence and data mining, learning analytics is referred to as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs” (Siemens, 2011). The purpose of this mixed method was to decode the learning analytics installed on Moodle LMS to determine the student-instructor and student-student interactions that promotes student learning and motivation. The sample population consisted of trainee educators enrolled multiple academic disciplines. An online instrument was also designed to capture trainees’ characteristics, their perceptions of learning, motivation, student-to-student interactions and student-to-instructor interactions. The results revealed that the quality interactions are determining and significant contributors of student learning and motivation.