Effects of Teaching Methodology on the Students’ Academic Performance in an Introductory Course of Programming

  • Patricia Compañ-RosiqueEmail author
  • Rafael Molina-CarmonaEmail author
  • Rosana Satorre-CuerdaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11590)


The work of a teacher is dynamic. Year after year it is necessary to adjust the contents and the methodology to the features of the students and the changes in the profession. The authors of this paper are aware of these needs and have been adapting over time a basic programming subject of the degree in Computer Engineering. The objective of this work is to analyse how the different teaching methodologies used in an introductory course to programming during several academic years affect the students’ performance. For this purpose, the students’ academic performance has been collected (the final grade in the first call of the subject) and they have been confronted with different input variables: methodology used (three methodologies: lecture, flipped learning, hybrid methodology), gender and university access grade. The article shows the results of this analysis and establishes the possible correlations between the variables studied.


Programming teaching Teaching methodologies Flipped learning Lecture 


  1. 1.
    Compañ-Rosique, P., Satorre-Cuerda, R., Llorens-Largo, F., Molina-Carmona, R.: Enseñando a programar: un camino directo para desarrollar el pensamiento computacional. Revista de Educación a Distancia (46), October 2015Google Scholar
  2. 2.
    Anderson, L.W., Krathwohl, D.R. (eds.): A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives, Complete edn. Longman, New York (2001)Google Scholar
  3. 3.
    Joyce, B.R.: Models of Teaching, 9th edn. Pearson, Boston (2015)Google Scholar
  4. 4.
    Journal of Learning: Journal of Learning Styles (2016)Google Scholar
  5. 5.
    Castaño, A., Marquós, M., Satorre Cuerda, R., Jaume i Capó, A., López Álvarez, D.: Tengo una respuesta para usted sobre estilos de aprendizaje, creencias y cambios en los estudiantes, Universidade de Santiago de Compostela. Escola Técnica Superior d’Enxeñaria, pp. 275–282, July 2010Google Scholar
  6. 6.
    Knight, K.: Book Reviews: The Manual of Learning Styles Peter Honey and Alan Mumford, 83 p. Peter Honey, Maidenhead (1982). £25.20, ISBN 0 9508444 0 3. Manag. Educ. Dev. 14(2), 147–150 (1983)Google Scholar
  7. 7.
    Bloom, B.S., Hastings, J.T., Madaus, G.F.: Handbook on Formative and Summative Evaluation of Student Learning. McGraw-Hill, New York (1971)Google Scholar
  8. 8.
    Fidalgo-Blanco, Á., Sein-Echaluce, M.L., García-Peñalvo, F.J.: Micro flip teaching with collective intelligence. In: Zaphiris, P., Ioannou, A. (eds.) LCT 2018. LNCS, vol. 10924, pp. 400–415. Springer, Cham (2018). Scholar
  9. 9.
    de Los Ríos, I., Cazorla, A., Díaz-Puente, J.M., Yagüe, J.L.: Project-based learning in engineering higher education: two decades of teaching competences in real environments. Procedia - Soc. Behav. Sci. 2(2), 1368–1378 (2010)CrossRefGoogle Scholar
  10. 10.
    Spencer, K.: Kagan Cooperative Learning. Kagan Publishing, San Clemente (2009)Google Scholar
  11. 11.
    Llorens-Largo, F., Villagrá-Arnedo, C., Gallego-Durán, F., Satorre-Cuerda, R., Compañ-Rosique, P., Molina-Carmona, R.: LudifyME. In: Formative Assessment, Learning Data Analytics and Gamification, pp. 245–269. Elsevier (2016)Google Scholar
  12. 12.
    Delisle, R.: How to Use Problem-based Learning in the Classroom. ASCD, Alexandria (1997)Google Scholar
  13. 13.
    Naiman, L.: Design thinking as a strategy for innovationGoogle Scholar
  14. 14.
    Yusuf, M.: Infusing thinking-based learning in twenty-first-century classroom: the role of training programme to enhance teachers’ skilful thinking skills. In: Amzat, I.H., Valdez, N.P. (eds.) Teacher Empowerment Toward Professional Development and Practices, pp. 211–220. Springer, Singapore (2017). Scholar
  15. 15.
    Fidalgo, A.: Metodologías. Lección Magistral: Qué es y cómo mejorarla (2016)Google Scholar
  16. 16.
    Lage, M.J., Platt, G.J., Treglia, M.: Inverting the classroom: a gateway to creating an inclusive learning environment. J. Econ. Educ. 31(1), 30–43 (2000)CrossRefGoogle Scholar
  17. 17.
    Fidalgo-Blanco, A., Sein-Echaluce, M.L., García-Peñalvo, F.J.: APFT: active peer-based flip teaching. In: Proceedings of the 5th International Conference on Technological Ecosystems for Enhancing Multiculturality - TEEM 2017, Cádiz, Spain, pp. 1–7. ACM Press (2017)Google Scholar
  18. 18.
    Lammers, S.M.: Programmers at Work: Interviews with 19 Programmers who Shaped the Computer Industry. Tempus Books of Microsoft Press, Redmond (1989)Google Scholar
  19. 19.
    Swartz, R.: ENSE\(\tilde{\rm {N}}\)NAR A PENSAR: 9 PRINCIPIOS BASICOS-IX - INED21Google Scholar
  20. 20.
    Sthle, L., Wold, S.: Analysis of variance (ANOVA). Chemom. Intell. Lab. Syst. 6(4), 259–272 (1989)CrossRefGoogle Scholar
  21. 21.
    Smalheiser, N.R.: ANOVA. In: Data Literacy, pp. 149–155. Elsevier (2017)Google Scholar
  22. 22.
    Yoo, S.K., Cotton, S.L., Sofotasios, P.C., Matthaiou, M., Valkama, M., Karagiannidis, G.K.: The Fisher-Snedecor \(\cal{F}\) distribution: a simple and accurate composite fading model. IEEE Commun. Lett. 21(7), 1661–1664 (2017)Google Scholar
  23. 23.
    Taylor, R.: Interpretation of the correlation coefficient: a basic review. J. Diagn. Med. Sonogr. 6(1), 35–39 (1990)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Sedgwick, P.: Pearson’s correlation coefficient. BMJ 345, e4483–e4483 (2012)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Cátedra Santander-UA de Transformación DigitalUniversidad de AlicanteAlicanteSpain

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