Tracking a multitude of abilities as they develop

• Published in: British journal of mathematical & statistical psychology 2022-11, Vol.75 (3), p.753-778
• Main Authors: Bolsinova, Maria, Brinkhuis, Matthieu J. S., Hofman, Abe D., Maris, Gunter
• Format: Article
• Language: English
• Subjects: Adaptive systems; Algorithms; Humans; Learning; Machine learning; Mathematics; Ratings; Tracking
• ISSN: 0007-1102; 2044-8317
• DOI: 10.1111/bmsp.12276

Recently, the Urnings algorithm (Bolsinova et al.,  2022, J. R. Stat. Soc. Ser. C Appl. Statistics, 71, 91) has been proposed that allows for tracking the development of abilities of the learners and the difficulties of the items in adaptive learning systems. It is a simple and scalable algorithm which is suited for large‐scale applications in which large streams of data are coming into the system and on‐the‐fly updating is needed. Compared to alternatives like the Elo rating system and its extensions, the Urnings rating system allows the uncertainty of the ratings to be evaluated and accounts for adaptive item selection which, if not corrected for, may distort the ratings. In this paper we extend the Urnings algorithm to allow for both between‐item and within‐item multidimensionality. This allows for tracking the development of interrelated abilities both at the individual and the population level. We present formal derivations of the multidimensional Urnings algorithm, illustrate its properties in simulations, and present an application to data from an adaptive learning system for primary school mathematics called Math Garden.