A Context-Aware Mobile Learning System Using Dynamic Content Adaptation for Personalized Learning

Curum, Brita; Chellapermal, Nigel; Khedo, Kavi Kumar

doi:10.1007/978-3-319-52171-8_27

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…[25] compiled the challenges faced by context-aware recommendation systems and demonstrated the ability of bio-inspired computing techniques and statistical computing techniques to handle these challenges. [26] presented a context-aware system that adapts the learning content based on the contexts of the user. Context awareness, such as the user's maturity, the cognitive load of the Toward a Context-Aware Course-Planning Model in Pervasive Learning Environments learner, and device configuration, was used to retrieve context data.…”

Section: Related Workmentioning

confidence: 99%

Toward a Context-Aware Course-Planning Model in Pervasive Learning Environments

Kosrane,

Gharzouli

2023

Int. J. Emerg. Technol. Learn.

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This article proposes a context-aware course-planning model for pervasive learning environments. The model considers learners’ preferences, including their learning styles, locations, activities, and devices. The model generates four-dimensional contexts based on these preferences, each dimension being a weighted vector of visual, aural, read/write, and kinesthetic (VARK) features. Using a content-based similarity algorithm, the model adapts the course content to each learner’s context. The courses are created in a sequence of rings, each containing all possible learning materials (LMs) that have the same content in different formats. Each LM is represented by a vector with weights like those of the learners’ context dimensions. The model generates all possible plans based on the predefined contexts of the learner, detects the learner’s actual context, and adapts the content accordingly. The goal of the model is to enable learners to learn what they want, in the way they prefer, and to complete courses efficiently, at any time, place, and during any activity, in the appropriate format.

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Section: Related Workmentioning

confidence: 99%

Toward a Context-Aware Course-Planning Model in Pervasive Learning Environments

Kosrane,

Gharzouli

2023

Int. J. Emerg. Technol. Learn.

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“…External dimension also known as extrinsic refers to the context that can be measured by sensors such as temperature, touch, sound, location, light, and many others. Intrinsic or internal context refers to the user's interaction, emotional state, learner's objectives and task to complete [16,17].…”

Section: Context-awarementioning

confidence: 99%

“…Here are nine related studies having been reviewed. The Personalized and Adaptive Context-Aware Mobile Learning (PACAML) [2], Personalized Context-Aware Learning System (PCALS) [3], Mobile Cloud Computing (MCC) [7], Accessible and Personalized Mobile Learning System (APMLS) [9], Units of Learning mobile Player (UoLmP) [5], Mobile Semantic Web Assessment Personalization (MobiSWAP) [6], Personal Data Locker (PDL) [8], Mobile Response System (MRS) [4], and Web-based Learning Platform (WLP) [16].…”

Section: Mobile Cloud Learning (Mcl) Systemsmentioning

confidence: 99%

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Semantic-Based Dynamic Service Adaptation in Context-Aware Mobile Cloud Learning

Muhamad

Admodisastro

Osman

et al. 2022

Cybernetics and Information Technologies

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Self-adaptable system concerns on service adaptation whenever errors persist within the system. Changes in contextual information such as networks or sensors will affect the system’s effectiveness because the service adaptation process is not comprehensively handled in those contexts. Besides, the correctness to get the most equivalence services to be substituted is limitedly being addressed from previous works. A dynamic service adaptation framework is introduced to monitor and run a reasoning control to solve these issues. Hence, this paper presents a case study to proof the dynamic service adaptation framework that leverages on semantic-based approach in a context-aware environment. The evaluation of the case study resulted in a significant difference for the effectiveness at a 95% confidence level, which can be interpreted to confirm that the framework is promising to be used in operating dynamic adaptation process in a pervasive environment.

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“…The correctness of the services is substantial for the continuity of user activity on the system. However, some researches [2], [8]- [11], [17] have not adequately addressed the correctness to get the most equivalent services to be deployed. Instead, their solutions have devoted to the aspects of effectiveness, reliability, availability, scalability, performance, and security.…”

Section: Related Workmentioning

confidence: 99%

The Correctness of Service in Runtime Adaptation for Context-Aware Mobile Cloud Learning

et.al¹

2021

TURCOMAT

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Service-Based Applications (SBAs) have become increasingly pervasive. These applications rely on the third-parties services available on the cloud, and services must be aware of and adapt to their changing contexts in highly dynamic environments. SBAs with context-aware capabilities have provided the users with personalized services based on their user's (intrinsic) and device's (extrinsic) contextual information, as well as the Quality of Services (QoS). The correctness of service substitution in runtime adaptation is substantial for the continuity of user activity on the system. In Mobile Cloud Learning (MCL) environment most works only focus on intrinsic context factors such as learner's profile, learner's location, etc. We then introduce a comprehensive Dynamic Service Adaptation of Context-Aware Mobile Cloud Learning (DACAMoL), which is designed to reason for bothcontextual factors and QoS inservice discovery, ranking, and selection. The framework represents the contextual information, service descriptions, and QoS using a semantic-based approach to improve the correctness of service substitution. In this paper, wepresent a quasi-experiment study to demonstrate the DACAMoL framework with a mobile app called Mudahnya BM. Mudahnya BM is a learning app to learn basic knowledge of Malay language that build using RESTful backend services. The study involved 30 participants and 33 randomized scenarios tested using One-Sample Wilcoxon Signed Rank test. The results show significantly better service substitutions with 32 out of 33educational servicesare correctly adapted (i.e. 95% of the population).

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A Context-Aware Mobile Learning System Using Dynamic Content Adaptation for Personalized Learning

Cited by 5 publications

References 13 publications

Toward a Context-Aware Course-Planning Model in Pervasive Learning Environments

Toward a Context-Aware Course-Planning Model in Pervasive Learning Environments

Semantic-Based Dynamic Service Adaptation in Context-Aware Mobile Cloud Learning

The Correctness of Service in Runtime Adaptation for Context-Aware Mobile Cloud Learning

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