Authors:
Dirk Tempelaar
1
;
Bart Rienties
2
and
Bas Giesbers
3
Affiliations:
1
Maastricht University School of Business and Economics, Netherlands
;
2
Open University UK and Institute of Educational Technology, United Kingdom
;
3
Rotterdam School of Management, Netherlands
Keyword(s):
Blended Learning, Dispositional Learning Analytics, e-tutorials, Formative Assessment, Learning Dispositions.
Related
Ontology
Subjects/Areas/Topics:
Blended Learning
;
Computer-Supported Education
;
e-Learning
;
e-Learning Platforms
;
Information Technologies Supporting Learning
;
Learning Analytics
;
Learning/Teaching Methodologies and Assessment
;
Simulation and Modeling
;
Simulation Tools and Platforms
Abstract:
Learning analytics seek to enhance the learning processes through systematic measurements of learning
related data and to provide informative feedback to learners and educators. In this follow-up study of
previous research (Tempelaar, Rienties, and Giesbers, 2015), we focus on the issues of stability and
sensitivity of Learning Analytics (LA) based prediction models. Do predictions models stay intact, when the
instructional context is repeated in a new cohort of students, and do predictions models indeed change,
when relevant aspects of the instructional context are adapted? 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. We compare two cohorts of a large introductory quantitative
methods module, with 1005 students in the ’13/’14 cohort, and 1
006 students in the ’14/’15 cohort. Both
modules were based on principles of blended learning, combining face-to-face Problem-Based Learning
sessions with e-tutorials, and have similar instructional design, except for an intervention into the design of
quizzes administered in the module. Focusing on the predictive power, we provide evidence of both stability
and sensitivity of regression type prediction models.
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