Learning Pulse
2017 Jan 14Todo mundo sabe que educação é um assunto muito atual nos dias de hoje, e o principal: como usar os smartphones para que os mesmos saiam de vilões da atenção para uma ferramenta de monitoramento e acompanhamento do desempenho acadêmico?
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Abstract: Learning Pulse explores whether using a machine learning approach on multimodal data such as heart rate, step count, weather condition and learning activity can be used to predict learning performance in self-regulated learning settings. An experiment was carried out lasting eight weeks involving PhD students as participants, each of them wearing a Fitbit HR wristband and having their application on their computer recorded during their learning and working activities throughout the day. A software infrastructure for collecting multimodal learning experiences was implemented. As part of this infrastructure a Data Processing Application was developed to pre-process, analyse and generate predictions to provide feedback to the users about their learning performance. Data from different sources were stored using the xAPI standard into a cloud-based Learning Record Store. The participants of the experiment 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 markers to train a Linear Mixed Effect Model to generate learner-specific predictions of the learning performance. We discuss the advantages and the limitations of the used approach, highlighting further development points.
Conclusions: This paper described Learning Pulse, an exploratory study whose aim was to use predictive modelling to generate timely predictions about learners’ performance during self-regulated learning by collecting multimodal data about their body, activity and context. Although the prediction accuracy with the data sources and experimental setup chosen in Learning Pulse led to modest results, all the research questions have been answered positively and have lead towards new insights on the storing, modelling and processing multimodal data. We raise some of the unsolved challenges that can be considered a research agenda for future work in the field of Predictive Learning Analytics with “beyond-LMS” multimodal data. The ones identified are: 1) the number of self-reports vs unobtrusiveness; 2) the homogeneity of the learning task specifications; 3) the approach to model random effects; 4) alternative machine learning techniques. There is a clear trade-off between the frequency of selfreports and the seamlessness of the data collection. The number of self-reports cannot be increased without worsening the quality of the learning process observed. On the other side, having a high number of labels is essential to make supervised machine learning work correctly. In addition, a more robust way of modelling random effects must be found. The found solution to group them manually into categories is not scalable. Learning is inevitably made up by random effects, i.e. by voluntary and unpredictable actions taken by the learners. The sequence of such events is also important and must be taken into account with appropriate models. As an alternative to supervised learning techniques, also unsupervised methods can be investigated, as with those methods fine graining the data into small intervals does not generate problems with matching the corresponding labels also the amount of labels is no longer needed. Regarding the experimental setup, it would be best to have a set of coherent learning tasks that the participants of the experiment need to accomplish, contrarily to as it was done in Learning Pulse, where the participants had completely different tasks, topics and working rhythms. It would be also useful to have a baseline group of participants, which do not have access to the visualisations while another group does have access; that would allow to see the difference of performance, whether there is an actual increase. To conclude, Learning Pulse set the first steps towards a new and exciting research direction, the design and the development of predictive learning analytics systems exploiting multimodal data about the learners, their contexts and their activities with the aim to predict their current learning state and thus being able to generate timely feedback for learning support.
