El efecto de la autoeficacia y el trabajo colaborativo en estudiantes novatos de programación
DOI:
https://doi.org/10.33064/iycuaa2018741760Palabras clave:
programación, motivación, metacognición, autoeficacia, aprendizaje colaborativo.Resumen
El aprendizaje de la programación es complicado para alumnos que inician una carrera relacionada con las tecnologías de información. Se han identificado factores significativos como la experiencia previa, los modelos mentales incorrectos y la habilidad para las matemáticas, entre otros. También se ha identificado una relación estrecha entre el desempeño académico y los procesos cognitivos, metacognitivos y motivacionales. En este estudio se intentó identificar las variables motivacionales y metacognitivas que influyen en los estudiantes universitarios que inician el aprendizaje de la programación. Se utilizó el instrumento MSLQ para recolectar información de una muestra aleatoria de 110 alumnos cursando programación introductoria en la UAA.
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• Alexander, P. A. (2008). Why this and why now? Introduction to the special issue on metacognition, self-regulation, and self-regulated learning. Educational Psychology Review, 20, 369-372.
• Arévalo Mercado, A. C., Muñoz Andrade, E. L., & Gómez Reynoso,
J. M. (2011). A software tool to visualize verbal protocols to enhance strategic and metacognitive abilities in basic programming. International Journal of Interactive Mobile Technologies (iJIM), 5(3), 12-19.
• Azevedo, R., & Aleven, V. (Eds.). (2013). Overview of current interdisciplinary research. En Metacognition and learning technologies
(pp. 1-16). New York: Springer International Handbooks of Education.
• Baldwin, L. P., & Macredie, R. D. (1999). Beginners and programming: Insights from second language learning and teaching. Education and Information Technologies, 4(2), 167-179.
• Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122-147.
• Barak, M., Kastelan, I., & Azia, Z. (2016). Exploring aspects of self-regulated learning among engineering students learning digital system design in the FPGA environment—methodology and findings. En R. Szewczyk, I. Kaštelan, M. Temerinac, M. Barak, & V. Sruk (Eds.), Embedded Engineering Education (pp. 139-160). Springer International Publishing. doi: 10.1007/978-3-319-27540-6_10
• Blackwell, A. F. (2002). First steps in programming: A Rationale for attention investment models. Proceedings IEEE 2002 Symposia on Human Centric Computing Languages and Environments, HCC 2002, 2-10.
• Bornat, R., Dehnadi, S., & Simon. (2008). Mental models, consistency
and programming aptitude. Proc. Tenth Australasian Computing Education Conference, ACE 2008, 53-62.
• Byrne, P., & Lyons, G. (2001). The effect of student attributes on success in programming. ACM SIGCSE Bulletin, 33(3), 49-52.
• Cooper, S., Dann, W., & Pausch, R. (2000). Alice: A 3D tool for introductory programming concepts. Journal of Computing Sciences in Colleges, 15(5), 107-116.
• Dehnadi, S., & Bornat, R. (2006). The camel has two humps (working title) (pp. 1-21). London: Middlesex University. Recuperado de http://www.eis.mdx.ac.uk/research/PhDArea/saeed/paper1.pdf
• Dent, A. L., & Koenka, A. C. (2016). The relation between self-regulated learning and academic achievement across childhood and adolescence: A meta-analysis. Educational Psychology Review, 28(3), 425-474. doi: 10.1007/s10648-015-9320-8
• Departamento de Sistemas de Información UAA. (18 de febrero de 2014). [Foto del perfil en Facebook]. Recuperada de https://scontent.fntr4-1.fna.fbcdn.net/v/t31.0-8/1796885_1405880379669853_144507141_o.jpg?_nc_cat=0&oh=1f68b05a6d1b8dd2afc2c9fd67c6f6d1&oe=5B9347F0
• Dewey, J. (1913). Interest and effort in education. Boston: Riverside.
• __________ (1933). How we think. A restatement of the relation of reflective thinking to the educative process. Boston, MA: D. C. Heath. Brilliant.
• Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906-911.
• Forbes-Riley, K., & Litman, D. (2010). Metacognition and learning in spoken dialogue computer tutoring. En Intelligent tutoring systems (pp. 379-388). Berlin-Heidelberg-New York: Springer-Verlag.
• Heggestad, E. D., & Kanfer, R. (2005). The predictive validity of self-efficacy in training performance: Little More than past performance. Journal of Experimental Psychology: Applied, 11(2), 84-97. doi: 10.1037/1076-898X.11.2.84
• Jenkins, T. (2002). On the difficulty of learning to program. Loughborough, Leicestershire, UK: Loughborough University.
• Kuhl, J. (2000). A functional-design approach to motivation and self-regulation: The dynamics of personality systems and interactions. En M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 111-169). San Diego, CA, US: Academic Press.
• Mahmoud, Q. H., Dobosiewicz, W., & Swayne, D. (2004). Making computer programming fun and accessible. Computer, 37(2), 106-108. doi: 10.1109/MC.2004.1266305
• Maries, A., & Kumar, A. (2007). Concept maps in intelligent tutors for programming. Journal of Computing Sciences in Colleges, 22(3), 54.
• Mayer, R. E. (1988). The psychology of how novices learn computer programming. En E. Soloway, & J. C. Spohrer (Eds.), Studying the novice programmer (pp. 129-159). Hillsdale, NJ: Lawrence Erlbaum Associates.
• __________ (1998). Cognitive, metacognitive, and motivational aspects of problem solving. Instructional Science, 26(1-2), 49-63.
• __________ (2010). Applying the science of learning. Pearson.
• Milne, I., & Rowe, G. (2002). Difficulties in learning and teaching programming-views of students and tutors. Education and Information Technologies, 7(1), 55-66.
• Mow, I. T. C. (2008). Issues and difficulties in teaching novice computer programming. Innovative Techniques in Instruction Technology, E-learning, E-assessment, and Education, 199-204.
• Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ) [Documento en pdf]. Ann Arbor, MI: The University of Michigan. Recuperado de https://files.eric.ed.gov/fulltext/ED338122.pdf
• Ramalingam, V. L., La Belle, D., & Wiedenbeck, S. (2004). Selfefficacy
and mental models in learning to program. ITiCSE ‘04 Proceedings of the 9th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education, UK, 2004, 171-175. doi: 10.1145/1007996.1008042
• Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67.
• Schiefele, U. (1991). Interest, learning, and motivation. Educational Psychologist, 26(3-4), 299-323.
• Skubch, H., & Thielscher, M. (2005). Strategy Learning for Reasoning
Agents. En J. Gama, R. Camacho, P. Brazdil, A. Jorge, & L. Torgo (Eds.), Machine Learning: ECML 2005 (pp. 733-740). Berlin-Heidelberg: Springer.
• Volet, S. E., & Lund, C. P. (1994). Metacognitive instruction in introductory computer programming: A better explanatory construct for performance than traditional factors. Journal of Educational Computing Research, 10(4), 297-328.
• Wiedenbeck, S., LaBelle, D., & Kain, V. N. R. (2004). Factors affecting course outcomes in introductory programming. En E. Dunican, & T. R. G. Green (Eds.), Proceedings of the Psychology of Programming Interest Group 16 (pp. 97-110). Ireland: PPIG.
• Wong, L.-H., Chai, C.-S., Chen, W., & Chin, C.-K. (2013). Measuring
singaporean students’ motivation and strategies of bilingual learning. The Asia-Pacific Education Researcher, 22(3), 263-272. doi: 10.1007/s40299-012-0032-2
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