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Erschienen in:
Learning Analytics in Higher Education—A Literature Review Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

4. A Review of Recent Advances in Adaptive Assessment

verfasst von : Jill-Jênn Vie, Fabrice Popineau, Éric Bruillard, Yolaine Bourda

Erschienen in: Learning Analytics: Fundaments, Applications, and Trends

Verlag: Springer International Publishing

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Abstract

Computerized assessments are an increasingly popular way to evaluate students. They need to be optimized so that students can receive an accurate evaluation in as little time as possible. Such optimization is possible through learning analytics and computerized adaptive tests (CATs): the next question is then chosen according to the previous responses of the student, thereby making assessment more efficient. Using the data collected from previous students in non-adaptive tests, it is thus possible to provide formative adaptive tests to new students by telling them what to do next. This chapter reviews several models of CATs found in various fields, together with their main characteristics. We then compare these models empirically on real data. We conclude with a discussion of future research directions for computerized assessments.

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Vorheriges Kapitel A Landscape of Learning Analytics: An Exercise to Highlight the Nature of an Emergent Field
Nächstes Kapitel Data-Driven Personalization of Student Learning Support in Higher Education
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Metadaten
Titel
A Review of Recent Advances in Adaptive Assessment
verfasst von
Jill-Jênn Vie
Fabrice Popineau
Éric Bruillard
Yolaine Bourda
Copyright-Jahr
2017
Buch
Learning Analytics: Fundaments, Applications, and Trends

Print ISBN: 978-3-319-52976-9

Electronic ISBN: 978-3-319-52977-6

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-52977-6_4