Diseño y validación interna de un instrumento de autoinforme para caracterizar las estrategias de aprendizaje de programación de computadores

ilustraciones, tablas

Autores:: Torres Jiménez, Stephanie

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Diseño y validación interna de un instrumento de autoinforme para caracterizar las estrategias de aprendizaje de programación de computadores
dc.title.translated.eng.fl_str_mv	Design and internal validation of a self-report instrument to characterize computer programming learning strategies
title	Diseño y validación interna de un instrumento de autoinforme para caracterizar las estrategias de aprendizaje de programación de computadores
spellingShingle	Diseño y validación interna de un instrumento de autoinforme para caracterizar las estrategias de aprendizaje de programación de computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Aprendizaje autorregulado Programación de computadores Estrategias de aprendizaje Instrumento de autoinforme propiedad psicométrica CEAPC MSLQ LASSI Self-regulated learning Computer programming Learning strategies Self-report instrument Psychometric property Programación de computadores Computer programming Método de enseñanza Teaching methods
title_short	Diseño y validación interna de un instrumento de autoinforme para caracterizar las estrategias de aprendizaje de programación de computadores
title_full	Diseño y validación interna de un instrumento de autoinforme para caracterizar las estrategias de aprendizaje de programación de computadores
title_fullStr	Diseño y validación interna de un instrumento de autoinforme para caracterizar las estrategias de aprendizaje de programación de computadores
title_full_unstemmed	Diseño y validación interna de un instrumento de autoinforme para caracterizar las estrategias de aprendizaje de programación de computadores
title_sort	Diseño y validación interna de un instrumento de autoinforme para caracterizar las estrategias de aprendizaje de programación de computadores
dc.creator.fl_str_mv	Torres Jiménez, Stephanie
dc.contributor.advisor.none.fl_str_mv	Ramírez Echeverry, Jhon Jairo Restrepo Calle, Felipe
dc.contributor.author.none.fl_str_mv	Torres Jiménez, Stephanie
dc.contributor.researchgroup.spa.fl_str_mv	PLaS - Programming Languages and Systems
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
topic	000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Aprendizaje autorregulado Programación de computadores Estrategias de aprendizaje Instrumento de autoinforme propiedad psicométrica CEAPC MSLQ LASSI Self-regulated learning Computer programming Learning strategies Self-report instrument Psychometric property Programación de computadores Computer programming Método de enseñanza Teaching methods
dc.subject.proposal.spa.fl_str_mv	Aprendizaje autorregulado Programación de computadores Estrategias de aprendizaje Instrumento de autoinforme propiedad psicométrica
dc.subject.proposal.eng.fl_str_mv	CEAPC MSLQ LASSI Self-regulated learning Computer programming Learning strategies Self-report instrument Psychometric property
dc.subject.spines.none.fl_str_mv	Programación de computadores Computer programming
dc.subject.unesco.none.fl_str_mv	Método de enseñanza Teaching methods
description	ilustraciones, tablas
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-08-02T17:38:23Z
dc.date.available.none.fl_str_mv	2021-08-02T17:38:23Z
dc.date.issued.none.fl_str_mv	2021
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/79877
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/79877 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Ambrosio, A. P., Almeida, L., Franco, A., Martins, S. & Georges, F. (2012). Assessment of self-regulated attitudes and behaviors of introductory programming students. Proceedings - Frontiers in Education Conference, FIE, 1-6. https://doi.org/10.1109/FIE.2012.6462314 Aron, J. D. (1974). The Program Development Process, Addison-Wesley. Babakus, E. & Mangold, W. G. (1992). Adapting the SERVQUAL Scale to Hospital Services: An empirital investigation. Bandura, A. (2005). Guide for constructing self-efficacy scales. https://doi.org/10.1017/CBO9781107415324.004 Begel, A. & Simon, B. (2008). Novice software developers, all over again. ICER’08 - Proceedings of the ACM Workshop on International Computing Education Research, 1(425), 3-14. https://doi.org/10.1145/1404520.1404522 Berger, J.-L. & Karabenick, S. A. (2011). Motivation and students’ use of learning strategies: Evidence of unidirectional effects in mathematics classrooms. Learning and Instruction, 416-428. Bergin, S., Reilly, R. & Traynor, D. (2005). Examining the role of self-regulated learning on introductory programming performance. First International Workshop on Computing Education Research, 81-86. https://doi.org/10.1145/1089786.1089794 Biggs, J. B. (1987). Study Process Questionnaire Manual. https://files.eric.ed.gov/fulltext/ED308200.pdf Bishop-Clark, C. (1995). Cognitive style, personality, and computer programming. Computers in Human Behavior, 11(2), 241-260. https://doi.org/10.1016/0747-5632(94)00034-F Boekaerts, M. (1996). Self-regulated Learning at the Junction of Cognition and Motivation. Boekaerts, M. (1999). Motivated learning: Studying student * situation transactional units. https://doi.org/10.1007/BF03173110 Bryman, A. (2012). Social Reseach Methods (4th, Vol. 66). Oxford University Press. Cárdenas, S. F. (2013). Los constructos en las investigaciones pedagógicas: cuantificación y tratamiento estadístico. Revista Científico Pedagógica, 24(23). Carretero-Dios, H. & Pérez, C. (2005). Normas para el desarrollo y revisión de estudios instrumentales. International Journal of Clinical and Health Psychology, 5(3), 521-551. Caruso, T., Hill, N., Van DeGrift, T. & Simon, B. (2011). Experience report: Getting novice programmers to THINK about improving their software development process. SIGCSE’11 - Proceedings of the 42nd ACM Technical Symposium on Computer Science Education, 493-498. https://doi.org/10. 