Analysis of academic performance based on sociogramsA case study with students from at risk groups

  1. Sanchez, Tarquino 1
  2. Naranjo, David 1
  3. Vidal, Jack 1
  4. Salazar, Diego 1
  5. Pérez, Cristina 1
  6. Jaramillo, Marianela 1
  1. 1 Escuela Politécnica Nacional
    info

    Escuela Politécnica Nacional

    Quito, Ecuador

    ROR https://ror.org/01gb99w41

Revue:
JOTSE

ISSN: 2013-6374

Année de publication: 2021

Volumen: 11

Número: 1

Pages: 167-179

Type: Article

DOI: 10.3926/JOTSE.1110 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

D'autres publications dans: JOTSE

Objectifs de Développement Durable

Résumé

The present work analyzes the academic performance of students from at-risk groups from the perspective of Social Network Analysis (SNA), studying the academic and interaction information of 45 students belonging to at-risk groups who attended a pilot socio-academic course during one academic term. This information was used to create a sociogram, which served as the basis for determining the centrality metrics of the SNA. The relationships between these metrics and the academic variables were then studied by means of correlation analysis and linear regression with LASSO standardization. As a preview of the results, it was determined that the academic performance of the students in the pilot course was influenced, on the one hand, by their academic knowledge prior to being admitted to the university, represented by the score on the Mathematics and Geometry section of the diagnostic test, and on the other hand, by the dynamics of the social network in which they interacted in the classroom, represented by the eigenvector centrality. These results have significant potential for explaining the academic performance according to SNA metrics, and they provide evidence to support the implementation of practices that promote a healthy social environment in an academic context.

