Adaptive item-based learning environments based on the item response theory: possibilities and challenges.
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 tec...
| Publicado en: | Journal of Computer Assisted Learning Vol. 26; no. 6; pp. 549 - 563 |
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| Autores principales: | , , |
| Formato: | tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Dec2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104813133&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104813133 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Dec2010 vid: 26 iid: 6 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104813133 2010863257 10.1111/j.1365-2729.2010.00368.x 104813133 ppf: 549 ppct: 14 formats: tig: atl: Adaptive item-based learning environments based on the item response theory: possibilities and challenges. aug: au: Wauters, K. Desmet, P. Van den Noortgate, W. affil: iTEC, Interdisciplinary Research on Technology, Education and Communication, Katholieke Universiteit Leuven, 8500 Kortrijk, Belgium sug: subj: Computerized Adaptive Testing Learning Environment Learning Methods Learning Theory Algorithms Test Construction Test Taking ab: 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. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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