A survey of adaptive context-aware learning environments

Hasanov, Aziz; Laine, Teemu H.; Chung, Tae-Sun

doi:10.3233/ais-190534

Cited by 44 publications

(40 citation statements)

References 115 publications

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“…that do not belong to the designer background. Alternatively, several research studies propose a semantic approach (Hariya et al, 2010;García et al, 2020;Huang et al, 2015;Moitra and Palla, 2016). Given a CAD part, this approach translates the geometry and topology into an ontology that allows the detection of design errors by semantic reasoning.…”

Section: Cognitive Assistantsmentioning

confidence: 99%

CACDA: A knowledge graph for a context-aware cognitive design assistant

Huet

Pinquié

Véron

et al. 2021

Computers in Industry

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The design of complex engineered systems highly relies on a laborious zigzagging between computeraided design (CAD) software and design rules prescribed by design manuals. Despite the emergence of knowledge management techniques (ontology, expert system, text mining, etc.), companies continue to store design rules in large and unstructured documents. To facilitate the integration of design rules and CAD software, we propose a knowledge graph that structures a large set of design rules in a computable format. The knowledge graph organises entities of design rules (nodes), relationships among design rules (edges), as well as contextual information. The categorisation of entities and relationships in four subcontexts: semantic, social, engineering, and IT -facilitates the development of the data model, especially the definition of the "design context" concept. The knowledge graph paves the way to a context-aware cognitive design assistant. Indeed, connected to or embedded in a CAD software, a context-aware cognitive design assistant will capture the design context in near real time and run reasoning operations on the knowledge graph to extend traditional CAD capabilities, such as the recommendation of design rules, the verification of design solutions, or the automation of design routines. Our validation experiment shows that the current version of the context-aware cognitive design assistant is more efficient than the traditional document-based design. On average, participants using an unstructured design rules document have a precision of 0.36 whereas participants using our demonstrator obtain a 0.61 precision score. Finally, designers supported by the design assistant spend more time designing than searching for applicable design rules compared to the traditional design approach.

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Section: Cognitive Assistantsmentioning

confidence: 99%

CACDA: A knowledge graph for a context-aware cognitive design assistant

Huet

Pinquié

Véron

et al. 2021

Computers in Industry

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“…In the traditional classroom and in the online learning environment, the data collection has been limited to the learning behaviors that occurred within the learning management systems (Bernard et al , 2017; Chen et al , 2016; Fan et al , 2020; Foung & Chen, 2019; Sweta & Lal, 2017). With the emergence of context-awareness technology, the sources, types and amount of available learning data have been greatly increased (Hasanov et al , 2019). How to use this data to serve personalized learning is a meaningful question in today’s education (Hwang et al , 2008).…”

Section: Literature Reviewmentioning

confidence: 99%

“…”Learning context” has been defined as the set of entities that contribute to the learner’s situation (Laine & Nygren, 2015). Contextual entities in learning environments might include both external factors and internal factors (Hasanov et al , 2019). Context-awareness has been defined as the process of detecting and analyzing context entities by various devices and analytical methods.…”

Section: Introductionmentioning

confidence: 99%

“…Context-awareness has been defined as the process of detecting and analyzing context entities by various devices and analytical methods. Hasanov et al (2019) classified contexts within learning environments into the dimensions of User, Technical, Spatio-Temporal, Pedagogical and Environmental. Transforming, aggregating and analyzing these data can generate information that is useful in understanding learners’ contexts and that, then, can enable or trigger adaptation (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Learning preference: development in smart learning environments

Zhang

Hung

et al. 2021

IDD

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Purpose The purpose of this study is to conduct a systematic review to understand state-of-art research related to learning preferences from the aspects of impacts, influential factors and evaluation methods. Design/methodology/approach This paper uses the systematic synthesis method to provide state-of-the-art knowledge on learning preference research by summarizing published studies in major databases and attempting to aggregate and reconcile the scientific results from the individual studies. The findings summarize aggregated research efforts and improve the quality of future research. Findings After analyzing existing literature, this study proposed three possible research directions in the future. First, researchers might focus on how to use the real-time tracking mechanism to further understand other impacts of learning preferences within the learning environments. Second, existing studies mainly focused on the influence of singular factors on learning preferences. The joint effects of multiple factors should be an important topic for future research. Finally, integrated algorithms might become the most popular evaluation method of learning preference in the era of smart learning environments. Research limitations/implications This review used the search results generated by Google Scholar and Web of Science databases. There might be published papers available in other databases that have not been taken into account. Originality/value The research summarizes the state-of-art research related to learning preferences. This paper is one of the first to discuss the development of learning preference research in smart learning environments.

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“…The review identifies different context types of which the student, location, and time are the most frequently used types in CAMLA development. Hasanov et al (2019) presented a literature search on Adaptive Context-Aware Learning Environments using a meta-analysis of 53 studies published between 2010 and 2018 identifying variables such as mobile devices, Radio-Frequency Identification, Near-Field Communication, ontology for context modeling approach, context data from the student's profile or location, rule-based adaptation, and informational feedback. The authors proposed a taxonomy of context-aware categories allowing them to propose recommendations for future context-aware research on the adaptation of educational applications.…”

Section: Context-awarenessmentioning

confidence: 99%