Collaborative Learning Group Formation Based on Personality Traits: An Empirical Study in Initial Programming Courses

2020 ◽  
pp. 73-84
Author(s):  
Oscar Revelo-Sánchez ◽  
César A. Collazos ◽  
Miguel A. Redondo
Keyword(s):  
Collaborative Learning ◽  
Empirical Study ◽  
Personality Traits ◽  
Group Formation ◽  
Learning Group ◽  
Programming Courses

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.

A Word Embeddings Based Clustering Approach for Collaborative Learning Group Formation

2021 ◽  
pp. 395-400
Author(s):  
Yongchao Wu ◽  
Jalal Nouri ◽  
Xiu Li ◽  
Rebecka Weegar ◽  
Muhammad Afzaal ◽  
...  
Keyword(s):  
Collaborative Learning ◽  
Group Formation ◽  
Word Embeddings ◽  
Learning Group ◽  
Clustering Approach

Who is the Perfect Match?

i-com ◽  
2018 ◽  
Vol 17 (1) ◽  
pp. 65-77 ◽  
Author(s):  
Henrik Bellhäuser ◽  
Johannes Konert ◽  
Adrienne Müller ◽  
René Röpke
Keyword(s):  
Personality Traits ◽  
Group Formation ◽  
Perfect Match ◽  
Learning Gain ◽  
Learning Groups ◽  
Learning Group ◽  
Time Investment ◽  
The Status ◽  
And Performance ◽  
Algorithmic Approaches

Abstract Using digital tools for teaching allows to unburden teachers from organizational load and even provides qualitative improvements that are not achieved in traditional teaching. Algorithmically supported learning group formation aims at optimizing group composition so that each learner can achieve his or her maximum learning gain and learning groups stay stable and productive. Selecting and weighting relevant criteria for learning group formation is an interdisciplinary challenge. This contribution presents the status quo of algorithmic approaches and respective criteria for learning group formation. Based on this theoretical foundation, we describe an empirical study that investigated the influence of distributing two personality traits (conscientiousness and extraversion) either homogeneously or heterogeneously on subjective and objective measures of productivity, time investment, satisfaction, and performance. Results are compared to an earlier study that also included motivation and prior knowledge as criteria. We find both personality traits to enhance group satisfaction and performance when distributed heterogeneously.

A novel metaheuristic approach for collaborative learning group formation

2018 ◽  
Vol 34 (6) ◽  
pp. 907-916 ◽  
Author(s):  
Dragan Lambić ◽  
Bojana Lazović ◽  
Aleksandar Djenić ◽  
Miroslav Marić
Keyword(s):  
Collaborative Learning ◽  
Group Formation ◽  
Learning Group

scholarly journals Collaborative Learning Group Formation with Density Clustering

2016 ◽  
Vol 9 (6) ◽  
pp. 117-126
Author(s):  
Mengxiong Zhou ◽  
Yanming Ye ◽  
Yizhi Ren ◽  
Yueshen Xu
Keyword(s):  
Collaborative Learning ◽  
Group Formation ◽  
Learning Group ◽  
Density Clustering

A “Mixed” Strategy for Collaborative Group Formation and Its Learning Outcomes

2018 ◽  
Vol 46 (4) ◽  
pp. 440-462
Author(s):  
Anal Acharya ◽  
Devadatta Sinha
Keyword(s):  
Collaborative Learning ◽  
Learning Styles ◽  
Group Formation ◽  
Learning System ◽  
Regression Coefficients ◽  
Collaborative Group ◽  
Subject Knowledge ◽  
Personal Learning ◽  
Learning Group ◽  
Prototype Learning

This study uses homogeneity in personal learning styles and heterogeneity in subject knowledge for collaborative learning group decomposition indicating that groups are “mixed” in nature. Homogeneity within groups was formed using K-means clustering and greedy search, whereas heterogeneity imbibed using agenda-driven search. For checking learning effectiveness, a simple schema of collaborative learning was proposed and prototype learning system developed using Android Emulator. Multiple regression analysis was applied on their learning results to derive regression coefficients for determining learning efficiency. The derived set of regression coefficients suggests more the time taken to form groups, better the student learning quality.

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.

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