Reputation Metadata for Recommending Personalized e-Learning Resources

Sánchez-Nielsen, Elena; Chávez-Gutiérrez, Francisco

doi:10.1109/smap.2007.32

Cited by 23 publications

(15 citation statements)

References 6 publications

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“…Recent trends show that most of the researchers use data mining approaches and information retrieval techniques as their recommendation strategies (Kerkiri, Manitsaris & Mavridou, 2007;Liang et al, 2006;Zaiane, 2002). Zaiane (2002) proposed the use of a web mining technique to build agents that could recommend online learning activities or shortcuts in a course website, based on learners' access histories, to improve course navigation as well as assist with the online learning process.…”

Section: Previous Researchmentioning

confidence: 99%

“…They use a clustering technique to cluster learners into a subclass according to the learning interest before using collaborative filtering to calculate learners' similarities for content recommendation. Kerkiri et al (2007) proposed a framework that exploits both description and reputation metadata to recommend personalised learning resources. Their experiment proved that the use of reputation metadata augmented learner's satisfaction by retrieving those learning materials which were evaluated positively.…”

Section: Previous Researchmentioning

confidence: 99%

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Measuring learner's performance in e-learning recommender systems

Ghauth

Abdullah

2010

AJET

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A recommender system is a piece of software that helps users to identify the most interesting and relevant learning items from a large number of items. Recommender systems may be based on collaborative filtering (by user ratings), content-based filtering (by keywords), and hybrid filtering (by both collaborative and content-based filtering). Recommender systems have been a useful tool to recommend items in many online systems, including e-learning. However, not much research has been done to measure the learning outcomes of the learners when they use e-learning with a recommender system. Instead, most of the researchers were focusing on the accuracy of the recommender system in predicting the recommendation rather than the knowledge gain by the learners. This research aims to compare the learning outcomes of the learners when they use several types of e-learning recommender systems. Based on the comparison made, we propose a new e-learning recommender system framework that uses content-based filtering and good learners' ratings to recommend learning materials, and in turn is able to increase the student's performance. The results show that students who used the proposed e-learning recommender system produced a significantly better result in the post-test. The results also show that the proposed e-learning recommender system has the highest percentage of score gain from pre-test to post-test.

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Section: Previous Researchmentioning

confidence: 99%

Section: Previous Researchmentioning

confidence: 99%

Measuring learner's performance in e-learning recommender systems

Ghauth

Abdullah

2010

AJET

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show abstract

“…Then a data mining technique was used to draw the rules of the best learning path for each group of learners. Kerkiri et al [24] proposed a framework that uses reputation metadata in a recommender system. The reputation metadata was the ratings of learning resources provided by the learners.…”

Section: Related Workmentioning

confidence: 99%

PLORS: a personalized learning object recommender system

Imran

Belghis-Zadeh

Chang

et al. 2015

Vietnam J Comput Sci

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Learning management systems (LMS) are typically used by large educational institutions and focus on supporting instructors in managing and administrating online courses. However, such LMS typically use a "one size fits all" approach without considering individual learner's profile. A learner's profile can, for example, consists of his/her learning styles, goals, prior knowledge, abilities, and interests. Generally, LMSs do not cater individual learners' needs based on their profile. However, considering learners' profiles can help in enhancing the learning experiences and performance of learners within the course. To support personalization in LMS, recommender systems can be used to recommend appropriate learning objects to learners to increase their learning. In this paper, we introduce the personalized learning object recommender system. The proposed system supports learners by providing them recommendations about which learning objects within the course are more useful for them, considering the learning object they are visiting as well as the learning objects visited by other learners with similar profiles. This kind of personalization can help in improving the overall quality of learning by providing recommenda-B Hazra Imran tions of learning objects that are useful but were overlooked or intentionally skipped by learners. Such recommendations can increase learners' performance and satisfaction during the course.

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“…Then a data mining technique was used to elicit the rules of the best learning path for each group of learners. Kerkiri, Manitsaris and Mavridou [16] proposed a framework that uses reputation metadata in a recommender system. Reputation is the cumulative scale of user opinions regarding persons, products, and ideas.…”

Section: Related Workmentioning

confidence: 99%

A Framework to Provide Personalization in Learning Management Systems through a Recommender System Approach

Imran

Quảng

Chang

et al. 2014

Intelligent Information and Database Systems

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Abstract. Personalization in learning management systems (LMS) occurs when such systems tailor the learning experience of learners such that it fits to their profiles, which helps in increasing their performance within the course and the quality of learning. A learner's profile can, for example, consist of his/her learning styles, goals, existing knowledge, ability and interests. Generally, traditional LMSs do not take into account the learners' profile and present the course content in a static way to every learner. To support personalization in LMS, recommender systems can be used to recommend appropriate learning objects to learners, not only based on their individual profile but also based on what worked well for learners with a similar profile. In this paper, we propose a framework to integrate a recommender system approach into LMS. The proposed framework is designed with the goal of presenting a flexible integration model which can provide personalization by automatically suggesting learning objects to learners based on their current situation as well as successful learning experiences of learners with similar profiles in a similar situation. Such advanced personalization can help learners in many ways such as reducing the learning time without negative impact on their marks, improving learning performance as well as increasing the level of satisfaction.

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Reputation Metadata for Recommending Personalized e-Learning Resources

Cited by 23 publications

References 6 publications

Measuring learner's performance in e-learning recommender systems

Measuring learner's performance in e-learning recommender systems

PLORS: a personalized learning object recommender system

A Framework to Provide Personalization in Learning Management Systems through a Recommender System Approach

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