Measurement of ability in adaptive learning and assessment systems when learners can use on-demand hints

Maria Bolsinova*, Benjamin Deonovic, Meirav Arieli-Attali, Settles Burr, Masato Hagiwara, Gunter Maris

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Adaptive learning and assessment systems support learners in acquiring knowledge and skills in a particular domain. The learners’ progress is monitored through them solving items matching their level and aiming at specific learning goals. Scaffolding and providing learners with hints are powerful tools in helping the learning process. One way of introducing hints is to make hint use the choice of the student. When the learner is certain of their response, they answer without hints, but if the learner is not certain or does not know how to approach the item they can request a hint. We develop measurement models for applications where such on-demand hints are available. Such models take into account that hint use may be informative of ability, but at the same time may be influenced by other individual characteristics. Two modeling strategies are considered: (1) The measurement model is based on a scoring rule for ability which includes both response accuracy and hint use. (2) The choice to use hints and response accuracy conditional on this choice are modeled jointly using Item Response Tree models. The properties of different models and their implications are discussed. An application to data from Duolingo, an adaptive language learning system, is presented. Here, the best model is the scoring-rule-based model with full credit for correct responses without hints, partial credit for correct responses with hints, and no credit for all incorrect responses. The second dimension in the model accounts for the individual differences in the tendency to use hints.
Original languageEnglish
Pages (from-to)219–235
JournalApplied Psychological Measurement
Volume46
Issue number3
DOIs
Publication statusPublished - 2022

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