Research output

In search for the most informative data for feedback generation: Learning analytics in a data-rich context

Research output: Contribution to journalArticleAcademicpeer-review

Standard

In search for the most informative data for feedback generation: Learning analytics in a data-rich context. / Tempelaar, D.T.; Rienties, B.C.; Giesbers, B.

In: Computers in Human Behavior, Vol. 47, 06.2015, p. 157-167.

Research output: Contribution to journalArticleAcademicpeer-review

Harvard

APA

Vancouver

Author

Bibtex

@article{180affaca4504ebcb5095544a3cad8cd,
title = "In search for the most informative data for feedback generation: Learning analytics in a data-rich context",
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.Data source: Data sets by 1st author",
keywords = "Blended learning, Dispositional learning analytics, e-Tutorials, Formative assessment, Learning dispositions, MANAGEMENT-SYSTEMS, STUDENTS, ENVIRONMENTS, MOTIVATION, COMMUNITIES, EDUCATION, BEHAVIOR",
author = "D.T. Tempelaar and B.C. Rienties and B. Giesbers",
year = "2015",
month = "6",
doi = "10.1016/j.chb.2014.05.038",
language = "English",
volume = "47",
pages = "157--167",
journal = "Computers in Human Behavior",
issn = "0747-5632",
publisher = "Elsevier Science",

}

RIS

TY - JOUR

T1 - In search for the most informative data for feedback generation: Learning analytics in a data-rich context

AU - Tempelaar, D.T.

AU - Rienties, B.C.

AU - Giesbers, B.

PY - 2015/6

Y1 - 2015/6

N2 - 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.Data source: Data sets by 1st author

AB - 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.Data source: Data sets by 1st author

KW - Blended learning

KW - Dispositional learning analytics

KW - e-Tutorials

KW - Formative assessment

KW - Learning dispositions

KW - MANAGEMENT-SYSTEMS

KW - STUDENTS

KW - ENVIRONMENTS

KW - MOTIVATION

KW - COMMUNITIES

KW - EDUCATION

KW - BEHAVIOR

U2 - 10.1016/j.chb.2014.05.038

DO - 10.1016/j.chb.2014.05.038

M3 - Article

VL - 47

SP - 157

EP - 167

JO - Computers in Human Behavior

T2 - Computers in Human Behavior

JF - Computers in Human Behavior

SN - 0747-5632

ER -