1145/1953163.1953307 Castellanos, H., Restrepo-Calle, F., González, F. A. & Echeverry, J. J. R. (2017). Understanding the relationships between self-regulated learning and students source code in a computer programming course. Proceedings - Frontiers in Education Conference, FIE, 2017-Octob, 1-9. https://doi.org/10.1109/FIE.2017.8190467 Cetin, I. & Ozden, M. Y. (2015). Development of computer programming attitude scale for university students. Computer Applications in Engineering Education, 23(5), 667-672. https://doi.org/10.1002/cae.21639 Chen, C. & Whitesel, J. (2012). The Validity and Reliability Study of a Revised Motivated Strategy for Learning Questionnaire (MSLQ) for Assessing Computer Software Learning Strategies. International Journal of E-Adoption, 4(2), 28-51. https://doi.org/10.4018/jea.2012040103 Cheng, G., Poon, L. K., Lau, W. W. & Zhou, R. C. (2019). Applying eye tracking to identify students’ use of learning strategies in understanding program code. ACM International Conference Proceeding Series, 140-144. https://doi.org/10.1145/3345120.3345144 Chyung, Y. S., Moll, A. J. & Berg, S. A. (2010). The Role of Intrinsic Goal Orientation, Self-Efficacy, and E-Learning Practice in Engineering Education. Cicchinelli, A., Veas, E., Pardo, A., Pammer-Schindler, V., Fessl, A., Barreiros, C. & Lindstädt, S. (2018). Finding traces of self-regulated learning in activity streams. Proceedings of the 8th International Conference on Learning Analytics and Knowledge - LAK ’18, 191-200. https://doi.org/10.1145/3170358.3170381 Clement, C. A., Kurland, D. M., Mawby, R. & Pea, R. D. (1986). Analogical Reasoning and Computer Programming. Journal of Educational Computing Research, 2(4), 473-486. https://doi.org/10.2190/dfh5-e0pg-1ml4-m34j Creswell, J. W. (2009). Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage Publications. Croasmun, J. T. . & Ostrom, L. (2011). Using Likert-type scales in the social sciences. Curione, K. & Huertas, J. A. (2017). MSLQ-UY , validation with Uruguayan university students. 17(2),1-17. Danielsiek, H., Toma, L. & Vahrenhold, J. (2017). An instrument to assess self-Efficacy in introductory algorithms courses. ACM International Computing Education Research, 9(1), 56-65. https://doi.org/10.1145/3105726.3106171 Dawes, J. (2008). Do data characteristics change according to the number of scale points used? An experiment using 5-point, 7-point and 10- point scales. International Journal of Market Research, 61-104. Decker, A. & McGill, M. M. (2019). A topical review of evaluation instruments for computing education. SIGCSE 2019 - Proceedings of the 50th ACM Technical Symposium on Computer Science Education, 558-564. https://doi.org/10.1145/3287324.3287393 Demir, O. & Seferoglu, S. S. (2020). A Comparison of Solo and Pair Programming in Terms of Flow Experience, Coding Quality, and Coding Achievement. Journal of Educational Computing Research, 1-19. Díaz-Bravo, L., Torruco-García, U., Martínez-Hernández, M. & Varela-Ruiz, M. (2013). La Entrevista, Recurso Flexible y Dinámico. Investigación en educación médica, 2(7), 162-167. Dorn, B. & Elliott Tew, A. (2015). Empirical validation and application of the computing attitudes survey. Computer Science Education, 25(1), 1-36. Ebomoyi, J. I. (2020). Metacognition and Peer Learning Strategies as Predictors in Problem-Solving Performance in Microbiology. 21(1). Eltegani, N. & Butgereit, L. (2015). Attributes of students engagement in fundamental programming learning. Proceedings - 2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering, ICCNEEE 2015, 101-106. https://doi.org/10.1109/ICCNEEE.2015.7381438 Entwistle, N. & McCune, V. (2004). The conceptual bases of study strategy inventories. Educational Psychology Review, 16(4), 325-345. https://doi.org/10.1007/s10648-004-0003-0 Escobar-Avila, J., Venuti, D., Di Penta, M. & Haiduc, S. (2019). A survey on online learning preferences for computer science and programming. Proceedings - 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering Education and Training, ICSE-SEET 2019, 41, 170-181. https://doi.org/10.1109/ICSE-SEET.2019.00026 Falkner, K., Vivian, R. & Falkner, N. J. (2014). Identifying computer science self-regulated learning strategies. ITICSE 2014 - Proceedings of the 2014 Innovation and Technology in Computer Science Education Conference, 291-296. https://doi.org/10.1145/2591708.2591715 Ferrando, P. J. & Anguiano-Carrasco, C. (2010). El análisis factorial como técnica de investigación en psicología. Papeles del Psicólogo, 31, 28-33. Fuller, U., Johnson, C. G., Ahoniemi, T., Cukierman, D., Hernán-Losada, I., Jackova, J., Lahtinen, E., Lewis, T. L., Thompson, D. M., Riedesel, C. & Thompson, E. (2007). Developing a computer science-specific learning taxonomy. ACM SIGCSE Bulletin, 39(4), 152-170. https://doi.org/10.1145/1345375.1345438 Gallego-Romero, J. M., Alario-Hoyos, C., Estévez-Ayres, I. & Delgado Kloos, C. (2020). Analyzing learners’ engagement and behavior in MOOCs on programming with the Codeboard IDE. https://doi.org/10.1007/s11423-020-09773-6 Garcia, R., Falkner, K. & Vivian, R. (2018). Systematic literature review: Self-Regulated Learning strategies using e-learning tools for Computer Science. Computers and Education, 123, 150-163. https://doi.org/10.1016/j.compedu.2018.05.006 García, T. & McKeachie, W. (2006). The Making of the Motivated Strategies for Learning Questionnaire. Journal of the American Mathematical Society, 19(3), 717-736. https://doi.org/10.1090/S0894-0347-05-00517-5 González-Torres, M.-C. & Torrano, F. (2012). Methods and instruments for measuring self-regulated learning. En A. Valle y J. C. Nunez (Eds.), Handbook of Instructional Resources & Applications. Nova Science Publishers, Inc. Hamui-Sutton, A. & Varela-Ruiz, M. (2013). La técnica de grupos focales. Investigación en Educación Médica, 2(5), 55-60. https://doi.org/10.1016/s2007-5057(13)72683-8 Hulin, C., Netemeyer, R. & Cudeck, R. (2001). Can a Reliability Coefficient Be Too High? Inzunza, B., Pérez, C., Márquez, C., Ortiz, L., Marcellini, S. & Duk, S. (2016). Estructura Factorial y Confiabilidad del Cuestionario de Motivación y Estrategias de Aprendizaje, MSLQ, en Estudiantes Universitarios Chilenos de Primer Año. https://doi.org/10.21865/RIDEP47.2.02 Jackson, C. R. (2018). Validating and Adapting the Motivated Strategies for Learning Questionnaire (MSLQ) for STEM Courses at an HBCU. AERA Open, 4, 1-16. https://doi.org/10.1177/2332858418809346 Jacobse, A. E. & Harskamp, E. G. (2012). Towards efficient measurement of metacognition in mathematical problem solving. Metacognition and Learning, 7(2), 133-149. https://doi.org/10.1007/s11409-012-9088-x Juarez-Ramirez, R., Navarro, C. X., Tapia-Ibarra, V., Macias-Olvera, R. & Guerra-Garcia, C. (2019). What is Programming? Putting all Together - A Set of Skills Required. 2018 6th International Conference in Software Engineering Research and Innovation (CONISOFT), 11-20. https://doi.org/10.1109/conisoft.2018.8645956 Konecki, M. (2014). Problems in Programming Education and Means of Their Improvement. https://doi.org/10.2507/daaam.scibook.2014.37 Korkmaz, Ö., Çakir, R. & Özden, M. Y. (2017). A validity and reliability study of the computational thinking scales (CTS). Computers in Human Behavior, 72, 558-569. https://doi.org/10.1016/j.chb.2017.01.005 Kumar, V., Winne, P., Hadwin, A., Nesbit, J., Jamieson-Noel, D., Calvert, T. & Samin, B. (2005). Effects of self-regulated learning in programming. Fifth IEEE International Conference on Advanced Learning Technologies (ICALT’05), 383-387. https://doi.org/10.1109/ICALT.2005.131 Linhorst, D. M. (2002). A Review of the Use and Potential of Focus Groups in Social Work Research. Qualitative Social Work, 1(2), 208-228. Loksa, D. & Ko, A. J. (2016). The Role of Self-Regulation in Programming Problem Solving Process and Success. Proceedings of the 2016 ACM Conference on International Computing Education Research -ICER ’16, 83-91. https://doi.org/10.1145/2960310.2960334 Loksa, D., Ko, A. J., Jernigan, W., Oleson, A., Mendez, C. J. & Burnett, M. M. (2016). Programming, Problem Solving, and Self-Awareness: Effects of Explicit Guidance. