Analyzing and Comparing Online Learning Experiences through Micro-Level Analytics

Abstract

Learning analytics collects and uses observations of interactions, which allow course instructors to search for the underlying patterns of a student' learning progress and to accordingly optimize the student' learning progress at a micro-level. Understanding the online learning experience through the learning analytics approach is essential to inform future pedagogical decisions in online learning design. This paper attempts to define the concept of an online learning experience in three dimensions. In addition, the Experience Sampling Method (ESM) is suggested as a supplement to Web log analysis (WLA) to collect data on cognitive involvement and learning emotion as well as to collect behavioral interaction data. Then, using Clow s learning analytics cycle as a framework, this paper demonstrates how the identified cognitive, emotional, and behavioral aspects of the online learning experience can be captured and reported in the online learning experience dashboard for each individual student. In addition, the online learning experience data between two courses were compared to find evidence of different learning experiences when courses are designed with different learning tasks. The main finding from this paper is that ESM enables us to capture online learners' psychological dimensions of learning experiences and provides rich information on each learners progress in an online course