A survey of adaptive context-aware learning environments

Document identifier: oai:DiVA.org:ltu-76056
Access full text here:10.3233/AIS-190534
Keyword: Natural Sciences, Computer and Information Sciences, Computer Sciences, Naturvetenskap, Data- och informationsvetenskap, Datavetenskap (datalogi), Media and Communication Technology, Medieteknik, Pervasive Mobile Computing, Distribuerade datorsystem
Publication year: 2019
Relevant Sustainable Development Goals (SDGs):
SDG 4 Quality educationSDG 3 Good health and wellbeing
The SDG label(s) above have been assigned by OSDG.ai


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 Previously, several related surveys have been conducted, but they are either outdated or they donot consider the important aspects of context-awareness, adaptation and pedagogy in the domain of ACALEs.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.


Aziz Hasanov

Ajou University
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Teemu Laine

Luleå tekniska universitet; Datavetenskap
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Tae-Sun Chung

Ajou University
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