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 1449-1461. https://doi.org/10.1145/2858036.2858252 McDougall, A., Boyle, M., Pellas, N. & Peroutseas, E. (2016). Student Strategies for Learning Computer Programming: Implications for Pedagogy in Informatics. Journal of Educational Computing Research, 54(2), 109-116. https://doi.org/10.1023/B:EAIT.0000027924.69726.b5 McGill, T. J. & Volet, S. E. (1997). A conceptual framework for analyzing students’ knowledge of programming. Journal of Research on Computing in Education, 29(3), 276-297. https://doi.org/10.1080/08886504.1997.10782199 Méndez Martínez, C. & Sepúlveda Rondón, M. A. (2012). Introducción al análisis factorial exploratorio. Revista Colombiana de Psiquiatría, 41(1), 197-207. https://doi.org/10.1016/s0034-7450(14)60077-9 Muñiz, J., Elosua, P. & Hambleton, R. (1975). International Test Commission Guidelines for Test Translation and Adaptation. https://doi.org/10.1111/j.1464-0597.1975.tb00322.x Nassar-McMillan, S. C., Wyer, M., Oliver-Hoyo, M. & Ryder-Burge, A. (2010). Using Focus Groups in Preliminary Instrument Development: Expected and Unexpected Lessons Learned Sylvia. The Qualitative Report, 15(6), 1621-1634. Nausheen, M. (2016). An Adaptation of the Motivated Strategies for Learning Questionnaire (MSLQ) for Postgraduate Students in Pakistan: Results of an Exploratory Factor Analysis An Adaptation of the MSLQ for PGS in Pakistan: Results of an Exploratory Factor Analysis. Bulletin of Education and Research, 38(1), 1-16. Niemczyk, M. C. & Savenye, W. C. (2001). The Relationship of student motivation and self-regulated learning strategies to performance in an undergraduate computer literacy course. Papers Presented at the National Convention of the Association for Educational Communications and Technology, 311-322. Osterlind, S. J. (1991). Constructing Test Items: Multiple-Choice, Constructed-Response, Performance, and Other Formats (Vol. 14). https://doi.org/10.1016/0149-7189(91)90058-o Ostroff, C. & Kozlowski, S. W. (1992). Organizational Socialization As a Learning Process: the Role of Information Acquisition. https://doi.org/10.1111/j.1744-6570.1992.tb00971.x Ott, C., Robins, A., Haden, P. & Shephard, K. (2015). Illustrating performance indicators and course characteristics to support students’ self-regulated learning in CS1. Computer Science Education, 25(2), 174-198. https://doi.org/10.1080/08993408.2015.1033129 Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. https://doi.org/10.3389/fpsyg.2017.00422 Park, H., Kim, K., Robertson, C. & Kim, D. (2019). The Effect of Memorization on the Retention and Learning Acquisition of Programming Practice. Journal of Strategic Innovation and Sustainability, 14(2), 129-135. https://doi.org/10.33423/jsis.v14i2.1378 Pedrosa, D., Cravino, J., Morgado, L. & Barreira, C. (2016). Self-regulated learning in computer programming: Strategies students adopted during an assignment. Communications in Computer and Information Science, 621, 87-101. https://doi.org/10.1007/978-3-319-41769-1_7 Pedrosa, D., Cravino, J., Morgado, L. & Barreira, C. (2017). Self-regulated learning in higher education: strategies adopted by computer programming students when supported by the SimProgramming approach. Production, 27(spe), 1-15. https://doi.org/10.1590/0103-6513.225516 Pett, M. & Lackey, N. (2003). Making Sense of Factor Analysis (Vol. 50). https://doi.org/10.4135/9781412984898 Pintrich, P. R. & De Groot, E. V. (1990). Motivational and Self-Regulated Learning Components of Classroom Academic Performance. Anzeiger für Schädlingskunde, 3(2), 13-15. https://doi.org/10.1007/BF02338175 Pintrich, P. R., Smith, D. A. F., Garcia, T. & Mckeachie, W. J. (1991). A Manual for the Use of the Learning Questionnaire Motivated Strategies for (MSLQ). https://doi.org/10.5901/mjss.2015.v6n1p156 Pintrich, P. R., Smith, D. A. F., Garcia, T. & Mckeachie, W. J. (1993). Reliability and Predictive Validity of the Motivated Strategies for Learning Questionnaire (Mslq). Educational and Psychological Measurement, 53(3), 801-813. https://doi.org/10.1177/0013164493053003024 Porter, R. & Calder, P. (2004). Patterns in learning to program: an experiment? http://dl.acm.org/citation.cfm?id=980000 Prather, J., Pettit, R., Becker, B. A., Denny, P., Loksa, D., Peters, A., Albrecht, Z. & Masci, K. (2019). First Things First: Providing Metacognitive Scaffolding for Interpreting Problem Prompts. Proceedings of the 50th ACM Technical Symposium on Computer Science Education - SIGCSE ’19, 531-537. https://doi.org/10.1145/3287324.3287374 Rafique, W., Dou, W., Hussain, K. & Ahmed, K. (2020). Factors influencing programming expertise in a web-based e-learning paradigm. Online Learning Journal, 24(1), 162-181. https://doi.org/10. 24059/olj.v24i1.1956 Ramalingam, V. & Wiedenbeck, S. (2005). Development and Validation of Scores on a Computer Programming Self-Efficacy Scale and Group Analyses of Novice Programmer Self-Efficacy. Journal of Educational Computing Research, 19(4), 367-381. https://doi.org/10.2190/c670-y3c8-ltj1-ct3p Ramírez, M. d. C., Canto, J. E., Bueno, J. A. & Echazarreta, A. (2013). Validación Psicométrica del Motivated Strategies for Learning Questionnaire en Universitarios Mexicanos. Electronic Journal of Research in Educational Psychology, 11(1). Ramírez Echeverry, J. J., Rosales Castro, L. F., Restrepo Calle, F. & González, F. A. (2018). Self-regulated learning in a computer programming course. