Estrutura Ontológica para representar Papéis Colaborativos Afetivos em ambientes CSCL

Reis, Rachel Carlos Duque; Rodríguez, Casiano; Lyra, Kamila Takayama; Isotani, Seiji

doi:10.5753/rbie.2017.25.02.87

Cited by 5 publications

(11 citation statements)

References 16 publications

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“…The Group Recommender system used a KNN (K-Nearest Neighbors) data mining algorithm to suggest learners with similar personality traits. Reis et al (2018) extended the previous review and found three other studies Iima, 2008, 2009;Farhangian et al 2015) to support group formation based on prediction models. Iima (2008, 2009) observed the use of neural networks and genetic algorithms for the prediction of learning results in group work, based on their personality trait.…”

Section: Related Worksupporting

confidence: 69%

“…Other examples of ACL roles can be found in the study developed by Reis et al (2016Reis et al ( , 2017. In Reis et al (2016) the ACL roles were based on a combination between introvert/extrovert personality traits and Anchored instructor/Problem holder CL roles, based on Anchored Instructor CL theory (CTGV, 1992).…”

Section: Affective Collaborative Learning Rolementioning

confidence: 99%

“…As shown by Figure 2(c), such conditions are based on learner' personality trait, his/her current knowledge (nothing, accretion, tuning, and restructuring), and his/her current skill (nothing, rough-cognitive stage, explanatory-cognitive stage, associative stage, and autonomous stage). Examples of ACL roles are Rigid/Non_rigid peer tutor and Rigid/Non_rigid peer tutee (Reis, 2019, p. 213), Extroverted/Introverted anchored instructor and Extroverted/Introverted problem holder (Reis et al, 2015b(Reis et al, , 2016, Stable/Unstable extroverted (Reis et al, 2017).…”

Section: Ontologymentioning

confidence: 99%

“…The results showed that the quality of groups formed by the algorithm was better than groups created using the random algorithm. Reis et al (2015Reis et al ( , 2015aReis et al ( , 2018 and Amara et al (2016) also developed literature reviews for the CSCL context. Reis et al (2015) developed a systematic mapping to investigate the use of affective states, such as emotion and mood, and personality trait in CSCL environment.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, heterogeneous groups, in terms of social and affective characteristics, tend to spend more time in the resolution of socio-emotional conflicts than in solving the problem itself (Jaques & Nunes, 2012). Thus, CL researchers highlight the importance of considering learners' personality traits to form groups, since such factor may influence students' performance and induce diverse actions and behaviors in group work (Bradley & Herbert, 1997;Caropreso & Chen, 2003;Reis et al, 2016Reis et al, , 2018Altanopoulou & Tselios, 2015).…”

Section: Introductionmentioning

confidence: 99%

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

Reis¹,

Lyra²,

Reis³

et al. 2020

RBIE

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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.

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Section: Related Worksupporting

confidence: 69%

Section: Affective Collaborative Learning Rolementioning

confidence: 99%

Section: Ontologymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Reis¹,

Lyra²,

Reis³

et al. 2020

RBIE

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Satisfacción del profesorado y alumnado con el empleo de Metodologías de Aprendizaje Colaborativo mediada por las TIC: Dos estudios de casos

Corporán

Martín

García³

2021

Estud. pedagóg.

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RESUMENEn este artículo se evalúa el nivel de satisfacción del profesorado y alumnado cuando trabajan con metodologías de aprendizaje colaborativo a través de las Tecnologías de la Información y la Comunicación en el proceso de enseñanza-aprendizaje. Para tal fin se analizaron varios estudios de casos en donde se exploraron las fortalezas y debilidades con que se encuentran los alumnos y profesores cuando emplean estas experiencias. El estudio se realizó en República Dominicana (nivel Secundario o Bachillerato) y se desarrolló con una muestra de 57 alumnos y 2 docentes. Se empleó el método mixto para analizar las fichas pedagógicas utilizadas por docentes y observadores y, la rúbrica cumplimentada por el alumnado. Los hallazgos ponen de manifiesto que la evaluación de las actividades en contextos de aprendizaje colaborativo con TIC sigue siendo un punto débil para el docente, quien tiene miedo de equivocarse y valorar erróneamente el trabajo de los estudiantes.

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Formação de Grupos em Ambientes CSCL baseada na combinação entre os Traços de Personalidade e Teorias de Aprendizagem Colaborativa

Reis¹,

Isotani²

2019

Anais Dos Workshops Do VIII Congresso Brasileiro De Informática Na Educação (CBIE 2019)

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Group formation in CSCL environments is considered one of the essential elements for the success of collaborative learning. Despite of important contributions, we observed the lack of studies to match personality traits to collaborative learning theories. Therefore, this paper aims at verifying the influence of personality traits on group formation based on learning theories and creating mechanisms to automate such formation. As main results, we present the creation of a Formal Model for the generation of new learning roles (PCAs) and the development of an algorithm that uses these PCAs to support the formation of more effective groups.

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Estrutura Ontológica para representar Papéis Colaborativos Afetivos em ambientes CSCL

Abstract: Resumo O uso de ontologias para representar os cenários CSCL tem sido investigado por diversos pesquisadores na literatura. Apesar das importantes contribuições desses trabalhos, observou-se a carência de estudos

Cited by 5 publications

References 16 publications

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

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

Satisfacción del profesorado y alumnado con el empleo de Metodologías de Aprendizaje Colaborativo mediada por las TIC: Dos estudios de casos

Formação de Grupos em Ambientes CSCL baseada na combinação entre os Traços de Personalidade e Teorias de Aprendizagem Colaborativa

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