A Learning Objects Recommendation Model based on the Preference and Ontological Approaches

Tsai, Kun Hua; Chiu, Ti Kai; Lee, Ming-Che; Wang, Tzone I.

doi:10.1109/icalt.2006.1652359

Cited by 48 publications

(10 citation statements)

References 6 publications

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“…Compared with these fields, and the emergence of the education field, course content recommendation is a new topic, which has only been investigated by several systems over the past few years. Many kinds of research into course recommendation systems that aim to help students to find courses that are suitable for them have been carried out [14][15][16]. Current course recommendation systems collect information from a single data source, including students, university databases, users' course ratings, course histories, past behaviour of students, historical enrolment data and previous students' work histories.…”

Section: Related Workmentioning

confidence: 99%

Using Ontology for Personalised Course Recommendation Applications

Ibrahim¹,

Yang²,

Ndzi³

2017

Computational Science and Its Applications – ICCSA 2017

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The primary data source for universities and courses for students is increasingly becoming the web, and with a vast amount of information about thousands of courses on different websites, it is quite a task to find one that matches a student's needs. That is why we are proposing the "Course Recommendation System", a system that suggests the course best suited for prospective students. As there has been a huge increase in course content on the Internet, finding the course you really need has become time-consuming, so we are proposing to use an ontology-based approach to semantic content recommendation. The aim is to enhance the efficiency and effectiveness of providing students with suitable recommendations. The recommender takes into consideration knowledge about the user (the student's profile) and course content, as well as knowledge about the domain that is being learned. Ontology is used to both models and represent such forms of knowledge. There are four steps to this: extracting information from multiple sources, applying ontologies by using Protégé tools, semantic relevance calculation and refining the recommendation. A personalised, complete and augmented course is then suggested for the student, based on these steps.

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Section: Related Workmentioning

confidence: 99%

Using Ontology for Personalised Course Recommendation Applications

Ibrahim¹,

Yang²,

Ndzi³

2017

Computational Science and Its Applications – ICCSA 2017

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“…Nadolski et al have used simulators to test personalized recommendation algorithms for lifelong learners and self-organized learning networks, to match learners with learning actions (similar to our Learning Nuggets) (Nadolski, et al, 2009). Other researchers have also investigated recommendation algorithms using collaborative filtering, preference-based, neighbor-interest-based, and other data mining techniques (Recker, Walker, & Lawless, 2003;Romero, Ventura, Delgado, & De Bra, 2007); Tsai, Chiu, Lee, & Wang, (Recker, Walker, & Lawless, 2003;Romero, Ventura, Delgado, & De Bra, 2007;Tsai, Chiu, Lee, & Wang, 2006). The Nugget recommendation algorithm identifies and ranks the best Nuggets for a learner that will help her master the topic in the most efficient way possible (i.e.…”

Section: Descriptionmentioning

confidence: 99%

An Asynchronous, Personalized Learning Platform―Guided Learning Pathways (GLP)

Shaw¹,

Larson²,

Sibdari³

2014

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The authors propose that personalized learning can be brought to traditional and non-traditional learners through a synchronous learning platform that recommends to individual learners the learning materials best suited for him or her. Such a platform would allow learners to advance towards individual learning goals at their own pace, with learning materials catered to each learner's interests and motivations. This paper describes the authors' vision and design for a modular, personalized learning platform called Guided Learning Pathways (GLP), and its characteristics and features. We provide detailed descriptions of and propose frameworks for critical modules like the Content Map, Learning Nuggets, and Recommendation Algorithms. A threaded user scenario is provided for each module to help the reader visualize different aspects of GLP. We discuss work done at MIT to support such a platform.

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“…In the literature, a shift may be noticed from architectures focusing on once-only personalized retrieval and recommendations of educational content (e.g. [32,33]), towards more complex architectures combining ITS and EAHS principles for steering and guiding the learning process either from a more global level, e.g. the GRAPPLE project [34] or from more nearby, e.g.…”

Section: Existing Adaptive Learning Systemsmentioning

confidence: 99%

Vocabulary Treatment in Adventure and Role-Playing Games: A Playground for Adaptation and Adaptivity

Cornillie

Jacques

Wannemacker

et al. 2011

Communications in Computer and Information Science

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Although there is pedagogical support for using computer adventure and role-playing games in order to learn a second language (L2), commercial games often lack the instructional qualities for making their language comprehensible for learners. In an interdisciplinary approach, this paper proposes a technique for adapting in-game text in order to teach L2 vocabulary, grounded in research on second language acquisition and adaptive learning systems.

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A Learning Objects Recommendation Model based on the Preference and Ontological Approaches

Cited by 48 publications

References 6 publications

Using Ontology for Personalised Course Recommendation Applications

Using Ontology for Personalised Course Recommendation Applications

An Asynchronous, Personalized Learning Platform―Guided Learning Pathways (GLP)

Vocabulary Treatment in Adventure and Role-Playing Games: A Playground for Adaptation and Adaptivity

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