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 13(2), 75-83. https://doi.org/10.1109/RITA.2018.2831758 Ramírez-Echeverry, J. J., García-Carrillo, À. & Olarte Dussán, F. A. (2016). Adaptation and Validation of the Motivated Strategies for Learning Questionnaire - MSLQ - in Engineering Students in Colombia. International Journal of Engineering Education, 32(4), 1774-1787. Renumol, V., Jayaprakash, S. & Janakiram, D. (2009). Classification of cognitive difficulties of students to learn computer programming. Indian Institute of Technology, India, 12. Rodríguez-Mesa, F., Kolmos, A. & Du, X. (2017). Diversidad del PBL - Principios y modelos de apren. Aprendizaje basado en problemas en ingeniería Teoría y práctica. Rovers, S. F., Clarebout, G., Savelberg, H. H., de Bruin, A. B. & van Merriënboer, J. J. (2019). Granularity matters: comparing different ways of measuring self-regulated learning. Metacognition and Learning, 1-19. https://doi.org/10.1007/s11409-019-09188-6 Rum, S. N. & Ismail, M. (2016). Metacognitive awareness assessment and introductory computer programming course achievement at university. International Arab Journal of Information Technology, 13(6), 667-676. Sabogal, L. F., Barraza, E., Hernández, A. & Zapata, L. (2011). Validación del cuestionario de motivación y estrategias de aprendizaje forma corta-MSLQ SF, en estudiantes universitarios de una institución pública-Santa Marta. Psicogente, 14(25), 36-50. Saks, K., Leijen, Ä., Edovald, T. & Õun, K. (2015). Cross-cultural Adaptation and Psychometric Properties of the Estonian Version of MSLQ. Procedia - Social and Behavioral Sciences, 191, 597-604. https://doi.org/10.1016/j.sbspro.2015.04.278 Schellings, G. (2011). Applying learning strategy questionnaires: Problems and possibilities. Metacognition and Learning, 6(2), 91-109. https://doi.org/10.1007/s11409-011-9069-5 Schellings, G. & Van Hout-Wolters, B. (2011). Measuring strategy use with self-report instruments: theoretical and empirical considerations. Metacognition and Learning, 6(2), 83-90. https://doi.org/10.1007/s11409-011-9081-9 Schellings, G. L., Van Hout-Wolters, B. H., Veenman, M. V. & Meijer, J. (2013). Assessing metacognitive activities: The in-depth comparison of a task-specific questionnaire with think-aloud protocols. European Journal of Psychology of Education, 28(3), 963-990. https://doi.org/10.1007/s10212-012-0149-y Schraw, G. & Sperling, R. (1994). Assesing Metacognitive Awareness. Smrithi Rekha, V. & Venkatapathy, S. (2015). Understanding the usage of online forums as learning platforms. Procedia Computer Science, 46(Icict 2014), 499-506. https: / /doi.org /10.1016 /j.procs.2015.02.074 Soemantri, D., McColl, G. & Dodds, A. (2018). Measuring medical students’ reflection on their learning: Modification and validation of the motivated strategies for learning questionnaire (MSLQ). https://doi.org/10.1186/s12909-018-1384-y Soloway, E. & Ehrlich, K. (1984). Empirical Studies of Programming Knowledge. Ieee Transactions on Software Engineering, 10(5), 595-609. Suárez, O. & Mora, C. (2016). Adaptación y validación del inventario MSLQ para los cursos iniciales de física en la educación superior. Latin American Journal of Physics Education, 10(3), 1-11. Susac, A., Bubic, A., Kaponja, J., Planinic, M. & Palmovic, M. (2014). Eye movements reveals student’s strategies in simple equation solving. International Journal of Science and Mathematics Education, 555-577. Tek, F. B., Benli, K. S. & Deveci, E. (2018). Implicit Theories and Self-Efficacy in an Introductory Programming Course. IEEE Transactions on Education, 61(3), 218-225. https://doi.org/10.1109/TE.2017.2789183 Tong, F., Guo, H., Wang, Z., Min, Y., Guo, W. & Yoon, M. (2019). Examining cross-cultural transferability of self- regulated learning model: an adaptation of the Motivated Strategies for Learning Questionnaire for Chinese adult learners. https://doi.org/10.1080/03055698.2019.1590183 Tsai, C. Y. (2019). Improving students’ understanding of basic programming concepts through visual programming language: The role of self-efficacy. Computers in Human Behavior, 95(May 2018), 224-232. https://doi.org/10.1016/j.chb.2018.11.038 Ursachi, G., Horodnic, I. A. & Zait, A. (2013). How reliable are measurement scales? External factors with indirect influence on reliability estimators. 7th International Conference on Globalization and Higher Education in Economics and Business Administration GEBA. Watson, J. C. (2017). Establishing evidence for internal structure using exploratory factor analysis. https://doi.org/10.1080/07481756.2017.1336931 Weinstein, C. E. & Mayer, R. E. (1986). The teaching of learning strategies. En M. C. Wittrock (Handbooko). MacMillan. Weinstein, C Palmen, D. (1990). LASSI-HS User’s Manual, 1-32. Whittall, S. J., Prashandi, W. A. C., Himasha, G. L. S., Silva, D. I. D. & Suriyawansa, T. K. (2017). CodeMage: Educational Programming Environment For Beginners. 9th International Conference on Knowledge and Smart Technology (KST), Chonburi, 2017, pp. 311-316., 311-316. Williams, B., Onsman, A. & Brown, T. (1996). Exploratory factor analysis: A five-step guide for novices. Journl of Emergency Primary Health Care, 19(May), 42-50. https://doi.org/10.1080/09585190701763982 Winne, P. H. & Perry, N. E. (2000). Measuring Self-Regulated Learning. Handbook of Self-Regulation. Wolters, C. A. & Pintrich, P. R. (1998). Contextual differences in student motivation and self-regulated learning in mathematics, English, and social studies classrooms. (August 1995), 27-47. Zhao, W. X., Zhang, W., He, Y., Xie, X. & Wen, J. R. (2018). Automatically learning topics and difficulty levels of problems in online judge systems. https://doi.org/10.1145/3158670 Zimmerman, B. J. (1989). Models of Self-Regulated Learning and Academic Achievement. Springer Series in Cognitive Development (p. 25). Springer, New York, NY. Zimmerman, B. J. (2002). Becoming a Self-Regulated Learner: An Overview. Theory into practice, 41(2), 64-67. https://doi.org/10.1207/s15430421tip4102 Zimmerman, B. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329-339. https://doi.org/10.1037/0022-0663.81.3.329 Zimmermann, S. A., Weinstein, C. E. & Palmer, D. R. (1988). Assessing learning strategies: the design and development of the lassi. Learning and Study Strategies(pp. 25-40). ACADEMIC PRESS, INC. https://doi.org/10.1016/b978-0-12-742460-6.50009-8
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
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dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería de Sistemas e Industrial
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dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
