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Students of the Introduction to Programming courses present low grades and this is reflected by the high failure and academic desertion rates. In this sense, looking for ways to improve and support the student's academic performance in the Fundamentals of Object-Oriented Programming (FPOO) course, a strategy based on Computer-Supported Collaborative Learning (CSCL) is proposed. It is supported by an algorithm to form workgroups automatically, which allows motivating students and creates a uniform criterion to develop programming tasks. Under the framework of the quasi-experimental design, the strategy was implemented in different evaluative activities of the FPOO course, which allowed us to answer questions related to the improvement of a student's final grade using the automatic formation of workgroups versus the traditional formation of workgroups. Moreover, we compared the grades obtained when activities are developed with no group formation. The experiments in this paper show that using the collaborative strategy improves students' grades by 22% in the laboratories and by 20% in the final project. In addition, it allows the exchange of knowledge to solve a programming task. This paper concludes that developing strategies that integrate collaboration positively impacts the programming learning process and improves student grades and people skills significantly, which encourage a better learning in programming courses.
M. Sahami et al., Computer Science Curricula 2013, vol. 1. IEEE Computer Society, 2013.
A. Ali and D. Smith, “Teaching an Introductory Programming Language in a General Education Course,” Journal of Information Technology Education: Innovations in Practice, vol. 13, pp. 57–67, 2014.
G. Alexandron, M. Armoni, M. Gordon, and D. Harel, “The effect of previous programming experience on the learning of scenario-based programming,” in Proceedings of the 12th Koli Calling International Conference on Computing Education Research, New York, NY, USA, Nov. 2012, pp. 151–159. doi: 10.1145/2401796.2401821.
M. McCracken et al., “A Multi-national, Multi-institutional Study of Assessment of Programming Skills of First-year CS Students,” in Working Group Reports from ITiCSE on Innovation and Technology in Computer Science Education, New York, NY, USA, 2001, pp. 125–180. doi: 10.1145/572133.572137.
S. Billis and O. Cubenas, “Assessing Collaborative Learning with E-Tools in Engineering and Computer Science Programs,” Advances in Intelligent Systems and Computing, vol. 1070, pp. 848–854, 2020, doi: 10.1007/978-3-030-32523-7_62.
S. I. Malik, “Improvements in Introductory Programming Course: Action Research Insights and Outcomes,” Systemic Practice and Action Research, vol. 31, no. 6, pp. 637–656, 2018, doi: 10.1007/s11213-018-9446-y.
L. Carvajal-Ortiz, B. Florian-Gaviria, and J. F. Díaz, “Modelos, métodos y prototipo de software para el apoyo del diseño, evaluación y análisis de aprendizajes en gestión curricular de la educación superior basada en competencias,” p. 10.
Universidad del Valle, “Reforma Currícular - Facultad de Ingeniería / Universidad del Valle / Cali, Colombia,” Reforma curricular 2020. http://ingenieria.univalle.edu.co/reforma-curricular (accessed Apr. 08, 2021).
C. Watson and F. W. B. Li, “Failure rates in introductory programming revisited,” in Proceedings of the 2014 conference on Innovation & technology in computer science education, New York, NY, USA, Jun. 2014, pp. 39–44. doi: 10.1145/2591708.2591749.
C. Watson and F. W. B. Li, “Failure rates in introductory programming revisited,” in Proceedings of the 2014 conference on Innovation & technology in computer science education - ITiCSE ’14, Uppsala, Sweden, 2014, pp. 39–44. doi: 10.1145/2591708.2591749.
L.-K. Soh, N. Khandaker, X. Liu, and H. Jiang, “A computer-supported cooperative learning system with multiagent intelligence,” in Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems - AAMAS ’06, New York, New York, USA, 2006, p. 1556. doi: 10.1145/1160633.1160933.
M. L. Séin-Echaluce, Á. Fidalgo Blanco, F. J. García-Peñalvo, and M. Á. Conde, “A Knowledge Management System to Classify Social Educational Resources Within a Subject Using Teamwork Techniques”, doi: 10.1007/978-3-319-20609-7_48.
C. Alvarado, C. B. Lee, and G. Gillespie, “New CS1 pedagogies and curriculum, the same success factors?,” in SIGCSE 2014 - Proceedings of the 45th ACM Technical Symposium on Computer Science Education, New York, New York, USA, 2014, pp. 379–384. doi: 10.1145/2538862.2538897.
A. R. Denham, R. Mayben, and T. Boman, “Integrating Game-Based Learning Initiative: Increasing the Usage of Game-Based Learning Within K-12 Classrooms Through Professional Learning Groups,” TechTrends, vol. 60, no. 1, pp. 70–76, Jan. 2016, doi: 10.1007/s11528-015-0019-y.
G. Lee, W. W. Fong, and J. Gordon, “Blended learning: The view is different from student, teacher, or institution perspective,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8038 LNCS, pp. 356–363, 2013, doi: 10.1007/978-3-642-39750-9_33.
