Ontology-based framework for context-aware mobile learning

Berri, Jawad; Benlamri, Rachid; Atif, Yacine

doi:10.1145/1143549.1143811

Cited by 52 publications

(34 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, SB GLOs can be prepared in advanced and taken from the library (if the existing item fits the educational needs, e.g. the number of stages; otherwise the teacher creates the SB GLO anew using the refactoring tool (Bespalova et al, 2013)). Note that the tool may be used in two modes: (a) automatic staging (the stages are formed using the context information {HP, IP, LP}); (b) manual staging (user is able to allocate parameters to stages interactively according to his/her vision).…”

Section: Case Study and Resultsmentioning

confidence: 99%

“…Many enumerations are defined for mobile learning. For example, Berri et al (Berri et al, 2006) distinguish between technical and learner context elements. The first category deals with the technical aspects of mobile devices, their operational environment and constraints.…”

Section: Context-related Issues In Telmentioning

confidence: 99%

“…The tool (Bespalova et al, 2013) fully hides the technological mechanism of staging (for more details, see Case Study, Section 7).…”

Section: Property 2 Values Of Different Parameter May Interact (Iementioning

confidence: 99%

“…The designer responsibility is to map the domain model onto the solution domain model in creating the initial GLO. That can be done manually or using the adequate tools (Bespalova et al, 2013).…”

Section: Designer's Perspectivementioning

confidence: 99%

See 3 more Smart Citations

Stage-Based Generative Learning Object Model for Automated Content Adaptation

Štuikys¹,

Burbaitė²,

Bespalova³

et al. 2017

BJMC

View full text Add to dashboard Cite

This paper introduces a Stage-Based (SB) Generative Learning Object (GLO) model to specify the learning content. Capabilities of the model are the content automatic generation and adaptation. Externally, our model has a similar structure as the known two-level generic models (i.e. metadata and content implementation). The internal structure, however, is quite different in both parts. The use of the external parameterization technology based on pre-programming predefines the internal structure. The SB model implements the deep internal staging by allocating parameters and functions (objects) into predefined stages according to the given context. The essence of the approach is the SB de-activation and activation of the objects within the specification. That ensures the automatic SB generation and flexibility for adaptation. We analyze the SB model capabilities, the use scenarios and processes, present a case study and extended results of using and evaluation in the robot-oriented computer science education.

show abstract

Section: Case Study and Resultsmentioning

confidence: 99%

Section: Context-related Issues In Telmentioning

confidence: 99%

“…The tool (Bespalova et al, 2013) fully hides the technological mechanism of staging (for more details, see Case Study, Section 7).…”

Section: Property 2 Values Of Different Parameter May Interact (Iementioning

confidence: 99%

Section: Designer's Perspectivementioning

confidence: 99%

See 2 more Smart Citations

Stage-Based Generative Learning Object Model for Automated Content Adaptation

Štuikys¹,

Burbaitė²,

Bespalova³

et al. 2017

BJMC

View full text Add to dashboard Cite

show abstract

“…A mechanism to support adaptation in m-learning systems has been proposed [26]. An ontology-based framework for context-aware mobile learning has been proposed [27]. Context-aware social presence mechanisms would support a learner [28].…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Mobile Learning

Economides

The Open Knowlege Society. A Computer Science and Information Systems Manifesto

View full text Add to dashboard Cite

Recent developments on mobile devices and networks enable new opportunities for mobile learning anywhere, anytime. Furthermore, recent advances on adaptive learning establish the foundations for personalized learning adapted to the characteristics of each individual learner. A mobile learner would perform an educational activity using the infrastructure (e.g. handheld devices, networks) in an environment (e.g. outdoors). In order to provide personalization, an adaptation engine adapts the educational activity and the infrastructure according to the context. The context is described by the learner's state, the educational activity's state, the infrastructure's state, and the environment's state. Furthermore, each one of these states is described by its dimensions. Many examples illustrate the adaptation decisions.

show abstract

Reuse Framework of the LO Domain

Štuikys

2015

Smart Learning Objects for Smart Education in Computer Science

View full text Add to dashboard Cite

Ontology-based framework for context-aware mobile learning

Cited by 52 publications

References 7 publications

Stage-Based Generative Learning Object Model for Automated Content Adaptation

Stage-Based Generative Learning Object Model for Automated Content Adaptation

Context-Aware Mobile Learning

Reuse Framework of the LO Domain

Contact Info

Product

Resources

About