Recommender system in collaborative learning environment using an influence diagram

Anaya, Antonio R.; Luque, Manuel; García-Saiz, Tomás

doi:10.1016/j.eswa.2013.07.030

Cited by 47 publications

(52 citation statements)

References 20 publications

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“…In a previous research (Anaya et al, 2013) we used the visual features of a decision tree (Quinlan, 1986) to explain the pedagogical implications of the proposed analysis method. In this paper we propose a tool that performs collaboration analytics and visually explains the results to students to enhance their self-reflection about collaboration and promote the students and tutor sensemaking.…”

Section: Introductionmentioning

confidence: 99%

A visual recommender tool in a collaborative learning experience

Anaya

Luque

Peinado

2016

Expert Systems with Applications

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Section: Introductionmentioning

confidence: 99%

A visual recommender tool in a collaborative learning experience

Anaya

Luque

Peinado

2016

Expert Systems with Applications

Self Cite

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“…Anaya, Luque and García-Saiz designed a recommender system that is able to warn students and tutors about potentially problematic circumstances; thus, this system can propose a recommendation that can solve the problem [13]. Students' browsing patterns and the learning material's attributes are modeled as a tree structure to improve the quality of the recommended LOs [14].…”

Section: Related Workmentioning

confidence: 99%

Learning Object Assembly Based on Learning Styles

Ramírez-Arellano

Reyes

Hernández-Simón

2016

Smart Education and E-Learning 2016

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The goal of this paper is to develop a system, referred to as the Management System for Merging Learning Objects (msMLO), which offers an approach that retrieves learning objects (LOs) based on students' learning styles and term-based queries and produces a new outcome. The first step ranks LOs using a unified learning style model and creates better LOs by merging the top-ranked LOs. The second step maps LOs onto a hierarchy of concepts to avoid including duplicated topics in the merged LO. Fifty-six students were randomly split into experimental and control groups. The experimental group browsed the LOs retrieved by the msMLO based on the students' learning styles, term-based queries and merge functionality, whereas the control group browsed the LOs retrieved based on the students' learning styles and term-based queries. The results demonstrated that the experimental group improves their learning performance, thus msMLO is a promising approach.

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“…In contrast, fuzzy ontology represents fuzzy concepts and their fuzzy relations, and meanwhile this relationship were represented by a four-tuples Θ = (C, R, P, X ), of which C is a fuzzy concept set, R is an attribute set, P is Cartesian products between fuzzy concept set and attribute set, and X is an axiomatic set. So, according to the definition of Θ , the fuzzy concept can be represented as C = ( 1 2 1 2 , ,..., r r rn n c c c ), where c i is an object, and r i is the property of c i [15]. Thus, through the fuzzy concept definition, the fuzzy ontology framework was constructed as follows.…”

Section: Fuzzy Ontology Frameworkmentioning

confidence: 99%

A Fuzzy Ontology Framework Based on User Profile

Shao¹

2017

EDU

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Abstract:Aimed at the problem of the classical ontology cannot represent the imprecision and uncertainty information, firstly, the ambiguity and uncertainty of fuzzy information was analyzed, and the fuzzy concept relationship was expressed by using fuzzy membership function. And then, the user interest estimation based on behavior was studied in term of user's learning preferences, and user profile was described by the learning object, Furthermore, fuzzy ontology under different granularity was built, the fuzzy concept lattice was clustered, and the concept similarity of fuzzy formal concepts was calculated. Finally, a fuzzy ontology framework based on user profile was proposed. As verified by experiment, the results have shown that the framework can reduce efficiently the uncertainty information of fuzzy ontology, and enhance the precision of ontology.

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Recommender system in collaborative learning environment using an influence diagram

Cited by 47 publications

References 20 publications

A visual recommender tool in a collaborative learning experience

A visual recommender tool in a collaborative learning experience

Learning Object Assembly Based on Learning Styles

A Fuzzy Ontology Framework Based on User Profile

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