Advancing Learning Analytics in Online Learning Environments Through the Method of Sequential Analysis

Abstract

Improving quality of learning in online environments requires formative and summative assessments particular to its distinctive online learning nature and dynamics. Feedback mechanism about learning engagement could be a source of assessment for the educators and the students. In online learning, not only the educators but also the students need to get informed about their learning engagements as cyberspace is not controllable for the educators and students could be in interaction with any content or human agency without teacher guidance and in this context, feedback based on analytical assessment is essential for the self-learning activities. Sometimes, in online group discussions, the teachers may not decide when to interrupt the ongoing discussions and in parallel with this, the students in a learning group may be in a vicious circle and repeatedly discussing the same topic without any advancement whereas critical thinking skills require adaptation of some instructional strategies. In these occasions, learning analytics (LA) is a key process to provide educational stakeholders with evidence based, analytical and contextual outcomes in a way of making sense of their learning engagements. In this conceptual framework, our research has attempted to examine sequential analysis as a likely method to get adapted by LA researchers with its prospects, opportunities and challenges. To this end, we focused on a part of a blended learning course and analyzed the students' online discussions to examine the method of sequential analysis with its potential to be an efficient tool to conduct learning analytics. The outcomes of this study have implications for the design of an online learning environment as well as LA as an emerging area.