Intervention of Three Educational Strategies for Higher Education Programming Courses
Abstract The main objective of present work is to show the results of interventions carried out in front of a group of three educational strategies that allowed having better percentages of accreditation and qualification, as well as decrease of dropout compared to those obtained in the last 8 years in initial courses of programming at the Technological University of Puebla. The first intervention involved evaluation of computational thinking through skills of generalization, decomposition, abstraction, evaluation and algorithmic design, this evaluation was the first strategy that allowed offering students 10 learning scenarios for Programming Methodology course. In the second intervention, 4 elements were manipulated to offer study options in Programming course, which were content, work modes, rhythms and time and evaluation; it was the second strategy with intention of creating personalized education. In both interventions, use of Moodle platform allowed exposing learning content and having an extra tool for students; the third strategy was consequently the use of b-learning. The main result obtained through voluntary surveys carried out by students, was the generation of a learning experience that contributed to motivation of student in line with academic goals of the aforementioned courses, so it can be concluded that combination of the strategies carried out in the two interventions improved accreditation rates and decreased percentage of dropouts, although there is still work to be done to improve average rating.
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Rojas-López, A. y García-Peñalvo, F. J. (2016a). Personalized contents based on cognitive level of student’s computational thinking for learning basic competencies of programming using an environment b-learning. In F. J. García-Peñalvo (Ed.), Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM’16) (Salamanca, Spain, November 2-4, 2016) (pp. 1139-1145). New York, NY, USA: ACM. http://doi.org/10.1145/3012430.3012660
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Tejeda-Lorente, A., Bernabé-Moreno, J., Porcel, C., Galindo-Moreno, P. y Herrera-Viedma, E. A. (2015). Dynamic recommender system as reinforcement for personalized education by a fuzzly linguistic web system. Procedia Computer Science, 55, 1143-1150. http://doi.org/10.1016/j.procs.2015.07.084
Tekin, C., Braun, J. y Van Der Schaar, M. (2015). eTutor: Online learning for personalized education. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, (pp.5545-5549). EEUU: IEEE. http://doi.org/10.1109/ICASSP.2015.7179032.
UK Bebras Computational Thinking Challenge, answers (2015). University of Oxford, available http://www.bebras.org
Villegas-Ch, W. y Luján-Mora, S. (2017). Analysis of data mining techniques applied to LMS for personalized education. EDUNINE 2017 - IEEE World Engineering Education Conference: Engineering Education - Balancing Generalist and Specialist Formation in Technological Carriers: A Current Challenge, Proceedings (pp. 85-89). EEUU: IEEE. http://doi.org/10.1109/EDUNINE.2017.7918188 http://doi.org/10.1145/2960310.2960347
Wing J. M. (2006). Computational Thinking. Communications of the ACM, 49(3), 33-35. http://doi.org/10.1145/1118178.1118215.
Zhao, F.-Q. (2016). Personalized Education Approaches for Chemical Engineering and Relevant Majors. MATEC Web of Conferences, 68, http://doi.org/10.1051/matecconf/20166820003
Weese, J. L. (2016). Mixed methods for the assessment and incorporation of computational thinking in K-12 and higher education. ICER 2016 - Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 279-280). New York, NY, USA: ACM.
Chrysafiadi, K. y Virvou, M. (2015a). A novel hybrid student model for personalized education. Advances in Personalized Web-Based Education (pp. 61-90). Cham: Springer. http://doi.org/10.1007/978-3-319-12895-5_3
Chrysafiadi, K. y Virvou, M. (2015b). Student Modeling for Personalized Education: A Review of the Literature. Advances in Personalized Web-Based Education (pp. 1-24). Cham: Springer. http://doi.org/10.1007/978-3-319-12895-5_1
Czerkawski, B. C. y Lyman, E. W. (2015). Exploring Issues About Computational Thinking in Higher Education. TechTrends, 59(2), 57-65. http://doi.org/10.1007/s11528-015-0840-3
García-Peñalvo, F. J. (2013). Education in knowledge society: A new PhD programme approach. In F. J. García-Peñalvo (Ed.), Proceedings of the First International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM’13) (Salamanca, Spain, November 14-15, 2013) (pp. 575-577). New York, NY, USA: ACM. http://doi.org/10.1145/2536536.2536624.
