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The final presentation of the master thesis project Visual Learning Pulse: Flow Prediction and Feedback in Self-Regulated Learning, a project collaboration between the Department of Data Science and Knowledge Engineering of the Univeristy of Maastricht and the Welten Institute of the Open University in the Netherlands.
Visual Learning Pulse: Flow Prediction and Feedback in Self-regulated Learning
Visual Learning Pulse is a Master thesis research project developed in cooperation with the Welten Institute, the Research Centre for Learning, Teaching and Technology at the Open University of the Netherlands, and partially nanced by the European project Learning Analytics Community Exchange (LACE). Visual Learning Pulse explores whether physiological and physical data such as heart rate, step count and weather data if correlated with learning activity data can be used to predict learning success in self-regulated learning settings.
To verify this hypothesis an experiment was opportunely designed, consisting of three phases, lasting six weeks and involving nine participants, each of them wearing a Fitbit HR wrist band and having their application usage recorded during their learning and working activities throughout the day. An ad-hoc infrastructure for longitudinal and multi-modal data was designed and implemented. The data from dierent sources were stored using the Experience API (xAPI) data standard in a cloud distributed database called Learning Record Store.
The participants (doctoral students at the Open Universiteit) - were asked to rate their learning experience through an Activity Rating Tool indicating their perceived level of productivity, stress, challenge and abilities. These self-reported performance indicators were used as training labels for the two algorithms employed for prediction of time series data: the Vector Autoregression and Linear Mixed Eect Model.
A major task of the thesis consisted of developing the software application to pre-process, perform the analysis and generate the predictions on real time, in order to provide timely feedback to the users about their learning performances. Although not showing high overall accuracy, the prediction models were successfully learnt and used in production: in the third phase of the experiment, two visualisations mechanisms were used, the Learner Dashboard and the Feedback Cubes.
In addition, a conceptual paper of Visual Learning Pulse, illustrating setup and overall the rationale was presented at the Learning Analytics & Knowledge conference 2016 in Edinburgh, Scotland and was included in CEUR workshop proceedings.