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spelling	Atribución-NoComercial-SinDerivadas 4.0 InternacionalDerechos reservados al autor, 2021http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ramírez Echeverry, Jhon Jairo70b8e84c9e9831321c48058d547ce865Restrepo Calle, Felipe82117c6c71f31211f86863049b600db3Torres Jiménez, Stephanie79fa762252a56479f96bebce45d1238ePLaS - Programming Languages and Systems2021-08-02T17:38:23Z2021-08-02T17:38:23Z2021https://repositorio.unal.edu.co/handle/unal/79877Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, tablasEn virtud de la gran acogida que la programación de computadores ha tenido en los últimos años tanto en la academia como en la industria, una inmensa mayoría de universidades la han incluido en sus currículos de Ingeniería. Sin embargo, la complejidad que la programación representa para muchos estudiantes producen altos niveles de deserción y pérdida de las asignaturas relacionadas con este conocimiento. Diversos autores han empleado instrumentos de autoinforme para determinar los aspectos que influyen positivamente durante el proceso de aprendizaje de la programación y que a su vez permiten lograr un aprendizaje significativo. Aunque en el marco del aprendizaje autorregulado se evidencia una gran cantidad de instrumentos que caracterizan aspectos como la motivación, no se encuentra un instrumento enfocado en la caracterización de las estrategias de aprendizaje de la programación de computadores. En ese sentido, este trabajo de investigación explora e identifica diferentes estrategias de aprendizaje con el objetivo de recopilarlas en un instrumento de autoinforme, el cual se denominó Cuestionario sobre Estrategias de Aprendizaje de la Programación de Computadores - CEAPC. La construcción del CEAPC se logró gracias a los procesos de diseño y validación llevados a cabo a través de una metodología mixta compuesta por métodos cuantitativos, como el Análisis Factorial Exploratorio y el cálculo del coeficiente del α de Cronbach, y métodos cualitativos como los grupos focales y las entrevistas semi-estructuradas. Los resultados fueron positivos en cuanto a las propiedades psicométricas obtenidas para el instrumento, como la validez de constructo y los índices de confiabilidad. Cuenta por un lado, con ítems de cargas factoriales mayores a 0.3 y por otro lado con valores de α de Cronbach entre 0.6 y 0.8, los cuales son aceptables de acuerdo con la literatura. Cabe resaltar que este instrumento permitirá identificar las estrategias que influyen en un proceso de aprendizaje profundo de la programación de computadores y, además, dará la posibilidad de determinar el rol que desempeña la autorregulación en el aprendizaje en esta área en particular. Así mismo, la caracterización de las estrategias de aprendizaje autorregulado, llevada a cabo a través del instrumento propuesto, permitirá plantear modos de aprendizaje fuera y dentro del aula. (Texto tomado de la fuente)Due to the great reception of computer programming in recent years, both in academia and in industry, several universities have included it in their Engineering curricula. However, the complexity that programming represents to many students produces high dropout rates and failing grades in subjects related to this knowledge. Various authors have used self-report instruments to determine the aspects that positively influence the programming learning process and allow significant learning to be achieved. Although there is evidence of a large number of instruments in self-regulated learning that characterize aspects such as motivation, there is no instrument focused on characterizing the learning strategies of computer programming. In this sense, this research explores and identifies different learning strategies to compile them in a self-report instrument, which was called Cuestionario sobre Estrategias de Aprendizaje de la Programación de Computadores - CEAPC. The construction of the CEAPC was achieved thanks to the design and validation processes carried out through a mixed methodology composed of quantitative methods, such as Exploratory Factor Analysis and the calculation of the coefficient of Cronbach’s α, and qualitative methods such as focus groups and semi-structured interviews. The results were positive regarding the psychometric properties obtained for the instrument, such as construct validity and reliability indices. On the one hand, items had factor loadings greater than 0.3 and, on the other hand, values of Cronbach’s α range between 0.6 and 0.8, which are acceptable according to the literature. It should be noted that this instrument will allow identifying the strategies that influence a profound learning process of computer programming and, determining the role that self-regulation plays in learning in this particular area. Likewise, the characterization of the self-regulated learning strategies accomplished through the proposed instrument will allow proposing of learning modes outside and inside the classroom. (Text taken from source)MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónComputación Aplicada - Educación en Ingeniería176 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computaciónAprendizaje autorreguladoProgramación de computadoresEstrategias de aprendizajeInstrumento de autoinformepropiedad psicométricaCEAPCMSLQLASSISelf-regulated learningComputer programmingLearning strategiesSelf-report instrumentPsychometric propertyProgramación de computadoresComputer programmingMétodo de enseñanzaTeaching methodsDiseño y validación interna de un instrumento de autoinforme para caracterizar las estrategias de aprendizaje de programación de computadoresDesign and internal validation of a self-report instrument to characterize computer programming learning strategiesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAmbrosio, A. P., Almeida, L., Franco, A., Martins, S. & Georges, F. (2012). Assessment of self-regulated attitudes and behaviors of introductory programming students. Proceedings - Frontiers in Education Conference, FIE, 1-6. https://doi.org/10.1109/FIE.2012.6462314Aron, J. D. (1974). The Program Development Process, Addison-Wesley.Babakus, E. & Mangold, W. G. (1992). Adapting the SERVQUAL Scale to Hospital Services: An empirital investigation.Bandura, A. (2005). Guide for constructing self-efficacy scales. https://doi.org/10.1017/CBO9781107415324.004Begel, A. & Simon, B. (2008). Novice software developers, all over again. ICER’08 - Proceedings of the ACM Workshop on International Computing Education Research, 1(425), 3-14. https://doi.org/10.1145/1404520.1404522Berger, J.-L. & Karabenick, S. A. (2011). Motivation and students’ use of learning strategies: Evidence of unidirectional effects in mathematics classrooms. Learning and Instruction, 416-428.Bergin, S., Reilly, R. & Traynor, D. (2005). Examining the role of self-regulated learning on introductory programming performance. First International Workshop on Computing Education Research, 81-86. https://doi.org/10.1145/1089786.1089794Biggs, J. B. (1987). Study Process Questionnaire Manual. https://files.eric.ed.gov/fulltext/ED308200.pdfBishop-Clark, C. (1995). Cognitive style, personality, and computer programming. Computers in Human Behavior, 11(2), 241-260. https://doi.org/10.1016/0747-5632(94)00034-FBoekaerts, M. (1996). Self-regulated Learning at the Junction of Cognition and Motivation.Boekaerts, M. (1999). Motivated learning: Studying student * situation transactional units. https://doi.org/10.1007/BF03173110Bryman, A. (2012). Social Reseach Methods (4th, Vol. 66). Oxford University Press.Cárdenas, S. F. (2013). Los constructos en las investigaciones pedagógicas: cuantificación y tratamiento estadístico. Revista Científico Pedagógica, 24(23).Carretero-Dios, H. & Pérez, C. (2005). Normas para el desarrollo y revisión de estudios instrumentales. International Journal of Clinical and Health Psychology, 5(3), 521-551.Caruso, T., Hill, N., Van DeGrift, T. & Simon, B. (2011). Experience report: Getting novice programmers to THINK about improving their software development process. SIGCSE’11 - Proceedings of the 42nd ACM Technical Symposium on Computer Science Education, 493-498. https://doi.org/10. 