The popularity of intelligent tutoring systems (ITSs) is increasing rapidly. In order to make learning environments more efficient, researchers have been exploring the possibility of an automatic adaptation of the learning environment to the learner or the context. One of the possible adaptation techniques is adaptive item sequencing by matching the difficulty of the items to the learner's knowledge level. This is already accomplished to a certain extent in adaptive testing environments, where the test is tailored to the person's ability level by means of the item response theory (IRT). Even though IRT has been a prevalent computerized adaptive test (CAT) approach for decades and applying IRT in item-based ITSs could lead to similar advantages as in CAT (e.g. higher motivation and more efficient learning), research on the application of IRT in such learning environments is highly restricted or absent. The purpose of this paper was to explore the feasibility of applying IRT in adaptive item-based ITSs. Therefore, we discussed the two main challenges associated with IRT application in such learning environments: the challenge of the data set and the challenge of the algorithm. We concluded that applying IRT seems to be a viable solution for adaptive item selection in item-based ITSs provided that some modifications are implemented. Further research should shed more light on the adequacy of the proposed solutions.
Abstract. This paper reports an interdisciplinary research project on adaptive and pervasive learning environments. Its interdisciplinary nature is built on a firm collaboration between three main research domains, namely, instructional science, methodology, and computer science. In this paper, we first present and discuss mutual, as well as distinctive, vision and goals of each domain from a computer science perspective. Thereafter, we argue for an ontology-driven approach employing ontologies at run-time and development-time where formalized ontologies and rules are considered as main medium of adaptivity, user involvement, and automatic application development. Finally, we introduce a prototype domain context ontology for item-based learning environments and demonstrate its run-time and development-time uses.
We present the design of a study in the field of feedback in adaptive learning environments. The study emphasizes the role of the learners and the control they can exert over the level of feedback. However, not all learners equally benefit from learner control and, hence, individual differences need to be taken into account. We discuss the research questions, the design and methodology of this study. We suggest that both learner characteristics (such as motivation and prior knowledge) and item characteristics (item difficulty level) have differential effects on learner control over feedback.
To evaluate the effect of an alternative guideline on inter-rater agreement of assessments of the activities of daily living (ADL) performed by home care nurses and advising nurses of health insurance organisations.
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