Exploring Social Learning Analytics to Support Teaching and Learning Decisions in Online Learning Environments

摘要

Most teachers to date have adopted summative assessment items as a benchmark to measure students' learning and for making pedagogical decisions. However, these may not necessarily provide comprehensive evidence for the actual learning process, particularly in online learning environments due to their failure to monitor students' online learning patterns over time. In this paper, we explore how social learning analytics (SLA) can be used as a proxy by teachers to understand students' learning processes and to support them in making informed pedagogical decisions during the run of a course. This study was conducted in a semester-long undergraduate course, at a large public university in Norway, and made use of data from 4 weekly online discussions delivered through the university learning management system Canvas. First, we used NodeXL a social network analysis tool to analyze and visualize students' online learning processes, and then we used Coh-Metrix, a theoretically grounded, computational linguistic tool to analyze the discourse features of students' discussion posts. Our findings revealed that SLA provides insight and an overview of the students' cognitive and social learning processes in online learning environments. This exploratory study contributes to an improved conceptual understanding of SLA and details some of the methodological implications of an SLA approach to enhance teaching and learning in online learning environments.