1145/1953163.1953307Castellanos, H., Restrepo-Calle, F., González, F. A. & Echeverry, J. J. R. (2017). Understanding the relationships between self-regulated learning and students source code in a computer programming course. Proceedings - Frontiers in Education Conference, FIE, 2017-Octob, 1-9. https://doi.org/10.1109/FIE.2017.8190467Cetin, I. & Ozden, M. Y. (2015). Development of computer programming attitude scale for university students. Computer Applications in Engineering Education, 23(5), 667-672. https://doi.org/10.1002/cae.21639Chen, C. & Whitesel, J. (2012). The Validity and Reliability Study of a Revised Motivated Strategy for Learning Questionnaire (MSLQ) for Assessing Computer Software Learning Strategies. International Journal of E-Adoption, 4(2), 28-51. https://doi.org/10.4018/jea.2012040103Cheng, G., Poon, L. K., Lau, W. W. & Zhou, R. C. (2019). Applying eye tracking to identify students’ use of learning strategies in understanding program code. ACM International Conference Proceeding Series, 140-144. https://doi.org/10.1145/3345120.3345144Chyung, Y. S., Moll, A. J. & Berg, S. A. (2010). The Role of Intrinsic Goal Orientation, Self-Efficacy, and E-Learning Practice in Engineering Education.Cicchinelli, A., Veas, E., Pardo, A., Pammer-Schindler, V., Fessl, A., Barreiros, C. & Lindstädt, S. (2018). Finding traces of self-regulated learning in activity streams. Proceedings of the 8th International Conference on Learning Analytics and Knowledge - LAK ’18, 191-200. https://doi.org/10.1145/3170358.3170381Clement, C. A., Kurland, D. M., Mawby, R. & Pea, R. D. (1986). Analogical Reasoning and Computer Programming. Journal of Educational Computing Research, 2(4), 473-486. https://doi.org/10.2190/dfh5-e0pg-1ml4-m34jCreswell, J. W. (2009). Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage Publications.Croasmun, J. T. . & Ostrom, L. (2011). Using Likert-type scales in the social sciences.Curione, K. & Huertas, J. A. (2017). MSLQ-UY , validation with Uruguayan university students. 17(2),1-17.Danielsiek, H., Toma, L. & Vahrenhold, J. (2017). An instrument to assess self-Efficacy in introductory algorithms courses. ACM International Computing Education Research, 9(1), 56-65. https://doi.org/10.1145/3105726.3106171Dawes, J. (2008). Do data characteristics change according to the number of scale points used? An experiment using 5-point, 7-point and 10- point scales. International Journal of Market Research, 61-104.Decker, A. & McGill, M. M. (2019). A topical review of evaluation instruments for computing education. SIGCSE 2019 - Proceedings of the 50th ACM Technical Symposium on Computer Science Education, 558-564. https://doi.org/10.1145/3287324.3287393Demir, O. & Seferoglu, S. S. (2020). A Comparison of Solo and Pair Programming in Terms of Flow Experience, Coding Quality, and Coding Achievement. Journal of Educational Computing Research, 1-19.Díaz-Bravo, L., Torruco-García, U., Martínez-Hernández, M. & Varela-Ruiz, M. (2013). La Entrevista, Recurso Flexible y Dinámico. Investigación en educación médica, 2(7), 162-167.Dorn, B. & Elliott Tew, A. (2015). Empirical validation and application of the computing attitudes survey. Computer Science Education, 25(1), 1-36.Ebomoyi, J. I. (2020). Metacognition and Peer Learning Strategies as Predictors in Problem-Solving Performance in Microbiology. 21(1).Eltegani, N. & Butgereit, L. (2015). Attributes of students engagement in fundamental programming learning. Proceedings - 2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering, ICCNEEE 2015, 101-106. https://doi.org/10.1109/ICCNEEE.2015.7381438Entwistle, N. & McCune, V. (2004). The conceptual bases of study strategy inventories. Educational Psychology Review, 16(4), 325-345. https://doi.org/10.1007/s10648-004-0003-0Escobar-Avila, J., Venuti, D., Di Penta, M. & Haiduc, S. (2019). A survey on online learning preferences for computer science and programming. Proceedings - 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering Education and Training, ICSE-SEET 2019, 41, 170-181. https://doi.org/10.1109/ICSE-SEET.2019.00026Falkner, K., Vivian, R. & Falkner, N. J. (2014). Identifying computer science self-regulated learning strategies. ITICSE 2014 - Proceedings of the 2014 Innovation and Technology in Computer Science Education Conference, 291-296. https://doi.org/10.1145/2591708.2591715Ferrando, P. J. & Anguiano-Carrasco, C. (2010). El análisis factorial como técnica de investigación en psicología. Papeles del Psicólogo, 31, 28-33.Fuller, U., Johnson, C. G., Ahoniemi, T., Cukierman, D., Hernán-Losada, I., Jackova, J., Lahtinen, E., Lewis, T. L., Thompson, D. M., Riedesel, C. & Thompson, E. (2007). Developing a computer science-specific learning taxonomy. ACM SIGCSE Bulletin, 39(4), 152-170. https://doi.org/10.1145/1345375.1345438Gallego-Romero, J. M., Alario-Hoyos, C., Estévez-Ayres, I. & Delgado Kloos, C. (2020). Analyzing learners’ engagement and behavior in MOOCs on programming with the Codeboard IDE. https://doi.org/10.1007/s11423-020-09773-6Garcia, R., Falkner, K. & Vivian, R. (2018). Systematic literature review: Self-Regulated Learning strategies using e-learning tools for Computer Science. Computers and Education, 123, 150-163. https://doi.org/10.1016/j.compedu.2018.05.006García, T. & McKeachie, W. (2006). The Making of the Motivated Strategies for Learning Questionnaire. Journal of the American Mathematical Society, 19(3), 717-736. https://doi.org/10.1090/S0894-0347-05-00517-5González-Torres, M.-C. & Torrano, F. (2012). Methods and instruments for measuring self-regulated learning. En A. Valle y J. C. Nunez (Eds.), Handbook of Instructional Resources & Applications. Nova Science Publishers, Inc.Hamui-Sutton, A. & Varela-Ruiz, M. (2013). La técnica de grupos focales. Investigación en Educación Médica, 2(5), 55-60. https://doi.org/10.1016/s2007-5057(13)72683-8Hulin, C., Netemeyer, R. & Cudeck, R. (2001). Can a Reliability Coefficient Be Too High?Inzunza, B., Pérez, C., Márquez, C., Ortiz, L., Marcellini, S. & Duk, S. (2016). Estructura Factorial y Confiabilidad del Cuestionario de Motivación y Estrategias de Aprendizaje, MSLQ, en Estudiantes Universitarios Chilenos de Primer Año. https://doi.org/10.21865/RIDEP47.2.02Jackson, C. R. (2018). Validating and Adapting the Motivated Strategies for Learning Questionnaire (MSLQ) for STEM Courses at an HBCU. AERA Open, 4, 1-16. https://doi.org/10.1177/2332858418809346Jacobse, A. E. & Harskamp, E. G. (2012). Towards efficient measurement of metacognition in mathematical problem solving. Metacognition and Learning, 7(2), 133-149. https://doi.org/10.1007/s11409-012-9088-xJuarez-Ramirez, R., Navarro, C. X., Tapia-Ibarra, V., Macias-Olvera, R. & Guerra-Garcia, C. (2019). What is Programming? Putting all Together - A Set of Skills Required. 