Integration of recommendations and adaptive hypermedia into java tutoring system

Klašnja-Milićević, Aleksandra; Vesin, Boban; Ivanović, Mirjana; Budimac, Zoran

doi:10.2298/csis090608021k

Cited by 32 publications

(21 citation statements)

References 12 publications

(23 reference statements)

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“…Papanikolaou et al [19] choose Honey and Mumford's model in order for presentation of the educational material on each of the performance levels and Drissi & Amirat [30] adopt the same model because is appropriate for presentation of pages and links at a hypermedia course. As far as the first family and Dunn and Dunn's Learning Style Model is concerned, its high index of reliability and validity, the breadth of six areas of learning preferences that covers [6] and the different presentation modes that serves, make it preferable [38]. According to [28], the VARK Learning Style Model is considered as the simplest model with the greatest influence.…”

Section: Brief Description Of Aehs Based On Learning Stylesmentioning

confidence: 99%

Adaptive Educational Hypermedia Systems based on Learning Styles: the Case of Adaptation Rules

Tsortanidou

Karagiannidis

Koumpis

2017

Int. J. Emerg. Technol. Learn.

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Abstract-This paper investigates the pedagogical basis of Adaptive Educational Hypermedia Systems (AEHS) that incorporate Learning Styles in order to accommodate user's learning style preferences and needs. Therefore, AES adapt the learning content, its presentation and navigation to the user's learning style preferences. We collect thirty three (33) Adaptive and Intelligent Web-based Educational Systems (AIWBES) that incorporate learning styles and discuss twenty of them, namely the AEHS, as the remaining are Intelligent Tutorng Systems. The main achievement of this work is the investigation of AEHS' pedagogical basis in terms of adaptation rules. We conclude that these systems follow similar patterns in their adaptation logic.

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Section: Brief Description Of Aehs Based On Learning Stylesmentioning

confidence: 99%

Adaptive Educational Hypermedia Systems based on Learning Styles: the Case of Adaptation Rules

Tsortanidou

Karagiannidis

Koumpis

2017

Int. J. Emerg. Technol. Learn.

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“…Future improvements will be concentrated on integrating it into our existing, fully-featured web-based e-learning architecture Protus [10,19], but it can also be integrated in some other types of available learning systems [14,18]. In order to achieve adaptability and personalization, Protus incorporates several models, including: Domain model, which serves as a storage of the learning material, Student model for maintaining both static and dynamic information about each individual student, Application model, which applies different strategies on the input received from the learner model in order to ensure efficient personalization, and the Adaptation model that follows the instructional directions provided by the application module in order to organize learning resources into a navigational sequence tailored to the particular learner.…”

Section: Discussionmentioning

confidence: 99%

“…At a later stage HAPA could be included as a component in other tutoring systems devoted to learning programming languages. Our intention is to incorporate it in Protus, a tutoring system we designed to help learners in learning essentials of Java programming language [10,19].…”

Section: Hapa System Overviewmentioning

confidence: 99%

HAPA: Harvester and Pedagogical Agents in E-learning Environments

Ivanović¹,

Mitrović²,

Budimac³

et al. 2015

INT J COMPUT COMMUN

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In the field of e-learning and tutoring systems two categories of software agents are of the special interest: harvester and pedagogical agents. This paper proposes a novel e-learning system that successfully combines both of these agent categories and introduces two distinct sub-types of pedagogical agents helpful and misleading. Whereas helpful agents provide the correct guidance for the given problem, misleading agents try to guide the learning process in the wrong direction by offering false hints and inadequate solutions. The rationale behind this approach is to motivate students not to trust the agent's instructions blindly, but to employ critical thinking. Consequently, students will be put in a "softly stressed" environment in order to prepare them for real working environments in their future work in companies. Nevertheless students themselves will decide on the correct solution to the problem in question.

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“…[1], Mag [25], AHyCo [27], etc. have been developed to overcome the problem of disorientation by providing appropriate contents for users with different backgrounds and interests based on the user model, adaptation model, and domain model.…”

Section: Related Workmentioning

confidence: 99%

“…Mag [25] is a tutoring system that supports learning programming languages in various courses. It provides adaptive learning and personalization of a content delivery based on learner model.…”

Section: Related Workmentioning

confidence: 99%

Authoring Adaptive Hypermedia using Ontologies

Jung

Park

2014

INT J COMPUT COMMUN

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Adaptive hypermedia has been developed to overcome the problems of disorientation by providing personalized presentation and link structure. An adaptive hypermedia system consists of an adaptation model, a domain model, and a user model. The user model describes various aspects of a user such as interests, knowledge, preferences, etc. The domain model describes the whole knowledge accessible in adaptive hypermedia. The adaptation model consists of adaptation rules that define both how to generate the personalized presentation and update the user model [12]. Authoring adaptive hypermedia typically starts by designing a domain model so that apprropiate adaptation model and user model can be created based the domain model. While there are authoring tools developed for creating domain model of an adaptive hypermedia, authors need to manually create basic concepts as well as their relationships for a domain of interests. In this paper, we present a system that transforms an ontology into the domain and adaptation model of adaptive hypermedia so that the authors can generate adaptive hypermedia easily. The system transforms classes and relationships between the classes defined in OWL ontology [7] into concepts and relationships between the concepts defined in the domain model of AHA! system which is one of best known open source general-purpose adaptive hypermedia systems [1]. Using our system, authors can utilize well-defined knowledge structure in ontologies for authoring adaptive hypermedia. On top of this, since designing domain model is generally an initial step to author adaptive hypermedia, our system can help authors reduce tasks to create adaptive hypermedia by automatically generating domain model from an ontology.

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Integration of recommendations and adaptive hypermedia into java tutoring system

Cited by 32 publications

References 12 publications

Adaptive Educational Hypermedia Systems based on Learning Styles: the Case of Adaptation Rules

Adaptive Educational Hypermedia Systems based on Learning Styles: the Case of Adaptation Rules

HAPA: Harvester and Pedagogical Agents in E-learning Environments

Authoring Adaptive Hypermedia using Ontologies

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