Relationship Between Context-Social and Academic Performance: First Notes

  • Ortega C. JuanEmail author
  • Gómez A. HéctorEmail author
  • Villavicencio Alvarez Victor EmilioEmail author
  • Lozada T. Edwin FabricioEmail author
  • Francisco R. Naranjo CEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1027)


Context: Student performance based on interaction with virtual learning environments and traditional classroom.

Problem: In the academy it is not clear what variables can influence the academic score, since there are different conclusions according to the context in analysis.

Objective: To find academic and social variables that influence academic performance in virtual environments of learning and traditional classroom.

Methodological Proposal: Apply data mining and correlation of variables as a first step to the identification of variables that influence academic score.

Experiment: We worked with two datasets according to the context under study. The results of the first experiment showed 83% of effectiveness that the level of education and the number of previous credits of the student directly influence their performance, while the interaction with the virtual learning environment does not directly influence the score. The high ratings in this data set is difficult to classify. In the second experiment, we worked on a traditional classroom. The results showed that academic performance is not linked to alcohol consumption at the end or midweek. Free days have no relation to performance. For the case of gender it seems to be better that women have a university preparation to achieve a better score. In relation to health, women are more affected with absences. Finally, if there is a relationship between internet access and performance. The results of this work are not conclusive, they are only the first notes to determine and corroborate the influence of academic and social variables in the academic score.


Score Variables Influence 


  1. 1.
    Bartolj, T., Polanec, S.: Does work harm academic performance of students? Evidence using propensity score matching. Res. High. Educ. 59, 401–429 (2018). Scholar
  2. 2.
    Lelli, R., García, R., Charczuk, N., et al.: Identificación de causales de deserción y desgranamiento de los estudiantes de la licenciatura en sistemas utilizando ingeniería de explotación de información. In: XVII Workshop de Investigadores en Ciencias de la Computación, Salta (2015)Google Scholar
  3. 3.
    García, J.C., Manuel, G., Zafrillo, A.I.: Desgranamiento university: student perspective in engineering, comunicaicón. In: International Colloquium on Gestao University na America do Sul, Florianopolis (2011)Google Scholar
  4. 4.
    Muller, C.: Parent involvement and academic achievement: an analysis of family resources available to the child. In: Parents, Their Children, And Schools. Taylor & Francis, New York, pp. 77–114 (2018)Google Scholar
  5. 5.
    Nair, R., Roche, K., White, R.: Acculturation gap distress among latino youth: prospective links to family processes and youth depressive symptoms, alcohol use, and academic performance. J. Youth Adolesc. 47(1), 105–120 (2017)CrossRefGoogle Scholar
  6. 6.
    Zuzanek, J., Hilbrecht, M.: Do parents matter? Teens’ time use, academic performance and well-being. [En línea], 15 Mayo 2018. Acceso 10 Noviembre 2018
  7. 7.
    Brewer, N., Thomas, K., Higdon, J.: Intimate partner violence, health, sexuality, and academic performance among a national sample of undergraduates. J. Am. Coll. Health 66(7), 683–692 (2018)CrossRefGoogle Scholar
  8. 8.
    Gomez, H.F.A., et al.: Emotional strategy in the classroom based on the application of new technologies: an initial contribution. In: Satapathy, S.C., Joshi, A. (eds.) Information and Communication Technology for Intelligent Systems. SIST, vol. 106, pp. 251–261. Springer, Singapore (2019). Scholar
  9. 9.
    Fong, W., Villa, A., Curiel, R.: Intrinsic motivation and its association with cognitive, actitudinal and previous knowledge processes in engineering student. Contemp. Eng. Sci. 11(3), 129–138 (2018)CrossRefGoogle Scholar
  10. 10.
    Gómez, H., Arias, S., Torres, P., et al.: Emotions analysis techniques: their application in the identification of criteria for selecting suitable Open Educational Resources (OERs). In: 2015 International Conference on Interactive Collaborative and Blended Learning (ICBL), Mexico (2015)Google Scholar
  11. 11.
    Torres, C., Duart, J., Goméz, H., et al.: Internet use and academic success in university students. Comunicar 14(48), 61–70 (2016). Revista Cientifica de Comunicación y EducaciónCrossRefGoogle Scholar
  12. 12.
    Gomez A, H.F., Arias T, S.A., Martinez, C.E., Martínez V, M.A., Sanchez, N.B., Sanchez-Cevallos, E.: Categorization of types of internautes based on their navigation preferences within educational environments. In: Rocha, Á., Serrhini, M. (eds.) EMENA-ISTL 2018. SIST, vol. 111, pp. 1–9. Springer, Cham (2019). Scholar
  13. 13.
    Iyanda, A., Ninan, O., et al.: Predicting student academic performance in computer science courses: a comparition of neural network models. Int. J. Mod. Educ. Comput. Sci. 6, 1–9 (2018)CrossRefGoogle Scholar
  14. 14.
    Marsh, H., Pekrun, R., Murayma, K., et al.: An integrated model of academic selfconcept development: academic selfconcept, grades, test scores, and tracking over six years. Dev. Psychol. 54(2), 263–280 (2018)CrossRefGoogle Scholar
  15. 15.
    Chen, C., Liu, K., Ma, K.: Research on evaluation of college students’ professional ability based on k-means clustering. In: SMIMA (2018)Google Scholar
  16. 16.
    Torres, J.C., Gomez, H., Arias, S.: Social learning environments. In: 2015 International Conference on Interactive Collaborative Learning, Firence (2015)Google Scholar
  17. 17.
    Kuzilek, J., Hlosta, M., Zdrahal, Z.: Open university learning analytics dataset. Knowledge Media Institute, The Open University (2017). Acceso diciembre 2018CrossRefGoogle Scholar
  18. 18.
    Cortez, P.: Student performance data set, April 2008. Acceso diciembre 2018
  19. 19.
    Gomez, H., Arias, S., Martinez, E., et al.: A methodology for identifying attributes of academic excellence based on a 20/80 Pareto distribution. In: 2016 IEEE Global Engineering Education Conference (EDUCON), Abu Dhabi (2016)Google Scholar
  20. 20.
    Cecilia, C., Pomerantz, E.: Does adolescents’ disclosure to their parents matter for their academic adjustment? Child Dev. 84(2), 693–710 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Subdirección de PosgradosUniversidad Católica de CuencaCuencaEcuador
  2. 2.Universidad Tecnica de AmbatoAmbatoEcuador
  3. 3.Departamento de Ciencias Humanas y Sociales, ESPESangolquiEcuador
  4. 4.Carrera de SoftwareUniversidad Autónoma de los Andes-UNIANDESAmbatoEcuador
  5. 5.Facultad de Ingeniería en Ciencias AplicadasUniversidad Técnica del NorteIbarraEcuador

Personalised recommendations