Rethinking dashboard analytics

Lots of data are available through dashboards in many LMSes and other learning software but access to dashboards seems to have little effect on learning and this could be due to the and inability to fully interpret and apply what is being presented.

In response, researchers from the Faculty of IT at Monash University, in collaboration with colleagues from the University of Edinburgh and the University of South Australia have developed a model for a user-centred learning analytics system, consisting of four dimensions that are interconnected including; scientific research of learning and education, human-centred design, educational feedback and evaluation.

Its development followed a systematic literature review of learning analytics dashboards.

“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 lead researcher, Prof Dragan Gasevic from the Faculty of IT.

By conducting an analysis of existing empirical studies about the use of learning analytics dashboards, 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,” Gasevic said.

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.” 

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.

To read the research paper, visit: https://bit.ly/2ZfPDNa