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Creator |
c9d52ef4a11ad1277436a89c20d0a5d4 |
| 54070 |
Creator |
71122b7715e55a3f53d5c14540adcbbb |
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Creator |
ext-d90fa66bf7395adabc51b74e0c007cd4 |
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Date |
2018 |
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Is Part Of |
repository |
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Is Part Of |
p17415659 |
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abstract |
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). |
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abstract |
Purpose
This empirical study aims to demonstrate how the combination of trace data derived
from technology-enhanced learning environments and self-response survey data can contribute
to the investigation of self-regulated learning processes.
Design/methodology/approach
Using a showcase based on 1,027 students’ learning in a blended introductory quantitative
course, the authors analysed the learning regulation and especially the timing of
learning by trace data. Next, the authors connected these learning patterns with self-reports
based on multiple contemporary social-cognitive theories.
Findings
The authors found that several behavioural facets of maladaptive learning orientations,
such as lack of regulation, self-sabotage or disengagement negatively impacted the
amount of practising, as well as timely practising. On the adaptive side of learning
dispositions, the picture was less clear. Where some adaptive dispositions, such as
the willingness to invest efforts in learning and self-perceived planning skills,
positively impacted learning regulation and timing of learning, other dispositions
such as valuing school or academic buoyancy lacked the expected positive effects.
Research limitations/implications
Due to the blended design, there is a strong asymmetry between what one can observe
on learning in both modes.
Practical implications
This study demonstrates that in a blended setup, one needs to distinguish the grand
effect on learning from the partial effect on learning in the digital mode: the most
adaptive students might be less dependent for their learning on the use of the digital
learning mode.
Originality/value
The paper presents an application of embodied motivation in the context of blended
learning. |
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authorList |
authors |
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issue |
4 |
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status |
peerReviewed |
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uri |
http://data.open.ac.uk/oro/document/644997 |
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uri |
http://data.open.ac.uk/oro/document/644998 |
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uri |
http://data.open.ac.uk/oro/document/644999 |
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uri |
http://data.open.ac.uk/oro/document/645000 |
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uri |
http://data.open.ac.uk/oro/document/645001 |
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uri |
http://data.open.ac.uk/oro/document/645002 |
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uri |
http://data.open.ac.uk/oro/document/660222 |
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volume |
15 |
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type |
AcademicArticle |
| 54070 |
type |
Article |
| 54070 |
label |
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). |
| 54070 |
label |
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. |
| 54070 |
label |
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. |
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Title |
A multi-modal study into students’ timing and learning regulation: time is ticking |
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in dataset |
oro |