Homogeneous Group Formation in Collaborative Learning Scenarios: An Approach Based on Personality Traits and Genetic Algorithms

2021 ◽  
pp. 1-1
Author(s):  
Oscar Revelosanchez ◽  
Cesar A. Collazos ◽  
Miguel A. Redondo ◽  
Ig Ibert Bittencourt
Keyword(s):  
Genetic Algorithms ◽  
Collaborative Learning ◽  
Personality Traits ◽  
Group Formation ◽  
Homogeneous Group ◽  
Learning Scenarios

scholarly journals A Strategy Based on Genetic Algorithms for Forming Optimal Collaborative Learning Groups: An Empirical Study

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 463
Author(s):  
Oscar Revelo Sánchez ◽  
César A. Collazos ◽  
Miguel A. Redondo
Keyword(s):  
Collaborative Learning ◽  
Personality Traits ◽  
Group Formation ◽  
Point Of View ◽  
Collaborative Activity ◽  
Computational Point ◽  
Learning Groups ◽  
Learning Scenarios ◽  
Genetic Algorithm Approach ◽  
Heterogeneous Formation

Considering that group formation is key when developing activities in collaborative learning scenarios, this paper aims to propose a strategy based on a genetic algorithm approach for achieving optimal collaborative learning groups, considering the students’ personality traits as grouping criteria. A controlled experiment was designed with 238 students, quantifying their personality traits through the “big five inventory” (BFI), forming working groups and developing a collaborative activity in programming and related courses. The experiment results allowed validation, not only from a computational point of view evaluating the algorithm performance but also from a pedagogical point of view, confronting the results obtained by students applying the proposed approach with those obtained through other group formation strategies. The highlight of the study is that those groups whose formation was pre-established by the teachers through the proposed strategy have generally had a better collaborative performance than the groups with traditional formation, except in the case of heterogeneous formation, at the time of developing a collaborative activity. In addition, through the experiment, it was found that not considering criteria related to personality traits before the group formation generally led to lower results.

scholarly journals The Use of Personality Traits to Enhance Theory-driven Group Formation

2020 ◽  
Vol 28 ◽  
pp. 796-818
Author(s):  
Rachel Carlos Duque Reis ◽  
Kamila Takayama Lyra ◽  
Clausius Duque Gonçalves Reis ◽  
Bruno Elias Penteado ◽  
Seiji Isotani
Keyword(s):  
Collaborative Learning ◽  
Personality Traits ◽  
Group Work ◽  
Current Knowledge ◽  
Group Structure ◽  
Group Formation ◽  
Learning Theories ◽  
Average Percentage ◽  
Context Learning ◽  
New Learning

Group formation is an important and challenging element for designing successful CSCL scenarios. Despite efforts from the scientific community in developing more effective algorithms to support group formation processes, we still face problems related to learners’ resistance and demotivation towards group work. In this sense, diverse studies highlight the importance of considering learners’ personality traits to form groups, since this factor can influence students’ performance and induce diverse actions and behaviors in group work. Therefore, this paper presents G-FusionPT (Group Formation USIng Ontology and Personality Trait), a group formation algorithm that support new learning roles, denominated Affective Collaborative Learning roles, based on relation between collaborative learning theories and students’ personality traits. The algorithm is based on a collaborative ontology to understand the learning theories (e.g., context, learning activities, group structure), and learners profile to understand learners’ needs (e.g., target/current knowledge/skill). To evaluate the algorithm, we used a 300 student simulated sample wit varying group size (three, five, and seven members), and compared G-FusionPT results to other group formation algorithms: G-Fusion (based specifically on collaborative learning theories) and Random (no strategy or criterion). The results demonstrated the effectiveness of G-FusionPT against G-Fusion and Random algorithms, as it generated the highest average percentage of learners in well-formed groups and lowest averagepercentage of learners in unfit groups.

scholarly journals Cooperative Learning: Adaptive Group Formation for Collaborative Learning

