Personalized recommendation of learning material using sequential pattern mining and attribute based collaborative filtering

Salehi, Mojtaba; Kamalabadi, Isa Nakhai; Ghaznavi‐Ghoushchi, M. B.

doi:10.1007/s10639-012-9245-5

Cited by 71 publications

(43 citation statements)

References 26 publications

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“…The content-based recommendation algorithm only considers the matching of learning resources and users' interest characteristics without considering the similarity between users, which results in only recommending resources that users have learned and are not interested in [8]. But there is no recommendation for learning resources that are not available to users.…”

Section: Content-based Recommendation Algorithmmentioning

confidence: 99%

A Literature Review of Personalized Learning Algorithm

Tang¹,

Wang²

2018

JSS

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With the rapid development of the online learning platform, the learning resources are also unprecedentedly rich. The study of personalized learning algorithms meets the need to provide students with the most suitable resources for learning. This paper constitutes a literature review on personalized learning algorithm at home and abroad. The review aims at elaborating the research based on recommendation system and data mining in personalized learning algorithm and look forward to the future research trend.

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Section: Content-based Recommendation Algorithmmentioning

confidence: 99%

A Literature Review of Personalized Learning Algorithm

Tang¹,

Wang²

2018

JSS

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“…Leaner preference tree (LPT) is brought to limelight to consider the multidimensional-attribute of materials, and learners' rating and energetic pattern dynamic and multi-preference of learners in the multidimensional attribute-based CF technique. In the long run, the recommendation outcomes of the two methods are integrated by means of the cascade, weighted and mixed techniques (Salehi et al, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Personalised e-learning system using learner profile ontology and sequential pattern mining-based recommendation

Muruganandam

Srininvasan

2017

IJBIDM

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Abstract:The ever-zooming issue of personalisation in the domain of the e-learning or the adaptive e-learning has emerged as a hot subject of intriguing debate among the inquisitive investigators in the last few years. In the learning contents module, all the critical learning materials in the shape of text, video or audio are collected and saved by means of the data management approach so as to effectively orchestrate the entire learning material. In the profile ontology module, the learner profile is saved as the ontology with a clear-cut framework and data. The Markov model is also utilised in this regard. In the instructor module, the online tutoring for each and every learner in accordance with the protocol devised by means of the captioned techniques is carried out. The execution is performed through the JAVA programming and protégé device and the performance is subjected to assessment.Keywords: e-learning; prefix-span algorithm; graphical user interface; dataset description.Reference to this paper should be made as follows: Muruganandam, S. and Srininvasan, N. (2017) 'Personalised e-learning system using learner profile ontology and sequential pattern mining-based recommendation', Int.

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“…This model was further updated with the data generated by student's interaction with the system in order to reflect more accurately their current preferences. In [21] they have proposed a new learning material recommender system based on sequential patter mining and multidimensional attribute based collaborative filtering. They combined the results of two approached using cascade, weighted and mix methods.…”

Section: Related Workmentioning

confidence: 99%

An Architecture for Recommendation of Courses in E-learning

Dwivedi¹,

Rawat²

2017

IJITCS

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Abstract-Over the last few years, the face of traditional learning has changed significantly, due to the emergence of the web. Consequently several learning systems have emerged such as computer-based learning, web-based learning among others, meeting different kinds of educational needs of the learners and educators as well. E-learning systems allow educators, distribute information, create content material, prepare assignments, engage in discussions, and manage distance classes among others. They accumulate a huge amount of data as a result of learner's interaction with the site. This data can be used to find students' learning pattern based on which appropriate courses could be recommended to them. However existing approaches of recommending courses to learner offer the same course to all the learners irrespective of their knowledge and skill level which results in decreasing their academic performance. This paper proposes an architecture for the recommendation of courses to a learner based on his/her profile. The profile of a learner is created by applying k-means algorithm to learner's interaction data in moodle. The results show that the non active learners should not be recommended advanced courses if they have obtained poor marks and are not active in the concern course. In the initial stage we discover learners' performance in data mining course which will further be extended to other courses as well.

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Personalized recommendation of learning material using sequential pattern mining and attribute based collaborative filtering

Cited by 71 publications

References 26 publications

A Literature Review of Personalized Learning Algorithm

A Literature Review of Personalized Learning Algorithm

Personalised e-learning system using learner profile ontology and sequential pattern mining-based recommendation

An Architecture for Recommendation of Courses in E-learning

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