2018 6th International Conference in Software Engineering Research and Innovation (CONISOFT), 11-20. https://doi.org/10.1109/conisoft.2018.8645956Konecki, M. (2014). Problems in Programming Education and Means of Their Improvement. https://doi.org/10.2507/daaam.scibook.2014.37Korkmaz, Ö., Çakir, R. & Özden, M. Y. (2017). A validity and reliability study of the computational thinking scales (CTS). Computers in Human Behavior, 72, 558-569. https://doi.org/10.1016/j.chb.2017.01.005Kumar, V., Winne, P., Hadwin, A., Nesbit, J., Jamieson-Noel, D., Calvert, T. & Samin, B. (2005). Effects of self-regulated learning in programming. Fifth IEEE International Conference on Advanced Learning Technologies (ICALT’05), 383-387. https://doi.org/10.1109/ICALT.2005.131Linhorst, D. M. (2002). A Review of the Use and Potential of Focus Groups in Social Work Research. Qualitative Social Work, 1(2), 208-228.Loksa, D. & Ko, A. J. (2016). The Role of Self-Regulation in Programming Problem Solving Process and Success. Proceedings of the 2016 ACM Conference on International Computing Education Research -ICER ’16, 83-91. https://doi.org/10.1145/2960310.2960334Loksa, D., Ko, A. J., Jernigan, W., Oleson, A., Mendez, C. J. & Burnett, M. M. (2016). Programming, Problem Solving, and Self-Awareness: Effects of Explicit Guidance. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 1449-1461. https://doi.org/10.1145/2858036.2858252McDougall, A., Boyle, M., Pellas, N. & Peroutseas, E. (2016). Student Strategies for Learning Computer Programming: Implications for Pedagogy in Informatics. Journal of Educational Computing Research, 54(2), 109-116. https://doi.org/10.1023/B:EAIT.0000027924.69726.b5McGill, T. J. & Volet, S. E. (1997). A conceptual framework for analyzing students’ knowledge of programming. Journal of Research on Computing in Education, 29(3), 276-297. https://doi.org/10.1080/08886504.1997.10782199Méndez Martínez, C. & Sepúlveda Rondón, M. A. (2012). Introducción al análisis factorial exploratorio. Revista Colombiana de Psiquiatría, 41(1), 197-207. https://doi.org/10.1016/s0034-7450(14)60077-9Muñiz, J., Elosua, P. & Hambleton, R. (1975). International Test Commission Guidelines for Test Translation and Adaptation. https://doi.org/10.1111/j.1464-0597.1975.tb00322.xNassar-McMillan, S. C., Wyer, M., Oliver-Hoyo, M. & Ryder-Burge, A. (2010). Using Focus Groups in Preliminary Instrument Development: Expected and Unexpected Lessons Learned Sylvia. The Qualitative Report, 15(6), 1621-1634.Nausheen, M. (2016). An Adaptation of the Motivated Strategies for Learning Questionnaire (MSLQ) for Postgraduate Students in Pakistan: Results of an Exploratory Factor Analysis An Adaptation of the MSLQ for PGS in Pakistan: Results of an Exploratory Factor Analysis. Bulletin of Education and Research, 38(1), 1-16.Niemczyk, M. C. & Savenye, W. C. (2001). The Relationship of student motivation and self-regulated learning strategies to performance in an undergraduate computer literacy course. Papers Presented at the National Convention of the Association for Educational Communications and Technology, 311-322.Osterlind, S. J. (1991). Constructing Test Items: Multiple-Choice, Constructed-Response, Performance, and Other Formats (Vol. 14). https://doi.org/10.1016/0149-7189(91)90058-oOstroff, C. & Kozlowski, S. W. (1992). Organizational Socialization As a Learning Process: the Role of Information Acquisition. https://doi.org/10.1111/j.1744-6570.1992.tb00971.xOtt, C., Robins, A., Haden, P. & Shephard, K. (2015). Illustrating performance indicators and course characteristics to support students’ self-regulated learning in CS1. Computer Science Education, 25(2), 174-198. https://doi.org/10.1080/08993408.2015.1033129Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. https://doi.org/10.3389/fpsyg.2017.00422Park, H., Kim, K., Robertson, C. & Kim, D. (2019). The Effect of Memorization on the Retention and Learning Acquisition of Programming Practice. Journal of Strategic Innovation and Sustainability, 14(2), 129-135. https://doi.org/10.33423/jsis.v14i2.1378Pedrosa, D., Cravino, J., Morgado, L. & Barreira, C. (2016). Self-regulated learning in computer programming: Strategies students adopted during an assignment. Communications in Computer and Information Science, 621, 87-101. https://doi.org/10.1007/978-3-319-41769-1_7Pedrosa, D., Cravino, J., Morgado, L. & Barreira, C. (2017). Self-regulated learning in higher education: strategies adopted by computer programming students when supported by the SimProgramming approach. Production, 27(spe), 1-15. https://doi.org/10.1590/0103-6513.225516Pett, M. & Lackey, N. (2003). Making Sense of Factor Analysis (Vol. 50). https://doi.org/10.4135/9781412984898Pintrich, P. R. & De Groot, E. V. (1990). Motivational and Self-Regulated Learning Components of Classroom Academic Performance. Anzeiger für Schädlingskunde, 3(2), 13-15. https://doi.org/10.1007/BF02338175Pintrich, P. R., Smith, D. A. F., Garcia, T. & Mckeachie, W. J. (1991). A Manual for the Use of the Learning Questionnaire Motivated Strategies for (MSLQ). https://doi.org/10.5901/mjss.2015.v6n1p156Pintrich, P. R., Smith, D. A. F., Garcia, T. & Mckeachie, W. J. (1993). Reliability and Predictive Validity of the Motivated Strategies for Learning Questionnaire (Mslq). Educational and Psychological Measurement, 53(3), 801-813. https://doi.org/10.1177/0013164493053003024Porter, R. & Calder, P. (2004). Patterns in learning to program: an experiment? http://dl.acm.org/citation.cfm?id=980000Prather, J., Pettit, R., Becker, B. A., Denny, P., Loksa, D., Peters, A., Albrecht, Z. & Masci, K. (2019). First Things First: Providing Metacognitive Scaffolding for Interpreting Problem Prompts. Proceedings of the 50th ACM Technical Symposium on Computer Science Education - SIGCSE ’19, 531-537. https://doi.org/10.1145/3287324.3287374Rafique, W., Dou, W., Hussain, K. & Ahmed, K. (2020). Factors influencing programming expertise in a web-based e-learning paradigm. Online Learning Journal, 24(1), 162-181. https://doi.org/10. 