2020 ◽  
Vol 9 (3) ◽  
pp. 752-754
Keyword(s):  
Collaborative Learning ◽  
Cooperative Learning ◽  
Learning Styles ◽  
Social Networking ◽  
Personality Traits ◽  
Clustering Algorithm ◽  
Group Formation ◽  
Social Networking Site ◽  
Interaction Patterns ◽  
Grouping Students

Collaborative learning affects with lot of factors like student’s personality, their interaction patterns, learning styles etc. Grouping of students is one of the important factors. It is important to arrange groups by skills and/or backgrounds. Hence it is noteworthy to create groups based on specific skills of students. Generally the students can be randomly grouped or grouped themselves. But this method of grouping students based on certain features like personality traits can improve the efficiency of collaborative learning. The student’s data can be collected from social networking site like Facebook. The personality of each student can be identified by comparing the individual’s chat history with psycholinguistic databases. The main objectives of this paper are to identify the student’s personality. Based on that, the group of students can be formed using k-means clustering algorithm.

scholarly journals Learning log-based automatic group formation: system design and classroom implementation study

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Changhao Liang ◽  
Rwitajit Majumdar ◽  
Hiroaki Ogata
Keyword(s):  
Collaborative Learning ◽  
Group Work ◽  
Intelligent System ◽  
Group Formation ◽  
Modern Society ◽  
Computer Supported Collaborative Learning ◽  
Classroom Implementation ◽  
Actual Learning ◽  
Group Member ◽  
Learning Log

AbstractCollaborative learning in the form of group work is becoming increasingly significant in education since interpersonal skills count in modern society. However, teachers often get overwhelmed by the logistics involved in conducting any group work. Valid support for executing and managing such activities in a timely and informed manner becomes imperative. This research introduces an intelligent system focusing on group formation which consists of a parameter setting module and the group member visualization panel where the results of the created group are shown to the user and can be graded. The system supports teachers by applying algorithms to actual learning log data thereby simplifying the group formation process and saving time for them. A pilot study in a primary school mathematics class proved to have a positive effect on students’ engagement and affections while participating in group activities based on the system-generated groups, thus providing empirical evidence to the practice of Computer-Supported Collaborative Learning (CSCL) systems.

Protocolo do experimento - CPBL/MA/FGAD (Projeto: Elaborar seminário de Biodiversidade) v2

2021 ◽  
Author(s):  
Geiser Chalco Challco
Keyword(s):  
Experimental Study ◽  
Collaborative Learning ◽  
High Performance ◽  
Group Formation ◽  
Agile Methods ◽  
Quasi Experimental ◽  
Learning Projects

Main protocol in portugues used to conduct a quasi-experimental study of group formation of high performance in Collaborative-Learning-Projects with Agile Methods

Scripts for Facilitating Computer Supported Collaborative Learning

2011 ◽  
pp. 745-750
Author(s):  
Bernhard Ertl ◽  
Heinz Mandl
Keyword(s):  
Distance Learning ◽  
Collaborative Learning ◽  
Learning Strategies ◽  
Text Comprehension ◽  
Comprehension Strategies ◽  
Computer Supported Collaborative Learning ◽  
Collaboration Scripts ◽  
Collaboration Skills ◽  
Learning Scenarios ◽  
Collaboration Process

Many distance learning scenarios, for example, virtual seminars, use collaborative arrangements for learning. By applying them, they offer learners the chance to construct knowledge collaboratively. However, learners often do not possess the skills necessary for a beneficial collaboration. It is therefore important that learners are offered support in these learning scenarios. Scripts for collaborative learning can provide support. They can guide learners through their collaboration process (Ertl, Kopp, & Mandl, 2007b) and help them to acquire collaboration skills (Rummel & Spada, 2005). Scripts for collaboration were originally developed in order to support text comprehension. They facilitate two or more learners—who are similar as far as their existing knowledge and learning strategies are concerned— in their efforts to understand contents provided by theory texts. Collaboration scripts split this process into a sequence of smaller steps, assign each learner to a particular role, and offer a number of comprehension strategies, such as questions, feedback, and elaboration. Each one of these learners has a defined role to play, which in turn is associated with certain strategies and varies within the different phases.

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