An agent-based simulator applied to teaching-learning process to predict sociometric indices in higher education

García-Magariño, Iván ; Plaza García, Inmaculada (Universidad de Zaragoza) ; Igual Catalán, Raúl (Universidad de Zaragoza) ; Lombas, Andrés S. (Universidad de Zaragoza) ; Jamali, Hana

Resumen: Most novice teachers and even some experienced teachers can lack appropriate tools for designing teaching strategies that ensure the quality of education. The ability of working in teams is crucial in educating professionals. The literature proves that social relations influence the performance of teams. For instance, the team cohesion is directly related with its performance. In the current work, we have developed an agent-based tool for assisting teachers in simulating their teaching strategies to estimate their influence on the group sociometrics like cohesion, coherence of reciprocal relations, dissociation and density of relations. The experiments with nine scenarios in disciplines of computer science, electronic, psychology, business, tourism and renewal energies with 239 students and six teachers including experienced and novice ones show that its underlying agent-based framework can adapt to different disciplines obtaining similar outcomes to the real ones. We learned that the tool was especially reliable in predicting the density of relations and the cohesion, being the latter one probably the most relevant due to its known relation with academic performance. In addition, we also learned that it was difficult to assess the prediction quality of the dissociation in higher education, due to the usual low amounts or absence of reciprocal rejections in the students' groups in this educational stage. The presented agent-based tool is publicly distributed as open source for facilitating other researchers in following this research line.

Idioma: Inglés

DOI: 10.1109/TLT.2019.2910067

Año: 2020

Publicado en: IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 13, 2 (2020), 246-258
ISSN: 1939-1382

Factor impacto JCR: 3.72 (2020)

Categ. JCR: EDUCATION & EDUCATIONAL RESEARCH rank: 50 / 264 = 0.189 (2020) - Q1 - T1
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 45 / 112 = 0.402 (2020) - Q2 - T2

Factor impacto SCIMAGO: 1.375 - Computer Science Applications (Q1) - Engineering (miscellaneous) (Q1) - Education (Q1) - E-learning (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/T49-17R
Financiación: info:eu-repo/grantAgreement/ES/MEC/CAS17-00005
Financiación: info:eu-repo/grantAgreement/ES/MEC/OAPEE-2013-1-CZ1-GRU06-14277
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TIN2014-57028-R
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TIN2017-88327-R
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TIN2016-81766-REDT
Financiación: info:eu-repo/grantAgreement/ES/UZ/IT1-18
Financiación: info:eu-repo/grantAgreement/ES/UZ/IT24-16
Financiación: info:eu-repo/grantAgreement/ES/UZ/JIUZ-2017-TEC-03
Financiación: info:eu-repo/grantAgreement/ES/UZ/PIIDUZ-15-193

Tipo y forma: Artículo (PostPrint)

Área (Departamento): Área Tecnología Electrónica (Dpto. Ingeniería Electrón.Com.)
Área (Departamento): Área Metod.Ciencias Comportam. (Dpto. Psicología y Sociología)
Área (Departamento): Área Ingeniería Eléctrica (Dpto. Ingeniería Eléctrica)