24059/olj.v24i1.1956Ramalingam, V. & Wiedenbeck, S. (2005). Development and Validation of Scores on a Computer Programming Self-Efficacy Scale and Group Analyses of Novice Programmer Self-Efficacy. Journal of Educational Computing Research, 19(4), 367-381. https://doi.org/10.2190/c670-y3c8-ltj1-ct3pRamírez, M. d. C., Canto, J. E., Bueno, J. A. & Echazarreta, A. (2013). Validación Psicométrica del Motivated Strategies for Learning Questionnaire en Universitarios Mexicanos. Electronic Journal of Research in Educational Psychology, 11(1).Ramírez Echeverry, J. J., Rosales Castro, L. F., Restrepo Calle, F. & González, F. A. (2018). Self-regulated learning in a computer programming course. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 13(2), 75-83. https://doi.org/10.1109/RITA.2018.2831758Ramírez-Echeverry, J. J., García-Carrillo, À. & Olarte Dussán, F. A. (2016). Adaptation and Validation of the Motivated Strategies for Learning Questionnaire - MSLQ - in Engineering Students in Colombia. International Journal of Engineering Education, 32(4), 1774-1787.Renumol, V., Jayaprakash, S. & Janakiram, D. (2009). Classification of cognitive difficulties of students to learn computer programming. Indian Institute of Technology, India, 12.Rodríguez-Mesa, F., Kolmos, A. & Du, X. (2017). Diversidad del PBL - Principios y modelos de apren. Aprendizaje basado en problemas en ingeniería Teoría y práctica.Rovers, S. F., Clarebout, G., Savelberg, H. H., de Bruin, A. B. & van Merriënboer, J. J. (2019). Granularity matters: comparing different ways of measuring self-regulated learning. Metacognition and Learning, 1-19. https://doi.org/10.1007/s11409-019-09188-6Rum, S. N. & Ismail, M. (2016). Metacognitive awareness assessment and introductory computer programming course achievement at university. International Arab Journal of Information Technology, 13(6), 667-676.Sabogal, L. F., Barraza, E., Hernández, A. & Zapata, L. (2011). Validación del cuestionario de motivación y estrategias de aprendizaje forma corta-MSLQ SF, en estudiantes universitarios de una institución pública-Santa Marta. Psicogente, 14(25), 36-50.Saks, K., Leijen, Ä., Edovald, T. & Õun, K. (2015). Cross-cultural Adaptation and Psychometric Properties of the Estonian Version of MSLQ. Procedia - Social and Behavioral Sciences, 191, 597-604. https://doi.org/10.1016/j.sbspro.2015.04.278Schellings, G. (2011). Applying learning strategy questionnaires: Problems and possibilities. Metacognition and Learning, 6(2), 91-109. https://doi.org/10.1007/s11409-011-9069-5Schellings, G. & Van Hout-Wolters, B. (2011). Measuring strategy use with self-report instruments: theoretical and empirical considerations. Metacognition and Learning, 6(2), 83-90. https://doi.org/10.1007/s11409-011-9081-9Schellings, G. L., Van Hout-Wolters, B. H., Veenman, M. V. & Meijer, J. (2013). Assessing metacognitive activities: The in-depth comparison of a task-specific questionnaire with think-aloud protocols. European Journal of Psychology of Education, 28(3), 963-990. https://doi.org/10.1007/s10212-012-0149-ySchraw, G. & Sperling, R. (1994). Assesing Metacognitive Awareness.Smrithi Rekha, V. & Venkatapathy, S. (2015). Understanding the usage of online forums as learning platforms. Procedia Computer Science, 46(Icict 2014), 499-506. https: / /doi.org /10.1016 /j.procs.2015.02.074Soemantri, D., McColl, G. & Dodds, A. (2018). Measuring medical students’ reflection on their learning: Modification and validation of the motivated strategies for learning questionnaire (MSLQ). https://doi.org/10.1186/s12909-018-1384-ySoloway, E. & Ehrlich, K. (1984). Empirical Studies of Programming Knowledge. Ieee Transactions on Software Engineering, 10(5), 595-609.Suárez, O. & Mora, C. (2016). Adaptación y validación del inventario MSLQ para los cursos iniciales de física en la educación superior. Latin American Journal of Physics Education, 10(3), 1-11.Susac, A., Bubic, A., Kaponja, J., Planinic, M. & Palmovic, M. (2014). Eye movements reveals student’s strategies in simple equation solving. International Journal of Science and Mathematics Education, 555-577.Tek, F. B., Benli, K. S. & Deveci, E. (2018). Implicit Theories and Self-Efficacy in an Introductory Programming Course. IEEE Transactions on Education, 61(3), 218-225. https://doi.org/10.1109/TE.2017.2789183Tong, F., Guo, H., Wang, Z., Min, Y., Guo, W. & Yoon, M. (2019). Examining cross-cultural transferability of self- regulated learning model: an adaptation of the Motivated Strategies for Learning Questionnaire for Chinese adult learners. https://doi.org/10.1080/03055698.2019.1590183Tsai, C. Y. (2019). Improving students’ understanding of basic programming concepts through visual programming language: The role of self-efficacy. Computers in Human Behavior, 95(May 2018), 224-232. https://doi.org/10.1016/j.chb.2018.11.038Ursachi, G., Horodnic, I. A. & Zait, A. (2013). How reliable are measurement scales? External factors with indirect influence on reliability estimators. 7th International Conference on Globalization and Higher Education in Economics and Business Administration GEBA.Watson, J. C. (2017). Establishing evidence for internal structure using exploratory factor analysis. https://doi.org/10.1080/07481756.2017.1336931Weinstein, C. E. & Mayer, R. E. (1986). The teaching of learning strategies. En M. C. Wittrock (Handbooko). MacMillan.Weinstein, C Palmen, D. (1990). LASSI-HS User’s Manual, 1-32.Whittall, S. J., Prashandi, W. A. C., Himasha, G. L. S., Silva, D. I. D. & Suriyawansa, T. K. (2017). CodeMage: Educational Programming Environment For Beginners. 9th International Conference on Knowledge and Smart Technology (KST), Chonburi, 2017, pp. 311-316., 311-316.Williams, B., Onsman, A. & Brown, T. (1996). Exploratory factor analysis: A five-step guide for novices. Journl of Emergency Primary Health Care, 19(May), 42-50. https://doi.org/10.1080/09585190701763982Winne, P. H. & Perry, N. E. (2000). Measuring Self-Regulated Learning. Handbook of Self-Regulation.Wolters, C. A. & Pintrich, P. R. (1998). Contextual differences in student motivation and self-regulated learning in mathematics, English, and social studies classrooms. (August 1995), 27-47.Zhao, W. X., Zhang, W., He, Y., Xie, X. & Wen, J. R. (2018). Automatically learning topics and difficulty levels of problems in online judge systems. https://doi.org/10.1145/3158670Zimmerman, B. J. (1989). Models of Self-Regulated Learning and Academic Achievement. Springer Series in Cognitive Development (p. 25). Springer, New York, NY.Zimmerman, B. J. (2002). Becoming a Self-Regulated Learner: An Overview. Theory into practice, 41(2), 64-67. https://doi.org/10.1207/s15430421tip4102Zimmerman, B. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329-339. https://doi.org/10.1037/0022-0663.81.3.329Zimmermann, S. A., Weinstein, C. E. & Palmer, D. R. (1988). Assessing learning strategies: the design and development of the lassi. Learning and Study Strategies(pp. 25-40). 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