Using Cortical Learning Algorithm to Arrange Sporadic Online Conversation Groups According to Personality Traits

García-Vélez, Roberto; López‐Nores, Martín; Blanco‐Fernández, Yolanda; Pazos‐Arias, José J.

doi:10.1007/978-3-319-39483-1_39

Cited by 1 publication

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other studies using personality traits to compose learning groups are: Yan and Chen (2013), García-Vélez et al (2016), andKonert et al (2016). Yan and Chen (2013) used a clustering algorithm, denominated "Chameleon algorithm", to create groups of up to eight students with maximal similarity of learning interest.…”

Section: Related Workmentioning

confidence: 99%

“…Such similarity is based on six factors that compose the personality, these being the learning interest, learning capability, learning style, learning activity, sex and age. García-Vélez et al (2016) proposed a solution that uses the concepts of data mining in students' personality traits and an algorithm, named "Cortical Learning Algorithm", to form sporadic groups. This algorithm uses concepts of machine learning (e.g., Bayesian network, neural network) to find the better composition of students.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Reis¹,

Lyra²,

Reis³

et al. 2020

RBIE

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%