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A survey of adaptive context-aware learning environments
Ajou University.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0001-5966-992x
Ajou University.
2019 (Engelska)Ingår i: Journal of Ambient Intelligence and Smart Environments, ISSN 1876-1364, E-ISSN 1876-1372, Vol. 11, nr 5, s. 403-428Artikel, forskningsöversikt (Refereegranskat) Published
Abstract [en]

Adaptive context-aware learning environments (ACALEs) can detect the learner’s context and adapt learning materi-als to match the context. The support for context-awareness and adaptation is essential in these systems so that they can makelearning contextually relevant. Previously, several related surveys have been conducted, but they are either outdated or they donot consider the important aspects of context-awareness, adaptation and pedagogy in the domain of ACALEs. To alleviate this,a comprehensive literature search on ACALEs was first performed. After filtering the results, 53 studies that were publishedbetween 2010 and 2018 were analyzed. The highlights of the results are: (i) mobile devices (PDAs, mobile phones, smartphones)are the most common client types, (ii) RFID/NFC are the most common sensors, (iii) ontology is the most common context mod-eling approach, (iv) context data typically originates from the learner profile or the learner’s location, (v) rule-based adaptationis the most used adaptation mechanism, and (vi) informative feedback is the most common feedback type. Additionally, we con-ducted a trend analysis on technology usage in ACALEs throughout the covered timespan, and proposed a taxonomy of contextcategories as well as several other taxonomies for describing various aspects of ACALEs. Finally, based on the survey results,directions for future research in the field were given. These results can be of interest to educational technology researchers andto developers of adaptive and context-aware applications.

Ort, förlag, år, upplaga, sidor
IOS Press, 2019. Vol. 11, nr 5, s. 403-428
Nationell ämneskategori
Datavetenskap (datalogi) Medieteknik
Forskningsämne
Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-76056DOI: 10.3233/AIS-190534ISI: 000486679700003OAI: oai:DiVA.org:ltu-76056DiVA, id: diva2:1352494
Anmärkning

Validerad;2019;Nivå 2;2019-09-20 (johcin)

Tillgänglig från: 2019-09-19 Skapad: 2019-09-19 Senast uppdaterad: 2020-03-30Bibliografiskt granskad

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