A multi-modal study into students’ timing and learning regulation: time is ticking

Abstract

How to cite:Tempelaar, Dirk; Rienties, Bart and Nguyen, Quan (2018). A multi-modal study into students' timing and learning regulation: time is ticking. Interactive Technology and Smart Education, 15(4) pp. 298-313.For guidance on citations see FAQs. ' (Schumacher and Ifenthaler, 2018, p. 397). The use of multi-modal data, which is characterised by two or more distinct types of data, offers new insights into long-standing academic debates that have been addressed in the past with empirical studies based on survey data only. The availability of trace data derived from the use of technology-enhanced learning, trace data of both process and product types (Azevedo et al., 2013), is a crucial aspect in this progress made in analysing learning behaviours. (2015), analysing goal setting survey data in combination with trace data, or Sergis et al. (2018), analysing self-determination based motivational survey data in combination with trace data. A related approach is that of Dispositional Learning Analytics (DLA, Buckingham Shum and Crick, 2012), that proposes an infrastructure that combines learning data (generated in learning activities through technology-enhanced systems) with a broad range of learner data: student dispositions, values, and attitudes measured through self-report surveys. Learning dispositions represent individual difference characteristics that impact all learning processes and include affective, behavioural and cognitive facets (Rienties et al., 2017). Students' preferred learning approaches are examples of such dispositions of both cognitive and behavioural type. In a series of studies (Nguyen et al., 2016;Tempelaar et al., 2015Tempelaar et al., , 2017aTempelaar et al., , 2017bTempelaar et al., , 2018 we have analysed bi-modal data derived from a first-year introductory course mathematics and statistics, offered in blended mode, in which several survey instruments were applied, that cover learning dispositions thought to be important in self-regulated learning. Students' preferences for alternative feedback modes, distinguishing between learners who prefer worked-out examples, tutored problem-solving or untutored problem-solving and investigating the role of learning dispositions as an antecedent of these preferences, was one of the aims of these studies. In our current paper, we continue this line of research, whereby we now focus on learning regulation and especially the timing of learning as part of a self-regulated learning process, and investigate the role of antecedents in this regulation, thereby focussing on antecedents that are part of the framework of embodied motivation (Spector and Park, 2018). Self-regulated learning and the timing of learningThere is an abundance of empirical research investigating learning time in self-regulated learning processes. Examples of such studies can be found in the domain of classical educational studies, such as Wolters et al. (2017) who find that students' self-perceptions of time management are associated with self-perceived motivational and strateg...

Learning Analytics for Blended Learning: A Systematic Review of Theory, Methodology, and Ethical Considerations

“…[36,52,58,80]. Numerous studies explored relations between trace data, performance and SRL using self-reports [30]; some in combination with other theories, for example theories of motivation [20] socio-cultural perspectives [31], Self-Determination Theory and the Control-Value Theory of achievement emotions [86][87][88]. 5.…”

Student engagement and wellbeing over time at a higher education institution

“…Dispositional learning analytics (see [26]), on the other hand, seeks to combine digital trace data (e.g., those generated by engagement in online learning activities) with learner data (e.g., dispositions, attitudes, and values assessed via self-report surveys). By doing so, recent research has found that learning dispositions (e.g., motivation, emotion, self-regulation) strongly and dynamically influence engagement and academic performance over time (e.g., [27–29]). In addition, this research suggests that the predictive value added by consideration of learner data might be time-dependent: learner data seems to play a critical role up until the point that feedback from assessment or online activities becomes available.…”

Evidence-Based Learning: Futures

“…Indeed recent research has highlighted that on a more micro-level learners at the OU have substantially different learning needs and ambitions (Law, 2015;Li et al, 2017), depending on a complex interplay of affective (Hillaire et al, Submitted;Tempelaar et al, 2018b), behavioural (Chua et al, 2017;Rets, 2018;Rizvi et al, 2018), and cognitive factors, as well as socio-economic and demographic factors (Richardson, 2015). Therefore, in the remainder of this chapter we will primarily focus on how to provide more personalised and individualised support for learning in the next 2-5 years.…”

“…(Herodotou et al, 2017(Herodotou et al, , 2019Hlosta et al, 2015). Furthermore, some institutions like Universiteit van Amsterdam (Berg et al, 2016), University of Keele (de Quincey et al, 2019), and Maastricht University (Tempelaar et al, 2018b) have successfully experimented with providing learning analytics data directly to students in order to support their learning processes and self-regulation.…”