A Student-Centered Hybrid Recommender System to Provide Relevant Learning Objects from Repositories

Rodríguez, Paula; Ovalle, Demetrio Arturo; Duque, Néstor D.

doi:10.1007/978-3-319-20609-7_28

Cited by 19 publications

(21 citation statements)

References 7 publications

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“…In [7], an Educational Recommender System, is presented. The aim of this article is to present a student-centered Learning Object (LO) recommender system based on a hybrid recommendation technique that combines the three following approaches: content-based, collaborative and knowledge-based.…”

Section: Theoretical Aspects and Related Workmentioning

confidence: 99%

A Learning Object Recommendation Model with User Mood Characteristics

Méndez

Zapata

Collázos

2016

Social Computing in Digital Education

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Abstract. Emotions influence human cognition, affecting perception and understanding of a specific situation; therefore emotions can affect positively or negatively the learning process. Currently there are few information systems that analyze users' emotions to optimize their learning. This article proposes a model that includes users' temporary emotions to recommend Learning Objects (LO) and deliver relevant educational materials. Three stages are set; initially, the model recognizes a user's emotions and learning style; then a recommendation system is applied to identify relevant LOs; and finally, the presentation of this information is showed to the user. In this work we present a method that identifies the emotion of the user based on facial recognition, and the process of recommendation is presented.

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Section: Theoretical Aspects and Related Workmentioning

confidence: 99%

A Learning Object Recommendation Model with User Mood Characteristics

Méndez

Zapata

Collázos

2016

Social Computing in Digital Education

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“…In previous work, authors have proposed a Student-Centered Hybrid ERS, designing and evaluating several recommendation techniques for delivering the most appropriate LO for each specific student [7,8]. Also, they have combined these techniques by using hybridization methods, improving the performance of isolated techniques.…”

Section: Motivationmentioning

confidence: 99%

“…Therefore, a LO delivered by our ERS can be considered as 'relevant' if it matches the student's learning objectives (determined by the keywords) and profile (his/her learning style, format, language, and learning time preferences). For this example, the traditional hybridization method that our ERS used to date [8] will select and provide the LO269 to Jose, since it has been recommended by two out of the three recommendation modules.…”

Section: Validationmentioning

confidence: 99%

“…To make the hybridization of recommendation techniques -using at least two of them-Burke [6] describes different methods that could be applied (e.g. weighted, switching, mixed, cascade, feature combination, feature augmentation, and meta-level).In previous work, authors have proposed a Student-Centered Hybrid ERS, designing and evaluating several recommendation techniques for delivering the most appropriate LO for each specific student [7,8]. Also, they have combined these techniques by using hybridization methods, improving the performance of isolated techniques.…”

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confidence: 99%

“…Then, the integration module performs the hybridization process to provide the student with the most relevant and appropriate LOs. This is done by selecting LOs that have been proposed by 2 4 Several hybridization methods, as proposed in [6], were tested, and the cascade approach achieved the best recommendation results [8]. or 3 of the recommendation modules.…”

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confidence: 99%

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Argumentation-Based Hybrid Recommender System for Recommending Learning Objects

Rodríguez

Heras

Palanca

et al. 2016

Multi-Agent Systems and Agreement Technologies

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Abstract. Recommender Systems aim to provide users with search results close to their needs, making predictions of their preferences. In virtual learning environments, Educational Recommender Systems deliver learning objects according to the student's characteristics, preferences and learning needs. A learning object is an educational content unit, which once found and retrieved may assist students in their learning process. In previous work, authors have designed and evaluated several recommendation techniques for delivering the most appropriate learning object for each specific student. Also, they have combined these techniques by using hybridization methods, improving the performance of isolated techniques. However, traditional hybidization methods fail when the learning objects delivered by each recommendation technique are very different from those selected by the other techniques (there is no agreement about the best learning object to recommend). In this paper, we present a hybrid recommendation method based on argumentation theory that combines content-based, collaborative and knowledge-based recommendation techniques and provides the students with those objects for which the system is able to generate more arguments to justify their suitability. This method has been tested by using a database with real data about students and learning objects, getting promising results. MotivationAccording to the IEEE, a learning object (LO) can be defined as a digital entity involving educational design characteristics. Each LO can be used, reused or referenced during computer-supported learning processes, aiming at generating knowledge and competences based on student's needs [1]. LOs have functional requirements such as accessibility, reuse, and interoperability. The concept of LO requires understanding of how people learn, since this issue directly affects the LO design in each of its three dimensions: pedagogical, didactic, and technological [2]. In addition, LOs have metadata that describe and identify the educational resources involved and facilitate their searching and retrieval. Learning ObjectsCorresponding author.Repositories (LORs), composed of thousands of LOs, can be defined as specialized digital libraries storing several types of heterogeneous resources. LORs are currently being used in various e-learning environments and belong mainly to educational institutions [2,3]. Also, federations of LORs provide educational applications to search, retrieve and access specific LO contents available in any LOR [4]. Recommender Systems aim to provide users with search results close to their needs, making predictions of their preferences [5]. In virtual learning environments, Educational Recommender Systems (ERS) deliver LOs according to the student's characteristics, preferences and learning needs. In order to improve recommendations, ERS must perform feedback processes and implement mechanisms that enable them to obtain a large amount of information about users and how they use the LOs. ERS can be classified into s...

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Personal Generative Libraries for Personalised Learning: A Case Study

Štuikys,

Burbaitė

2024

Evolution of STEM-Driven Computer Science Education

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A Student-Centered Hybrid Recommender System to Provide Relevant Learning Objects from Repositories

Cited by 19 publications

References 7 publications

A Learning Object Recommendation Model with User Mood Characteristics

A Learning Object Recommendation Model with User Mood Characteristics

Argumentation-Based Hybrid Recommender System for Recommending Learning Objects

Personal Generative Libraries for Personalised Learning: A Case Study

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