Computer assisted, formative assessment and dispositional learning analytics in learning mathematics and statistics

Tempelaar, Dirk T.; Rienties, Bart and Giesbers, Bas (2014). Computer assisted, formative assessment and dispositional learning analytics in learning mathematics and statistics. In: Kalz, M and Ras, E eds. Computer Assisted Assessment. Research into E-Assessment. Communications in Computer and Information Science (439). Cham: Springer, pp. 67–78.

DOI (Digital Object Identifier) Link:	https://doi.org/10.1007/978-3-319-08657-6_7
Google Scholar:	Look up in Google Scholar

Abstract

Learning analytics seeks to enhance the learning process through systematic measurements of learning related data and to provide informative feedback to learners and teachers, so as to support the regulation of the learning. Track data from technology enhanced learning systems constitute the 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 [1]: an infrastructure that combines learning dispositions data with data extracted from computer assisted, formative assessments. In a large introductory quantitative methods module based on the principles of blended learning, combining face-to-face problem-based learning sessions with e-tutorials, we investigate the predictive power of learning dispositions, outcomes of continuous formative assessments and other system generated data in modeling student performance 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.

Item Type:	Book Section
Copyright Holders:	2014 Springer International Publishing Switzerland
ISBN:	3-319-08656-1, 978-3-319-08656-9
ISSN:	1865-0929
Extra Information:	International Conference, CAA 2014, Zeist, The Netherlands, June 30 – July 1, 2014. Proceedings Series ISSN: 1865-0929
Academic Unit/School:	Learning and Teaching Innovation (LTI) > Institute of Educational Technology (IET) Learning and Teaching Innovation (LTI)
Item ID:	41826
Depositing User:	Bart Rienties
Date Deposited:	13 Jan 2015 09:25
Last Modified:	07 Dec 2018 10:28
URI:	http://oro.open.ac.uk/id/eprint/41826
Share this page:

Metrics

Altmetrics from Altmetric	Citations from Dimensions

Actions (login may be required)

RDF+XMLBibTeXRIOXX2 XMLRDF+N-TriplesJSONDublin CoreAtomSimple MetadataReferMETSHTML CitationASCII CitationRefMan RIS Format (UTF-8)OpenURL ContextObjectEndNoteMODSOpenURL ContextObject in SpanMPEG-21 DIDLEP3 XMLRefMan RIS FormatRDF+N3