Evaluation of an Intervention on Activity Planning in CS1

• Alberto Gómez ^[2] ; María-Jesús Marco-Galindo ^[1] ; Julià Minguillón ^[1]
  1. [1] Universitat Oberta de Catalunya

     Universitat Oberta de Catalunya

     Barcelona, España

     
  2. [2] Department of Computer and Telematic Systems Engineering, Universidad de Extremadura, Badajoz, Spain Computer Science, Multimedia and Telecommunications Department, Universitat Oberta de Catalunya, Barcelona, Spain
• Localización: Revista Iberoamericana de Tecnologías del Aprendizaje: IEEE-RITA, ISSN 1932-8540, Vol. 18, Nº. 3, 2023, págs. 287-294
• Idioma: inglés
• DOI: 10.1109/RITA.2023.3302174
• Texto completo no disponible (Saber más ...)
• Resumen

  • A key factor in online learning is an instructional design that ensures that students maintain an adequate and constant learning pace throughout the course. This is especially relevant when a fundamentally practical and progressive learning approach is required, such as in introductory programming courses. This article describes an intervention conducted in a first-year subject of the Computer Engineering degree called “Programming Fundamentals”. This subject poses many challenges related to the introduction of abstract concepts, the completion of programming exercises in a specific language, and the monitoring of the pace of proposed learning activities so that students can achieve adequate learning. Based on academic results from several semesters, it was decided to make an intervention that modified the planning of learning activities to maintain motivation and learning pace throughout the semester, while reducing the time between completing the activities and receiving feedback. An analysis of the results following the change shows that more students complete the core activities, with a decrease in dropouts from continuous assessment and an increase in the number of students passing the course. Data analysis has been validated using propensity score matching, a method for evaluating interventions with a quasi-experimental design.

• Referencias bibliográficas
  • A. Luxton-Reilly et al., “Introductory programming: A systematicliterature review,” in Proc. Companion 23rd Annu. ACM Conf.Innov. Technol....
  • C. Watson and F. W. B. Li, “Failure rates in introductory programmingrevisited,” in Proc. Conf. Innov. Technol. Comput. Sci. Educ. (ITiCSE).New...
  • A. S. Carter, C. D. Hundhausen, and O. Adesope, “Blending measuresof programming and social behavior into predictive models of studentachievement...
  • J. Sorva, “Visual program simulation in introductory programmingeducation,” Ph.D. dissertation, Dept. Comput. Sci. Eng., Aalto Univ.,Espoo,...
  • R. Hoda and P. Andreae, “It’s not them, it’s us! Why computer sciencefails to impress many first years,” in Proc. 16th Australas. Comput....
  • C. Ott, A. Robins, and K. Shephard, “Translating principles of effectivefeedback for students into the CS1 context,” ACM Trans. Comput. Educ.,vol....
  • L. Porter and D. Zingaro, “Importance of early performance in CS1:Two conflicting assessment stories,” in Proc. 45th ACM Tech. Symp.Comput....
  • G. Kanaparan, R. Cullen, and D. D. Mason, “Self-efficacy and engagementas predictors of student programming performance,” in Proc.PACIS,...
  • D. Bueno, Neurociencia Para Educadores, 3rd ed. Barcelona, Spain:Octaedro, 2017.
  • J. Biggs and C. Tang, Teaching for Quality Learning at University,4th ed. Maidenhead, U.K.: SRHE and Open Univ. Press, 2003.
  • B. E. Vaessen, A. van den Beemt, G. van de Watering,L. W. van Meeuwen, L. Lemmens, and P. D. Brok, “Students’perception of frequent assessments...
  • A. Sangra, “Decálogo para la mejora de la docencia online: Propuestaspara educar en contextos presenciales discontinuos,” in DecálogoPara...
  • A. Vihavainen, J. Airaksinen, and C. Watson, “A systematicreview of approaches for teaching introductory programming andtheir influence on...
  • M.-J. Marco-Galindo and J. Minguillón, “La evaluación formativacomo factor decisivo en el aprendizaje online. Intervención en unaasignatura...
  • G. Cousin, Researching Learning in Higher Education: An Introductionto Contemporary Methods and Approaches (SEDA Series).New York, NY, USA:...
  • A. J. Ko and S. A. Fincher, “A study design process,” in The CambridgeHandbook of Computing Education Research. Cambridge, U.K.: CambridgeUniv....
  • M. M. Arias, “Lectura crítica en pequeñas dosis índices de propensión.El deseo de parecerse al ensayo clínico,” Pediatría Atención Primaria,vol....
  • C. R. Ovalle, “Sobre la técnica de puntajes de propensión (propensityscore matching) y sus usos en la investigación en educación,” Educ.Cienc.,...
  • H. D. Harris, “Propensity score matching in higher educationassessment,” M.S. thesis, James Madison Univ., Harrisonburg,VA, USA, May 2015....
  • S. Guo, M. Fraser, and Q. Chen, “Propensity score analysis: Recentdebate and discussion,” J. Soc. Social Work Res., vol. 11, no. 3,pp. 463–482,...
  • H. Harris and S. J. Horst, “A brief guide to decisions at each step of thepropensity score matching process,” Practical Assessment Res. Eval.,vol....
  • L.-A. Lim et al., “What changes, and for whom? A study ofthe impact of learning analytics-based process feedback in a largecourse,” Learn....
  • D. E. Ho, K. Imai, G. King, and E. A. Stuart, “MatchIt: Nonparametricpreprocessing for parametric causal inference,” J. Stat. Softw., vol....
  • J. S. Sekhon, “Multivariate and propensity score matching software withautomated balance optimization: The matching package for R,” J. Stat.Softw.,...
  • N. Greifer. (2020). Cobalt: Covariate Balance Tables and Plots.[Online]. Available: https://cran.r-project.org/package=cobalt
  • J. E. Helmreich and R. M. Pruzek, “PSAgraphics: An R packageto support propensity score analysis,” J. Stat. Softw., vol. 29, no. 6,pp. 1–23,...
  • M. J. M. Galindo and J. P. Blázquez, “Necesidades específicas parala docencias de programación en un entorno virtual,” Actas JENUI,pp. 5–12,...
  • J. Minguillón, J. Conesa, M. E. Rodríguez, and F. Santanach, “Learninganalytics in practice: Providing e-learning researchers and practitionerswith...
  • A. V. Robins, “Novice programmers and introductory programming,”in The Cambridge Handbook of Computing Education Research(Cambridge Handbooks...
  • A. Luxton-Reilly, “Learning to program is easy,” in Proc. ACMConf. Innov. Technol. Comput. Sci. Educ. New York, NY, USA:Association for Computing...
  • G. Bain and I. Barnes, “Why is programming so hard to learn?” inProc. Conf. Innov. Technol. Comput. Sci. Educ. (ITiCSE). New York,NY, USA:...
  • T. Sancho-Vinuesa, R. Masià, M. Fuertes-Alpiste, and N. Molas-Castells,“Exploring the effectiveness of continuous activity with automaticfeedback...
  • A. Gómez, M. J. Marco-Galindo, J. Minguillón, andJ. Escayola-Mansilla, “Análisis de la mejora de los resultados deuna asignatura inicial...