A Fuzzy-Based Multi-agent Model for Group Formation in Collaborative Learning Environments

Torres, Sebastián; Salazar, Oscar M.; Ovalle, Demetrio Arturo

doi:10.1007/978-3-319-60819-8_1

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…The authors formed groups of students in order to achieve both inter-homogeneous and intra-heterogeneous groups in terms of student characteristics, and they also confirmed that group formation provides a positive effect on the development of the activities within the collaborative learning environment. Similarly, Torres et al introduced a fuzzy-based approach for group formation to balance different working groups [16]. Another direction is based on social group formation.…”

Section: Related Workmentioning

confidence: 99%

Geolocation-based Group Formation and its Mobile Application Prototype

Sukstrienwong¹

2022

TEM Journal

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Due to the rapid growth of mobile and wireless technologies, mobile collaborative activities have become important in group learning. However, the problem of establishing optimal groups of members located in various locations occurs if distances between each member and the group itself are concerned. Furthermore, the challenge becomes increasingly complex and time consuming as the number of people required for location-based group formation rises. As a result, the main contribution of this paper is to propose a greedy approach for generating optimal groups of members with the shortest traveling distances between members. We also want to demonstrate that the algorithm can be utilized on mobile devices. A prototype of a mobile -based graphical user interface is presented. In addition, a questionnaire is conducted in order to obtain user feedback that will help us shape the development of the future mobile application in an appropriate manner.

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Section: Related Workmentioning

confidence: 99%

Geolocation-based Group Formation and its Mobile Application Prototype

Sukstrienwong¹

2022

TEM Journal

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“…Torres et al [32] propose a fuzzy-based multi-agent model for group formation based on nine roles defined by Belbin typology, using the strengths and ideal responsibilities for each group member role. To better balance the different working groups based on existing roles, they employ a fuzzy logic approach that allows classifying the role performance of each individual into the group.…”

Section: Related Workmentioning

confidence: 99%

Group formation in collaborative learning contexts based on personality traits: An empirical study in initial Programming courses

Sánchez¹,

Ordóñez²,

Duque³

2021

IxD&A

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Considering that group formation is one of the key processes when developing activities in collaborative learning contexts, this paper aims to propose a technique based on an approach of genetic algorithms to achieve homogeneous groups, considering the students' personality traits as grouping criteria. For its validation, an experiment was designed with 132 first semesters engineering students, quantifying their personality traits through the “Big Five Inventory”, forming workgroups and developing a collaborative activity in initial Programming courses. The experiment made it possible to compare the results obtained by the students applying the proposed approach to those obtained through other group formation strategies. It was demonstrated through the experiment that the homogeneous groups generated by the proposed technique produce better academic results compared to the grouping technique by students’ preference, traditionally used by the teachers when developing a collaborative activity.

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“…Many applications of fuzzy logic that have been performed around the world have been proven effective in solving various types of problems in which the available knowledge is imperfect. Multi-agent technology with its features is the best way to model and build complex distributed systems [4,5]. The combination of these two technologies should therefore open up a new avenue of research for the design and realization of modern systems that are often complex systems, composed with several entities interacting in distributed mode, and furthermore they are characterized by imprecision and uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Multi Agent System for Automatic Classification and Negotiation of QOS in Cloud Computing

Bakraouy¹,

Abbass²,

Baïna³

et al. 2019

JAMRIS

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The use of Multi Agents Systems (MAS), Cloud Computing (CC) and Fuzzy Inference System (FIS) in e-commerce has increased in recent years. The purpose of these systems is to enable users of electronic markets to make transactions in the best conditions and to help them in their decisions. The design and implementation is often characterized by the constant manipulation of information, many of which are imperfect. The use of the multi-agent paradigm for the realization of these systems implies the need to integrate mechanisms that take into account the processing of fuzzy information. This makes it necessary to design multi-agent systems (MAS) with fuzzy characteristics. For the modeling and realization of this system, we chose to use the FMAS model. This paper deals with the presentation of the use of the Fuzzy MAS model for the development of a management and decision support application in a virtual market with high availability. After the presentation of the system to be realized in the first section, we describe in the second section the application of the model FMAS for the design and the realization of this system. We then specify the JADE implementation platform and how the fuzzy agents of our model (Expert, Choice and Query) can be implemented using this platform

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A Fuzzy-Based Multi-agent Model for Group Formation in Collaborative Learning Environments

Cited by 5 publications

References 9 publications

Geolocation-based Group Formation and its Mobile Application Prototype

Geolocation-based Group Formation and its Mobile Application Prototype

Group formation in collaborative learning contexts based on personality traits: An empirical study in initial Programming courses

Fuzzy Multi Agent System for Automatic Classification and Negotiation of QOS in Cloud Computing

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