loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

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

ISBN: 978-989-758-107-6

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 10 06 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. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.162.239.233

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Tempelaar D., Rienties B. and Giesbers B. (2015). Stability and Sensitivity of Learning Analytics based Prediction Models.In Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-107-6, pages 156-166. DOI: 10.5220/0005497001560166

@conference{csedu15,
author={Dirk Tempelaar and Bart Rienties and Bas Giesbers},
title={Stability and Sensitivity of Learning Analytics based Prediction Models},
booktitle={Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2015},
pages={156-166},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005497001560166},
isbn={978-989-758-107-6},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Stability and Sensitivity of Learning Analytics based Prediction Models
SN - 978-989-758-107-6
AU - Tempelaar D.
AU - Rienties B.
AU - Giesbers B.
PY - 2015
SP - 156
EP - 166
DO - 10.5220/0005497001560166

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.