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Towards actionable learning analytics using dispositions

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Towards actionable learning analytics using dispositions. / Tempelaar, Dirk; Rienties, Bart; Nguyen, Quan.

In: IEEE Transactions on Learning Technologies, Vol. 10, No. 1, 1, 2017, p. 6-16.

Research output: Contribution to journalArticleAcademicpeer-review

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Tempelaar, Dirk ; Rienties, Bart ; Nguyen, Quan. / Towards actionable learning analytics using dispositions. In: IEEE Transactions on Learning Technologies. 2017 ; Vol. 10, No. 1. pp. 6-16.

Bibtex

@article{55720e8ed46a493ba64d4fd7508144d7,
title = "Towards actionable learning analytics using dispositions",
abstract = "Studies in the field of learning analytics (LA) have shown students’ demographics and learning management system (LMS) data to be effective identifiers of “at risk” performance. However, insights generated by these predictive models may not be suitable for pedagogically informed interventions due to the inability to explain why students display these behavioral patterns. Therefore, this study aims at providing explanations of students’ behaviors on LMS by incorporating dispositional dimensions (e.g., self-regulation and emotions) into conventional learning analytics models. Using a combination of demographic, trace, and self-reported data of eight contemporary social-cognitive theories of education from 1,069 students in a blended introductory quantitative course, we demonstrate the potential of dispositional characteristics of students, such as procrastination and boredom. Our results highlight the need to move beyond simple engagement metrics, whereby dispositional learning analytics provide an actionable bridge between learning analytics and educational intervention.",
keywords = "dispositional learning analytics, actionable learning analytics, technology enhanced learning, learning dispositions, predictive models",
author = "Dirk Tempelaar and Bart Rienties and Quan Nguyen",
note = "Self collected survey and trace data",
year = "2017",
doi = "10.1109/TLT.2017.2662679",
language = "English",
volume = "10",
pages = "6--16",
journal = "IEEE Transactions on Learning Technologies",
issn = "1939-1382",
publisher = "IEEE",
number = "1",

}

RIS

TY - JOUR

T1 - Towards actionable learning analytics using dispositions

AU - Tempelaar, Dirk

AU - Rienties, Bart

AU - Nguyen, Quan

N1 - Self collected survey and trace data

PY - 2017

Y1 - 2017

N2 - Studies in the field of learning analytics (LA) have shown students’ demographics and learning management system (LMS) data to be effective identifiers of “at risk” performance. However, insights generated by these predictive models may not be suitable for pedagogically informed interventions due to the inability to explain why students display these behavioral patterns. Therefore, this study aims at providing explanations of students’ behaviors on LMS by incorporating dispositional dimensions (e.g., self-regulation and emotions) into conventional learning analytics models. Using a combination of demographic, trace, and self-reported data of eight contemporary social-cognitive theories of education from 1,069 students in a blended introductory quantitative course, we demonstrate the potential of dispositional characteristics of students, such as procrastination and boredom. Our results highlight the need to move beyond simple engagement metrics, whereby dispositional learning analytics provide an actionable bridge between learning analytics and educational intervention.

AB - Studies in the field of learning analytics (LA) have shown students’ demographics and learning management system (LMS) data to be effective identifiers of “at risk” performance. However, insights generated by these predictive models may not be suitable for pedagogically informed interventions due to the inability to explain why students display these behavioral patterns. Therefore, this study aims at providing explanations of students’ behaviors on LMS by incorporating dispositional dimensions (e.g., self-regulation and emotions) into conventional learning analytics models. Using a combination of demographic, trace, and self-reported data of eight contemporary social-cognitive theories of education from 1,069 students in a blended introductory quantitative course, we demonstrate the potential of dispositional characteristics of students, such as procrastination and boredom. Our results highlight the need to move beyond simple engagement metrics, whereby dispositional learning analytics provide an actionable bridge between learning analytics and educational intervention.

KW - dispositional learning analytics

KW - actionable learning analytics

KW - technology enhanced learning

KW - learning dispositions

KW - predictive models

U2 - 10.1109/TLT.2017.2662679

DO - 10.1109/TLT.2017.2662679

M3 - Article

VL - 10

SP - 6

EP - 16

JO - IEEE Transactions on Learning Technologies

T2 - IEEE Transactions on Learning Technologies

JF - IEEE Transactions on Learning Technologies

SN - 1939-1382

IS - 1

M1 - 1

ER -