New model promises user-centred edtech

As technology use in the education sector has boomed, so too has the capacity for data capture and analysis. While learning analytics dashboards are developed and offered by tech vendors with a goal to support self-regulated learning, there is in fact little understanding of the effectiveness of that relationship.

Researchers from the Faculty of Information Technology (IT) at Monash University have collaborated with colleagues from the University of Edinburgh and the University of South Australia to develop a new user-centred model for learning analytics that consists of four interconnected dimensions: scientific research of learning and education, human-centred design, educational feedback and evaluation.

“This user-centred learning model will emphasise critical properties of self-regulated learning by focusing on metacognitive, cognitive, affective and behavioural aspects of learning and guide the future work of developers, researchers and adopters, to create better learning systems,” said the lead researcher, Professor Dragan Gasevic from the Faculty of IT.

By conducting an analysis of existing empirical studies about the use of learning analytics dashboards, Processor Gasevic and his team found that existing learning analytics dashboards are rarely grounded in recommendations established in educational research.

“Despite the growing adoption of learning analytics dashboards, there are many limitations in the design of their systems which our research has identified. Particularly, learners find it hard to interpret the data presented in dashboards and to make use of the feedback presented in dashboards to inform future learning strategies,” Professor Gasevic said.

The results also showed that learning analytics dashboards cannot be suggested to empower students to advance the understanding of their own learning. Current learning analytics dashboards often fail to offer advice to students and teachers on the use of effective learning tactics and strategies and have significant limitations in how their evaluation is conducted and reported.

“Another major concern is that the impact of learning dashboards and recommendation systems on student learning and success is found to be relatively low. Feedback presented in learning analytics dashboards can also be difficult to translate into a meaningful actionable recommendation to guide students in their learning,” Professor Gasevic said.

Properly developed learning analytics tools can provide teachers with additional insights into student learning strategies and also provide students with personalised advice on their performance.

The value of learning analytics to support the development of self-regulated learning is voiced by many stakeholders and this is especially relevant in the times of digitalisation in which policymakers and education leaders recognise skills for self-regulated learning as essential for the future of life and work.

The full research paper can be found using this link.

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