Références bibliographiques

  • Abbasi, A., Altmann, J., & Hossain, L. (2011). Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of Informetrics, 5(4), 594-607. https://doi.org/10.1016/j.joi.2011.05.007
  • Amo, C., & Santelices, M.V. (2017). Trayectorias universitarias: más que persistencia o deserción. Congresos CLABES. http://revistas.utp.ac.pa/index.php/clabes/article/view/1676/2412
  • Bhardwaj, A. (2016). Importance of Education in Human Life: a Holistic Approach. International Journal of Science and Consciousness, 2(2), 23-28. www.ijsc.net
  • Caballero, C.C., Abello, R.Ll., & Palacio, J. (2007). Relación del burnout y el rendimiento académico con la satisfacción frente a los estudios en estudiantes universitarios. Avances en Psicología Latinoamericana, 25(2), 98-111. https://www.redalyc.org/articulo.oa?id=799/79925207
  • Callejas, Z., Griol, D., & Lázaro-Álvarez, N. (2020). Predicting Computer Engineering Students’ Dropout in Cuban Higher Education with Pre-Enrollment and early performance data. Journal of Technology and Science Education, 10(2), 241-258.
  • De-Marcos, L., Garciá-López, E., Garciá-Cabot, A., Medina-Merodio, J.A., Domínguez, A., Martínez-Herraíz, J.J., et al. (2016). Social network analysis of a gamified e-learning course: Small-world phenomenon and network metrics as predictors of academic performance. Computers in Human Behavior, 60, 312-321. https://doi.org/10.1016/j.chb.2016.02.052
  • García, M.I.B., Lamos-Duarte, A.F., Vargas-Rivera, O.I., Camargo-Villalba, G.E., & Capacho, N.S. (2019). Learning approaches, academic performance and related factors; in students that curve last year of the programs of the faculty of health sciences. Educacion Medica, 20, 10-17. https://doi.org/10.1016/j.edumed.2017.11.008
  • Gauraha, N. (2018). Introduction to the LASSO. Resonance, 23(4), 439-464. https://doi.org/10.1007/s12045-018-0635-x
  • Gomes Jr., L. (2019). In-class social networks and academic performance: how good connections can improve grades. Anais do XXXIV Simpósio Brasileiro de Banco de Dados (25-36). https://doi.org/10.5753/sbbd.2019.8805
  • Goodchild, S., & Bjørkestøl, K. (2020). Assessing First-Year Engineering Students’ Pre-University Mathematics Knowledge: Preliminary Validity Results. Journal of Technology and Science Education, 10(2), 259-270.
  • Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., Murray, D.J., et al. (2018). Predicting academic performance by considering student heterogeneity. Knowledge-Based Systems, 161, 134-146. https://doi.org/10.1016/j.knosys.2018.07.042
  • Ismail, A.O.A., Mahmood, A.K., & Abdelmaboud, A. (2018). Factors influencing academic performance of students in blended and traditional domains. International Journal of Emerging Technologies in Learning, 13(2), 170-187. https://doi.org/10.3991/ijet.v13i02.8031
  • Jain, T., & Langer, N. (2014). Does Who You Know Matter? Unraveling the Influence of Student Networks on Academic Performance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2425477
  • Mihaly, K. (2011). Do More Friends Mean Better Grades?: Student Popularity and Academic Achievement. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1371883
  • Navarro, R. (2003). El rendimiento académico: concepto, investigación y desarrollo. REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación, 1(2). https://revistas.uam.es/index.php/reice/article/view/5354
  • Newman, M., (2003). The Structure and Function of Complex Networks. Society for Industrial and Applied Mathematics Review, 45(2), 167-256. https://doi.org/10.1137/S003614450342480
  • Patacsil, F.F., & Tablatin, C.L.S. (2017). Exploring the importance of soft and hard skills as perceived by it internship students and industry: A gap analysis. Journal of Technology and Science Education, 7(3), 347-368. https://doi.org/10.3926/jotse.271
  • Pizarro, R. (1985). Rasgos y Actitudes del Profesor Efectivo. Tesis para optar al Grado de Magister en Ciencias de la Educación. Pontificia Universidad Católica de Chile.
  • Pulgar, J., Candia, C., & Leonardi, P.M. (2020). Social networks and academic performance in physics: Undergraduate cooperation enhances ill-structured problem elaboration and inhibits well-structured problem solving. Physical Review Physics Education Research, 16(1), 10137. https://doi.org/10.1103/physrevphyseducres.16.010137
  • Ramírez Ortiz, M.G., Caballero Hoyos, J.R., & Ramírez López, M.G. (2004). The social networks of academic performance in a student context of poverty in Mexico. Social Networks, 26(2), 175-188. https://doi.org/10.1016/j.socnet.2004.01.010
  • Ramos, V., Sánchez, T., Reina, J. & Franco-Crespo A. (2020). Differences Between Vulnerable and NonVulnerable Students Regarding the Psychological Abilities and Self-Control Skills within the Development of Learning. INTED2020 Proceedings (8388-8394). https://doi.org/10.21125/inted.2020.2282
  • Rizzuto, T.E., Ledoux, J., & Hatala, J.P. (2009). It’s not just what you know, it’s who you know: Testing a model of the relative importance of social networks to academic performance. Social Psychology of Education, 12(2), 175-189. https://doi.org/10.1007/s11218-008-9080-0
  • Sánchez, T., Gilar-Corbi, R., Castejón, J.L., Vidal, J., & León, J. (2020). Students’ Evaluation of Teaching and Their Academic Achievement in a Higher Education Institution of Ecuador. Frontiers in Psychology, 11, 1-10. https://doi.org/10.3389/fpsyg.2020.00233
  • Sandoval, I., Sánchez, T., Velasteguí, V., & Naranjo, D. (2018). Factores Asociados Al Abandono En Estudiantes De Grupos Vulnerables. Caso Escuela Politécnica Nacional. Congresos CLABES (132-141). https://revistas.utp.ac.pa/index.php/clabes/article/view/1907
  • Sandoval, I., Sánchez, T., Naranjo, D., & Jiménez, A. (2019). Proposal of a mathematics pilot program for engineering students from vulnerable groups of Escuela Politécnica Nacional. Proceedings of the LACCEI International Multi-Conference for Engineering, Education and Technology. https://doi.org/10.18687/LACCEI2019.1.1.387
  • Sandoval-Palis, I., Naranjo, D., Vidal, J. & Gilar-Corbi, R. (2020). Early Dropout Prediction Model : A Case Study of University Leveling Course Students. Sustainability, 12(22), 1-17. https://doi.org/10.3390/su12229314
  • Sawyer, R. (2013). Beyond Correlations: Usefulness of High School GPA and Test Scores in Making College Admissions Decisions. Applied Measurement in Education, 26(2), 89-112. https://doi.org/10.1080/08957347.2013.765433
  • Smith, W.C., Fraser, P., Chykina, V., Ikoma, S., Levitan, J., Liu, J., et al. (2017). Global citizenship and the importance of education in a globally integrated world. Globalisation, Societies and Education, 15(5), 648-665. https://doi.org/10.1080/14767724.2016.1222896
  • Yang, H.L., & Tang, J.H. (2003). Effects of social network on students’ performance: A web-based forum study in Taiwan. Journal of Asynchronous Learning Network, 7(3), 93-107. https://doi.org/10.24059/olj.v7i3.1848