| 41813 |
Creator |
c9d52ef4a11ad1277436a89c20d0a5d4 |
| 41813 |
Creator |
ext-acfe72f67b403ac1193a22825bede515 |
| 41813 |
Creator |
ext-4f821e471fb2183debb2eca2f8591d18 |
| 41813 |
Date |
2015-06 |
| 41813 |
Date |
2015-06-30 |
| 41813 |
Is Part Of |
p07475632 |
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Is Part Of |
repository |
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abstract |
Learning analytics seek to enhance the learning processes through systematic measurements
of learning related data and to provide informative feedback to learners and teachers.
Track data from learning management systems (LMS) constitute a main data source for
learning analytics. This empirical contribution provides an application of Buckingham
Shum and Deakin Crick’s theoretical framework of dispositional learning analytics:
an infrastructure that combines learning dispositions data with data extracted from
computer-assisted, formative assessments and LMSs. In a large introductory quantitative
methods module, 922 students were enrolled in a module based on the principles of
blended learning, combining face-to-face problem-based learning sessions with e-tutorials.
We investigated the predictive power of learning dispositions, outcomes of continuous
formative assessments and other system generated data in modelling student performance
of and their potential to generate informative feedback. Using a dynamic, longitudinal
perspective, computer-assisted formative assessments seem to be the best predictor
for detecting underperforming students and academic performance, while basic LMS data
did not substantially predict learning. If timely feedback is crucial, both use-intensity
related track data from e-tutorial systems, and learning dispositions, are valuable
sources for feedback generation. |
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authorList |
authors |
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status |
peerReviewed |
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uri |
http://data.open.ac.uk/oro/document/280834 |
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uri |
http://data.open.ac.uk/oro/document/280837 |
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uri |
http://data.open.ac.uk/oro/document/281118 |
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uri |
http://data.open.ac.uk/oro/document/281143 |
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uri |
http://data.open.ac.uk/oro/document/281144 |
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uri |
http://data.open.ac.uk/oro/document/281145 |
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uri |
http://data.open.ac.uk/oro/document/281146 |
| 41813 |
uri |
http://data.open.ac.uk/oro/document/281147 |
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uri |
http://data.open.ac.uk/oro/document/281330 |
| 41813 |
volume |
47 |
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type |
AcademicArticle |
| 41813 |
type |
Article |
| 41813 |
label |
Tempelaar, Dirk T.; Rienties, Bart and Giesbers, Bas (2015). In search for the
most informative data for feedback generation: learning analytics in a data-rich context.
Computers in Human Behavior, 47 pp. 157–167. |
| 41813 |
label |
Tempelaar, Dirk T.; Rienties, Bart and Giesbers, Bas (2015). In search for the most
informative data for feedback generation: learning analytics in a data-rich context.
Computers in Human Behavior, 47 pp. 157–167. |
| 41813 |
Title |
In search for the most informative data for feedback generation: learning analytics
in a data-rich context |
| 41813 |
in dataset |
oro |