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

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 (Early Access).

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Blended learning and other types of technology-enhanced education offer unique opportunities to investigate traditional, educational research questions from new perspectives: ‘The advance of technology-enhanced learning environments is opening up new opportunities for reconstructing and analysing students' learning behavior.’ (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. Learning analytics (LA) methods, that use ‘dynamic information about learners and learning environments, assessing, eliciting and analysing it, for real-time modelling, prediction and optimisation of learning processes, learning environments and educational decision-making’ (Ifenthaler, 2015), have boosted the use of trace data in research applications. However, most ‘classical’ LA research suffers from the same shortcomings as classical educational research: they often use only one type of data, this time trace data, and thus focus on one single perspective.

Recently, several multi-modal studies have started to integrate different types of learning analytics data as well as exploring learning from an intertemporal perspectives. Examples of studies applying multi-modal data are Duffy and Azevedo (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., 2015, 2017a, 2017b, 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).

Item Type: Journal Item
ISSN: 1741-5659
Academic Unit/School: Learning and Teaching Innovation (LTI) > Institute of Educational Technology (IET)
Learning and Teaching Innovation (LTI)
Research Group: Centre for Research in Education and Educational Technology (CREET)
Item ID: 54070
Depositing User: Bart Rienties
Date Deposited: 04 Apr 2018 11:24
Last Modified: 10 Sep 2018 22:55
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