Showing papers by "Dirk T. Tempelaar published in 2017"
TL;DR: The potential of dispositional characteristics of students, such as procrastination and boredom, are demonstrated, highlighting the need to move beyond simple engagement metrics, whereby dispositional learning analytics provide an actionable bridge between learning analytics and educational intervention.
Abstract: Studies in the field of learning analytics (LA) have shown students’ demographics and learning management system (LMS) data to be effective identifiers of “at risk” performance. However, insights generated by these predictive models may not be suitable for pedagogically informed interventions due to the inability to explain why students display these behavioral patterns. Therefore, this study aims at providing explanations of students’ behaviors on LMS by incorporating dispositional dimensions (e.g., self-regulation and emotions) into conventional learning analytics models. Using a combination of demographic, trace, and self-reported data of eight contemporary social-cognitive theories of education from 1,069 students in a blended introductory quantitative course, we demonstrate the potential of dispositional characteristics of students, such as procrastination and boredom. Our results highlight the need to move beyond simple engagement metrics, whereby dispositional learning analytics provide an actionable bridge between learning analytics and educational intervention.
61 citations
TL;DR: The article demonstrates the potential of behavioral learning analytic data in understanding how epistemic cognition is brought to bear in rich information seeking and processing tasks in a large-scale, naturalistic, study environment.
26 citations
TL;DR: It is conjecture that using learning dispositions with trace data has significant advantages for understanding student’s learning behaviours, and lessons learned from LA applications should focus on potential causes of suboptimal learning, such as applying ineffective learning strategies.
Abstract: This empirical study aims to demonstrate how Dispositional Learning Analytics (DLA) can provide a strong connection between Learning Analytics (LA) and pedagogy. Where LA based models typically do well in predicting course performance or student drop-out, they lack actionable data in order to easily connect model predictions with educational interventions. Using a showcase based on learning processes of 1080 students in a blended introductory quantitative course, we analysed the use of worked-out examples by students. Our method is to combine demographic and trace data from learning-management systems with self-reports of several contemporary social-cognitive theories. Students differ not only in the intensity of using worked-out examples but also in how they positioned that usage in their learning cycle. These differences could be described both in terms of differences measured by LA trace variables and by differences in students’ learning dispositions. We conjecture that using learning dispositions with trace data has significant advantages for understanding student’s learning behaviours. Rather than focusing on low user engagement, lessons learned from LA applications should focus on potential causes of suboptimal learning, such as applying ineffective learning strategies.
12 citations
TL;DR: The paper describes this perspective, which motivated empirical work to ‘orchestrate’ a CIS searching to learn session, and foregrounds this approach to demonstrate the potential of orchestration as a design approach for researching and implementing CIS as a ‘searching to learn’ context.
Abstract: The paper describes our novel perspective on ‘searching to learn’ through collaborative information seeking (CIS). We describe this perspective, which motivated empirical work to ‘orchestrate’ a CIS searching to learn session. The work is described through the lens of orchestration, an approach which brings to the fore the ways in which: background context—including practical classroom constraints, and theoretical perspective; actors—including the educators, researchers, and technologies; and activities that are to be completed, are brought into alignment. The orchestration is exemplified through the description of research work designed to explore a pedagogically salient construct (epistemic cognition), in a particular institutional setting. Evaluation of the session indicated satisfaction with the orchestration from students, with written feedback indicating reflection from them on features of the orchestration. We foreground this approach to demonstrate the potential of orchestration as a design approach for researching and implementing CIS as a ‘searching to learn’ context.
11 citations
Journal Article•
TL;DR: In this paper, the same maladaptive learning orientations that play a role in worked examples learning theories as to explain the effectiveness of worked examples do predict the use of worked example: this time in the role of individual learning dispositions.
Abstract: This empirical study aims to demonstrate how Dispositional Learning Analytics can contribute in the investigation of the effectiveness of didactical scenarios in authentic settings, where previous research has mostly been laboratory based. Using a showcase based on learning processes of 1080 students in a blended introductory quantitative course, we analyse the use of worked examples by students. Our method is to combine demographic and trace data from technology enhanced systems with self-reports of several contemporary social-cognitive theories. We find that the same maladaptive learning orientations that play a role in worked examples learning theories as to explain the effectiveness of worked examples do predict the use of worked examples: this time in the role of individual learning dispositions.
5 citations