García-Peñalvo, F. J. (2014). Formación en la sociedad del conocimiento, un programa de doctorado con una perspectiva interdisciplinar. Education in the Knowledge Society, 15(1), 4-9.
García-Peñalvo, F. J. (2015). Engineering contributions to a Knowledge Society multicultural perspective. IEEE Revista Iberoamericana de Tecnologías del Aprendizaje (IEEE RITA), 10(1), 17-18. http://doi.org/10.1109/RITA.2015.2391371.
García-Peñalvo, F. J. (2016a). What Computational Thinking Is. Journal of Information Technology Research, 9(3), v-viii.
García-Peñalvo, F. J. (2016b). Proyecto TACCLE3 – Coding. In F. J. García-Peñalvo y J. A. Mendes (Eds.), XVIII Simposio Internacional de Informática Educativa, SIIE 2016 (pp. 187-189). Salamanca, España: Ediciones Universidad de Salamanca.
García-Peñalvo, F. J. (2016c) A brief introduction to TACCLE 3 – Coding European Project. In F. J. García-Peñalvo y J. A. Mendes (Eds.), 2016 International Symposium on Computers in Education (SIIE16). USA: IEEE. http://doi.org/10.1109/SIIE.2016.7751876.
García-Peñalvo, F. J. y Cruz-Benito, J. (2016). Computational thinking in pre-university education. In F. J. García-Peñalvo (Ed.), Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM’16) (Salamanca, Spain, November 2-4, 2016) (pp. 13-17). New York, NY, USA: ACM. http://doi.org/10.1145/3012430.3012490.
García-Peñalvo, F. J., Reimann, D., Tuul, M., Rees, A. y Jormanainen, I. (2016). An overview of the most relevant literature on coding and computational thinking with emphasis on the relevant issues for teachers. TACCLE3 Consortium. Belgium: TACCLE3 Consortium. http://doi.org/10.5281/zenodo.165123
Hart, S. A. (2016). Precision Education Initiative: Moving Toward Personalized Education. Mind, Brain, and Education, 10(4), 209-211. http://doi.org/10.1111/mbe.12109.
Kostolányová, K. (2017). Adaptation of Personalized Education in E-learning Environment. In T. T. Wu, R. Gennari, Y. M. Huang, H. Xie y Y. Cao (Eds.), Emerging Technologies for Education. SETE 2016 (pp.433-442). Cham: Springer. http://doi.org/10.1007/978-3-319-52836-6_46
Kucirkova, N. y Littleton, K. (2017). Developing personalised education for personal mobile technologies with the pluralisation agenda. Oxford Review of Education, 43(3), 276-288. http://doi.org/10.1080/03054985.2017.1305046
Secretaría de Educación Pública. (2017). Ruta para la implementación del modelo educativo. SEPMéxico (pp. 14-16).
Laksitowening, K. A. y Hasibuan, Z. A. (2015). Personalized e-learning architecture in standardbased education. Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015 (pp. 110-114). http://doi.org/10.1109/ICSITech.2015.7407787.
Morrowy, T., Sarvestaniz, S. S. y Hursony, A. R. (2016). Algorithmic decision support for personalized education. Proceedings - 2016 IEEE 17th International Conference on Information Reuse and Integration, IRI 2016 (pp. 188-197). http://doi.org/10.1109/IRI.2016.32
Román M., Pérez J. C. y Jiménez C. (2015). Test de Pensamiento Computacional: diseño y psicometría general Computational Thinking Test: design y general psychometry. III Congreso Internacional sobre Aprendizaje, Innovación y Competitividad (CINAIC 2015), octubre 14-16, 2015, Madrid, España.
