An ontology-based planning system for e-course generation

Kontopoulos, Efstratios; Vrakas, Dimitris; Kokkoras, Fotios; Bassiliades, Nick; Vlahavas, Ioannis

doi:10.1016/j.eswa.2007.07.034

Cited by 63 publications

(42 citation statements)

References 5 publications

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“…Such a work takes into consideration general students' behavior without focusing on specific learning contents. On the other hand, similarly to our idea, intelligent planning has been used for learning paths' personalization (Kontopoulos, Vrakas, Kokkoras, Bassiliades, & Vlahavas, 2008). That work focuses on creating a new planning ontology from the e-learning information and use standard planners to solve the problem.…”

Section: Learning Management Systemsmentioning

confidence: 99%

Student-oriented planning of e-learning contents for Moodle

Caputi

Garrido

2015

Journal of Network and Computer Applications

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We present a way to automatically plan student-oriented learning contents in Moodle. Rather than offering the same contents for all students, we provide personalized contents according to the students' background and learning objectives. Although curriculum personalization can be faced in several ways, we focus on Artificial Intelligence (AI) planning as a very useful formalism for mapping actions, i.e. learning contents, in terms of preconditions (precedence relationships) and causal effects to find plans, i.e. learning paths that best fit the needs of each student. A key feature is that the learning path is generated and shown in Moodle in a seamless way for both the teacher and student, respectively. We also include some experimental results to demonstrate the scalability and viability of our approach.

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Section: Learning Management Systemsmentioning

confidence: 99%

Student-oriented planning of e-learning contents for Moodle

Caputi

Garrido

2015

Journal of Network and Computer Applications

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“…In scenarios where metadata for the given learning objects is available systems like PASER (Kontopoulos et al, 2008) allow the calculation of a learning path. In our scenario we address informal learning situations in which this metadata is not available.…”

Section: Related Workmentioning

confidence: 99%

Towards Automatic Building of Learning Pathways

Siehndel

Kawase

Nunes

et al. 2014

Proceedings of the 10th International Conference on Web Information Systems and Technologies

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Abstract:Learning material usually has a logical structure, with a beginning and an end, and lectures or sections that build upon one another. However, in informal Web-based learning this may not be the case. In this paper, we present a method for automatically calculating a tentative order in which objects should be learned based on the estimated complexity of their contents. Thus, the proposed method is based on a process that enriches textual objects with links to Wikipedia articles, which are used to calculate a complexity score for each object. We evaluated our method with two different datasets: Wikipedia articles and online learning courses. For Wikipedia data we achieved correlations between the ground truth and the predicted order of up to 0.57 while for subtopics inside the online learning courses we achieved correlations of 0.793.

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“…Particularly, AI planning techniques have been successfully applied to the construction of adapted courses as a means to bring the right content to the right person [10,12]. However, designing a course usually requires to deal with aspects such as group interaction, collaboration and sharing of some particular (and perhaps costly) resources.…”

Section: Introductionmentioning

confidence: 99%

“…This personalization perspective has been addressed by using different techniques, such as adjacency matrices, integer programming models, neural networks and intelligent planning techniques [10,11,12,9]. Particularly, AI planning techniques have been successfully applied to the construction of adapted courses as a means to bring the right content to the right person [10,12].…”

Section: Introductionmentioning

confidence: 99%

Assembling Learning Objects for Personalized Learning: An AI Planning Perspective

Garrido

Onaindía

2013

IEEE Intell. Syst.

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The aim of educational systems is to assemble learning objects on a set of topics tailored to the goals and individual students' styles. Given the amount of available learning objects, the challenge of e-learning is to select the proper objects, define their relationships, and adapt their sequencing (i.e. course composition) to the specific needs, objectives and background of the student. This paper describes the general requirements for this course adaptation, the full potential of applying planning techniques on the construction of personalized e-learning routes, and how to accommodate the temporal and resource constraints to make the course applicable in a real scenario.

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An ontology-based planning system for e-course generation

Cited by 63 publications

References 5 publications

Student-oriented planning of e-learning contents for Moodle

Student-oriented planning of e-learning contents for Moodle

Towards Automatic Building of Learning Pathways

Assembling Learning Objects for Personalized Learning: An AI Planning Perspective

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