A. J. Lakanen and V. Isomöttönen, “High school students’ perspective to university CS1,” in Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE, New York, New York, USA, 2013, pp. 261–266. doi: 10.1145/2462476.2465585.
Y.-H. Chen, W.-C. Lee, C.-H. Tseng, L. Y. Deng, C.-Y. Chang, and L.-H. Lee, “Cognitive learning performance assessment and analysis with CSCL applied on the NetGuru platform and CSPL applied on the TAoD platform for the network experiment class,” Journal of Supercomputing, vol. 76, no. 1, pp. 16–46, 2020, doi: 10.1007/s11227-019-02836-3.
Z. Mehennaoui, Y. Lafifi, H. Seridi, and A. Boudria, “A new approach for grouping learners in CSCL systems,” in International Conference on Multimedia Computing and Systems -Proceedings, 2014, pp. 628–632. doi: 10.1109/ICMCS.2014.6911143.
A. Böhne, N. Faltin, and B. Wagner, “Distributed group work in a remote programming laboratory - A comparative study,” International Journal of Engineering Education, vol. 23, no. 1, pp. 162–170, 2007.
A. Kardan and H. Sadeghi, “Modeling the learner group formation problem in computer-supported collaborative learning using mathematical programming,” 2015. doi: 10.1109/ICELET.2014.7040616.
F. D. Pereira et al., “Early Dropout Prediction for Programming Courses Supported by Online Judges,” in Artificial Intelligence in Education, vol. 11626, S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, and R. Luckin, Eds. Cham: Springer International Publishing, 2019, pp. 67–72. doi: 10.1007/978-3-030-23207-8_13.
G. STAHL, T. KOSCHMANN, and D. SUTHERS, Computer-supported collaborative learning: An historical perspective. na, 2006. [Online]. Available: https://www.semanticscholar.org/paper/Computer-supported-collaborative-learning-%3A-An-Stahl-Koschmann/00c0825352eded96cdc99fefcbfb52be4c6a796e
M. N. Demaidi, M. Qamhieh, and A. Afeefi, “Applying Blended Learning in Programming Courses,” IEEE Access, vol. 7, pp. 156824–156833, 2019, doi: 10.1109/ACCESS.2019.2949927.
G. Ayala, M. Ortíz, and M. Osorio, “Agent modelling for CSCL environments using answer sets programming,” in Proceedings of the Mexican International Conference on Computer Science, 2005, vol. 2005, pp. 214–221. doi: 10.1109/ENC.2005.9.
J. Lämsä, P. Uribe, A. Jiménez, D. Caballero, R. Hämäläinen, and R. Araya, “Deep Networks for Collaboration Analytics: Promoting Automatic Analysis of Face-to-Face Interaction in the Context of Inquiry-Based Learning,” Journal of Learning Analytics, vol. 8, no. 1, Art. no. 1, Apr. 2021, doi: 10.18608/jla.2021.7118.
J. Chen, M. Wang, P. A. Kirschner, and C.-C. Tsai, “The Role of Collaboration, Computer Use, Learning Environments, and Supporting Strategies in CSCL: A Meta-Analysis,” Review of Educational Research, vol. 88, no. 6, pp. 799–843, Dec. 2018, doi: 10.3102/0034654318791584.
M. Coto, S. Mora, and C. Collazos, “Evaluation of the collaboration process from an individual and collaborative perspective,” in ACM International Conference Proceeding Series, 2014, vol. 10-12-Sept, pp. 1–9. doi: 10.1145/2662253.2662342.
J. Bennedsen, “Failure Rates in Introductory Programming — 12 Years Later,” p. 6.
F. D. Pereira, E. H. T. Oliveira, D. Fernandes, and A. Cristea, “Early Performance Prediction for CS1 Course Students using a Combination of Machine Learning and an Evolutionary Algorithm,” in 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), Maceió, Brazil, Jul. 2019, pp. 183–184. doi: 10.1109/ICALT.2019.00066.
F. D. Pereira, S. C. Fonseca, E. H. T. Oliveira, D. B. F. Oliveira, A. I. Cristea, and L. S. G. Carvalho, “Deep learning for early performance prediction of introductory programming students: a comparative and explanatory study,” RBIE, vol. 28, pp. 723–748, Oct. 2020, doi: 10.5753/rbie.2020.28.0.723.
F. D. Pereira et al., “Explaining Individual and Collective Programming Students’ Behavior by Interpreting a Black-Box Predictive Model,” IEEE Access, vol. 9, pp. 117097–117119, 2021, doi: 10.1109/ACCESS.2021.3105956.
“Novel Approach to Facilitating Tradeoff Multi-Objective Grouping Optimization | IEEE Journals & Magazine | IEEE Xplore.” https://ieeexplore.ieee.org/document/7229323 (accessed Apr. 27, 2022).