Intelligent web-based learning system with personalized learning path guidance

Chen, Chin‐Ming

doi:10.1016/j.compedu.2007.08.004

Cited by 313 publications

(137 citation statements)

References 13 publications

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“…From a perspective based on the design, analysis and scoring of tests, the personalization of e-learning systems has been approached by using the Item Response Theory (PEL-IRT) which, by considering the difficulty of the learning materials to be provided and the ability of the students, finds personalized learning paths (Chen, Lee, & Chen, 2005). Another work based on the students' results of pre-tests, has led to a genetic-based customized e-learning system which conducts to a personalized curriculum sequencing (Chen, 2008). Also, a real-time assessment of students' productivity and interest in learning by using a Recommender System has been considered in (Kaklauskas et al, 2013).…”

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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“…Therefore, development of learning environments with individualized learning mechanisms is an important research topic. Researchers are interested in developing learning environments, which could adaptively provide learning path (Chen, 2008), but the challenge is the development of advanced learning applications that include intelligence and adaptivity (Brusilovsky and Peylo, 2003).…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Learning Support System

Gavriushenko

Khriyenko

Tuhkala

2017

Proceedings of the 9th International Conference on Computer Supported Education

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Abstract:Fast-growing technologies are shaping many aspects of societies. Educational systems, in general, are still rather traditional: learner applies for school or university, chooses the subject, takes the courses, and finally graduates. The problem is that labor markets are constantly changing and the needed professional skills might not match with the curriculum of the educational program. It might be that it is not even possible to learn a combination of desired skills within one educational organization. For example, there are only a few universities that can provide high-quality teaching in several different areas. Therefore, learners may have to study specific modules and units somewhere else, for example, in massive open online courses. A person, who is learning some particular content from outside of the university, could have some knowledge gaps which should be recognized. We argue that it is possible to respond to these challenges with adaptive, intelligent, and personalized learning systems that utilize data analytics, machine learning, and Semantic Web technologies.In this paper, we propose a model for an Intelligent Learning Support System that guides learner during the whole lifecycle using semantic annotation methodology. Semantic annotation of learning materials is done not only on the course level but also at the content level to perform semantic reasoning about the possible learning gaps. Based on this reasoning, the system can recommend extensive learning material.

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“…Hence, the objects are selected and reordered according to a user query and its classification. Chen (Chen, 2008) present an evolutionary approach that uses a genetic algorithm to generate a learning plan. The genetic approach is based on a pretest performed by students, where missed concepts help in creating new learning plans, according to concepts and levels of difficulty of the learning objects.…”

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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Intelligent web-based learning system with personalized learning path guidance

Cited by 313 publications

References 13 publications

Student-oriented planning of e-learning contents for Moodle

Student-oriented planning of e-learning contents for Moodle

An Intelligent Learning Support System

Towards Automatic Building of Learning Pathways

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