A Review of Recent Advances in Adaptive Assessment

• Published in: Learning Analytics: Fundaments, Applications, and Trends Vol. 94; pp. 113 - 142
• Main Authors: Vie, Jill-Jênn, Popineau, Fabrice, Bruillard, Éric, Bourda, Yolaine
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 01.01.2017; Springer International Publishing
• Series: Studies in Systems, Decision and Control
• Subjects: Adaptive testing; Artificial intelligence; Cognitive diagnosis models; Computer Science; Educational equipment & technology, computer-aided learning (CAL); Item response theory; Knowledge space theory; Latent knowledge extraction; Machine Learning; Q-matrix; Teaching skills & techniques; Technology for Human Learning; knowledge space theory; item response theory; cognitive diagnosis models; q-matrix; adaptive testing; latent knowledge extraction
• ISBN: 9783319529769; 3319529765
• ISSN: 2198-4182; 2198-4190
• DOI: 10.1007/978-3-319-52977-6_4

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.