Learning Feedback Based on Dispositional Learning Analytics

Abstract

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.

Microlearning for the Development of Teachers’ Digital Competence Related to Feedback and Decision Making

“…In the field of educational scaffolding, Espasa and Meneses [52] considered the potential of semantic feedback, while Alemdag and Yildirim [53] analysed the impact of scaffolding on peer feedback. In this same block, the studies of Sedrakyan et al [54], Tempelaar et al [55], and Banihashem et al [11] identified a theoretical basis supporting the importance of creating learning metric dashboards to guide feedback.…”

Dispositions towards learning: the importance of epistemic attributes for postgraduate learners

“…This may be particularly powerful when used in combination with data from online learning systems such as Canvas, which can contribute data about online learning activity. This is the principle of the emerging field of dispositional learning analytics (Tempelaar et al, 2020) which has the potential to identify potentially at-risk students early, and automatically tailor advice and even interventions to promote more effective learning. The automation of such advice is increasingly possible with the adoption of more advanced student relationship technologies such as student relationship engagement system (sres.io) (Liu et al, 2017;Arthars et al, 2019;Vigentini et al, 2020).…”

Using Learner Analytics to Explore the Potential Contribution of Multimodal Formative Assessment to Academic Success in Higher Education

“…In recent years, both the number of L2 learners pursuing higher education and efforts to integrate technology into EAP programs have increased greatly. However, research on the value of formative assessment has chiefly examined other courses and disciplines such as quantitative methods (e.g., Choi et al, 2018;Tempelaar et al, 2018Tempelaar et al, , 2020, business (e.g., Tempelaar et al, 2011), history, social care, maths, and engineering (e.g., Hlosta, et al, 2017). There has been little in-depth analysis of the extent to which EAP formative assessments delivered through LMSs such as Blackboard can predict students' future performance and on the potential of learner analytics to identify L2 students who are academically at risk.…”