Learning analytics dashboard (LAD) is one of the essential features on most online learning platforms. LAD is the hub that aggregates different indicators and metrics about learners and learning processes into visualizations. While informative, how effective these LADs are for self-directed learning needs further examination.
In a recently paper, Matcha, Uzir, Gašević, and Pardo (2019)
conducted a systematic review of empirical studies on LADs from the perspective of self-regulated learning. Using Winne and Hadwin’s
framework of self-regulated learning (which includes four cyclical recursive phases: task definition, goal setting and planning, enactment of tactics and strategies, and adaptation), they examined current empirical studies on LADs and their impact on learning and teaching.
The findings reveal that most of the LADs from the studies do not support self-regulated learning and metacognition. In addition, they do not provide suggestions for effective learning strategies that are critical to enhance self-directed learning. Based on these limitations, Matcha et al. (2019) propose the model for user-centered learning analytics systems (MULAS).
In the MULAS model, Matcha et al (2019) suggest that LADs add a goal-setting functionality (like the so-called S.M.A.R.T goals that are specific, measurable, achievable, relevant, and time-bound) and a recommendation feature that provides learning tactics based on data mining results. In addition, they remind developers and designers to explore various forms of communications and representations, and not assume that one style of representation or visualization would work for all learners. Future LADs research should also study the extent to which learners understand and can act on feedback received through the LADs, and how LADs can act as a mediator of feedback.
As online learning becomes popular, how to design effective learning analytics dashboards are critical to successful learning. Findings from this study provide useful guidelines for future LADs design.