Rojas-López, A. y García-Peñalvo, F. J. (2016a). Personalized contents based on cognitive level of student’s computational thinking for learning basic competencies of programming using an environment b-learning. In F. J. García-Peñalvo (Ed.), Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM’16) (Salamanca, Spain, November 2-4, 2016) (pp. 1139-1145). New York, NY, USA: ACM. http://doi.org/10.1145/3012430.3012660
Rojas-López, A. y García-Peñalvo, F. J. (2016b). Relationship of knowledge to learn in programming methodology and evaluation of computational thinking. In F. J. García-Peñalvo (Ed.), Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM’16) (Salamanca, Spain, November 2-4, 2016) (pp. 73-77). New York, NY, USA: ACM. http://doi.org/10.1145/3012430.3012499
Sadovaya, V. V., Korshunova, O. V. y Nauruzbay, Z. Z. (2016). Personalized education strategies. Mathematics Education, 11(1), 199-209. http://doi.org/10.12973/iser.2016.21019a
Selby, C. C. (2015). Relationships: computational thinking, pedagogy of programming, and Bloom’s Taxonomy. In Proceedings of the Workshop in Primary and Secondary Computing Education (WiPSCE‘15) (pp. 80-87). New York, NY, USA: ACM. http://doi.org/10.1145/2818314.2818315.
Sun, N., Li, K. y Zhu, X. (2016). Action Research on Visualization Learning of Mathematical Concepts Under Personalized Education Idea: Take Learning of Geometrical Concepts of Elementary Math for Example. In S. Cheung, L. Kwok, J. Shang, A. Wang y R. Kwan (Eds.), Blended Learning: Aligning Theory with Practices. ICBL 2016 (pp. 348-359). Cham: Springer. http://doi.org/10.1007/978-3-319-41165-1_31
TACCLE 3 Consortium. (2017) TACCLE 3: Coding Erasmus + Project website. Retrieved from http://www.taccle3.eu/
Talent Search (2015). Elite: Grade 12+, Institute of IT Professionals South Africa, available http://www.olympiad.org.za.
Tejeda-Lorente, A., Bernabé-Moreno, J., Porcel, C., Galindo-Moreno, P. y Herrera-Viedma, E. A. (2015). Dynamic recommender system as reinforcement for personalized education by a fuzzly linguistic web system. Procedia Computer Science, 55, 1143-1150. http://doi.org/10.1016/j.procs.2015.07.084
Tekin, C., Braun, J. y Van Der Schaar, M. (2015). eTutor: Online learning for personalized education. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, (pp.5545-5549). EEUU: IEEE. http://doi.org/10.1109/ICASSP.2015.7179032.
UK Bebras Computational Thinking Challenge, answers (2015). University of Oxford, available http://www.bebras.org
Villegas-Ch, W. y Luján-Mora, S. (2017). Analysis of data mining techniques applied to LMS for personalized education. EDUNINE 2017 - IEEE World Engineering Education Conference: Engineering Education - Balancing Generalist and Specialist Formation in Technological Carriers: A Current Challenge, Proceedings (pp. 85-89). EEUU: IEEE. http://doi.org/10.1109/EDUNINE.2017.7918188 http://doi.org/10.1145/2960310.2960347
Wing J. M. (2006). Computational Thinking. Communications of the ACM, 49(3), 33-35. http://doi.org/10.1145/1118178.1118215.
Zhao, F.-Q. (2016). Personalized Education Approaches for Chemical Engineering and Relevant Majors. MATEC Web of Conferences, 68, http://doi.org/10.1051/matecconf/20166820003
Weese, J. L. (2016). Mixed methods for the assessment and incorporation of computational thinking in K-12 and higher education. ICER 2016 - Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 279-280). New York, NY, USA: ACM.
Rojas López, A. (2017). Intervention of Three Educational Strategies for Higher Education Programming Courses. Education in The Knowledge Society, 18(4), 21–34. https://doi.org/10.14201/eks20171842134
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