Microlearning for the development of Teachers’ Digital Competence related to feedback and decision making

[EN] The assessment and feedback area of the European Framework for the Digital Competence of Educators (DigCompEdu) establishes a specific competence related to the ability to use digital technologies to provide feedback and make decisions for learning. According to the literature, this particular competence is one of the least developed in the teaching profession. As there are few specialised training strategies in the field of information and communication technology (ICT)-mediated feedback, this study aims to validate a microlearning proposal for university teachers, organised in levels of progression following the DigCompEdu guidelines. To validate the proposal, a literature analysis was carried out and a training proposal was developed and submitted to a peer review process to assess its relevance. This study identifies the elements that should be included in a training strategy in the area of feedback and decision making for university contexts. Finally, it is concluded that this type of training requires a combination of agile and self-managed strategies (characteristics of microlearning), which can be complemented by the presentation of evidence and collaborative work with colleagues

Learning Feedback Based on Dispositional Learning Analytics

The combination of trace data captured from technology-enhanced learning support systems, formative assessment data and learning disposition data based on self-report surveys, offers a very rich context for learning analytics applications. In previous research, we have demonstrated how such Dispositional Learning Analytics applications not only have great potential regarding predictive power, e.g. with the aim to promptly signal students at risk, but also provide both students and teacher with actionable feedback. The ability to link predictions, such as a risk for drop-out, with characterizations of learning dispositions, such as profiles of learning strategies, implies that the provision of learning feedback is not the end point, but can be extended to the design of learning interventions that address suboptimal learning dispositions. Building upon the case studies we developed in our previous research, we replicated the Dispositional Learning Analytics analyses in the most recent 17/18 cohort of students based on the learning processes of 1017 first-year students in a blended introductory quantitative course. We conclude that the outcomes of these analyses, such as boredom being an important learning emotion, planning and task management being crucial skills in the efficient use of digital learning tools, help both predict learning performance and design effective interventions