Artificial Intelligence and Computer-Supported Collaborative Learning in Programming: A Systematic Mapping Study

dc.creatorHidalgo Suarez, Carlos Giovanny
dc.creatorBucheli-Guerrero, Víctor Andrés
dc.creatorOrdóñez-Eraso, Hugo Armando
dc.date2023-01-01
dc.date.accessioned2024-08-22T00:14:23Z
dc.date.available2024-08-22T00:14:23Z
dc.descriptionObjective: The Computer-Supported Collaborative Learning (CSCL) approach integrates artificial intelligence (AI) to enhance the learning process through collaboration and information and communication technologies (ICTs). In this sense, innovative and effective strategies could be designed for learning computer programming. This paper presents a systematic mapping study from 2009 to 2021, which shows how the integration of CSCL and AI supports the learning process in programming courses. Methodology: This study was conducted by reviewing data from different bibliographic sources such as Scopus, Web of Science (WoS), ScienceDirect, and repositories of the GitHub platform. It employs a quantitative methodological approach, where the results are represented through technological maps that show the following aspects: i) the programming languages used for CSCL and AI software development; ii) CSCL software technology and the evolution of AI; and iii) the ACM classifications, research topics, artificial intelligence techniques, and CSCL strategies. Results: The results of this research help to understand the benefits and challenges of using the CSCL and AI approach for learning computer programming, identifying some strategies and tools to improve the process in programming courses (e.g., the implementation of the CSCL approach strategies used to form groups, others to evaluate, and others to provide feedback); as well as to control the process and measure student results, using virtual judges for automatic code evaluation, profile identification, code analysis, teacher simulation, active learning activities, and interactive environments, among others. However, for each process, there are still open research questions. Conclusions: This work discusses the integration of CSCL and AI to enhance learning in programming courses and how it supports students' education process. No model integrates the CSCL approach with AI techniques, which allows implementing learning activities and, at the same time, observing and analyzing the evolution of the system and how its users (students) improve their learning skills with regard to programming. In addition, the different tools found in this paper could be explored by professors and institutions, or new technologies could be developed from them.en-US
dc.descriptionObjetivo: El enfoque de aprendizaje colaborativo asistido por computadora (CSCL) integra la inteligencia artificial (IA) para mejorar el proceso de aprendizaje a través de la colaboración y las tecnologías de la información y la comunicación (TICs). En este sentido, se podrían diseñar estrategias innovadoras y efectivas para el aprendizaje de la programación de computadoras. Este artículo presenta un estudio sistemático de mapeo de los años 2009 a 2021, el cual muestra cómo la integración del CSCL y la IA apoya el proceso de aprendizaje en cursos de programación. Metodología: Este estudio se realizó mediante una revisión de datos proveniente de distintas fuentes bibliográficas como Scopus, Web of Science (WoS), ScienceDirect y repositorios de la plataforma GitHub. El trabajo emplea un enfoque metodológico cuantitativo, en el cual los resultados se representan a través de mapas tecnológicos que muestran los siguientes aspectos: i) los lenguajes de programación utilizados para el desarrollo de software de CSCL e IA; ii) la tecnología de software CSCL y la evolución de la IA; y iii) las clasificaciones, los temas de investigación, las técnicas de inteligencia artificial y las estrategias de CSCL de la ACM. Resultados: Los resultados de esta investigación ayudan a entender los beneficios y retos de usar el enfoque de CSCL e IA para el aprendizaje de la programación de computadoras, identificando algunas estrategias y herramientas para mejorar el proceso en cursos de programación (e.g., La implementación de estrategias del enfoque CSCL utilizadas para formar grupos, de otras para evaluar y de otras para brindar retroalimentación); así como para monitorear el proceso y medir los resultados de los estudiantes utilizando jueces virtuales para la evaluación automática del código, identificación de perfiles, análisis de código, simulación de profesores, actividades de aprendizaje activo y entornos interactivos, entre otros. Sin embargo, aún hay preguntas investigación por resolver para cada proceso. Conclusiones: Este trabajo discute la integración del CSCL y la IA para mejorar el aprendizaje en cursos de programación y cómo esta apoya el proceso educativo de los estudiantes. Ningún modelo integra el enfoque CSCL con técnicas de IA, lo cual permite implementar actividades de aprendizaje y, al mismo tiempo, observar y analizar la evolución del sistema y de la manera en que sus usuarios (estudiantes) mejoran sus habilidades de aprendizaje con respecto a la programación. Adicionalmente, las diferentes herramientas encontradas en este artículo podrían ser exploradas por profesores e instituciones, o podrían desarrollarse nuevas tecnologías a partir de ellas.es-ES
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dc.identifierhttps://revistas.udistrital.edu.co/index.php/Tecnura/article/view/19637
dc.identifier.urihttps://ciencianacional.co/handle/123456789/202419
dc.journalTecnura
dc.languagespa
dc.publisherUniversidad Distrital Francisco José de Caldas. Colombiaes-ES
dc.relationhttps://revistas.udistrital.edu.co/index.php/Tecnura/article/view/19637/18636
dc.relationhttps://revistas.udistrital.edu.co/index.php/Tecnura/article/view/19637/18935
dc.relation/*ref*/Abirami, A. M., & Kiruthiga, P. (2018). Collaborative learning tools for data structures. Journal of Engineering Education Transformations, 31(3), 79-83. https://doi.org/10.16920/jeet/2018/v31i3/120763
dc.relation/*ref*/Abdulwahhab, R. S., & Abdulwahab, S. S. (2017). Integrating learning analytics to predict student performance behavior [Conference presentation]. 2017 6th International Conference on Information and Communication Technology and Accessibility (ICTA), Muscat (pp. 1–6). IEEE. https://doi.org/10.1109/ICTA.2017.8336060
dc.relation/*ref*/ACM (n.d.). CCS 2012. https://dl.acm.org/ccs/ccs.cfm
dc.relation/*ref*/Aglio (2016). Judge0 ap. https://api.judge0.com/
dc.relation/*ref*/Agredo-Delgado, V., Ruiz, P. H., Collazos, C. A., Alghazzawi, D. M., & Fardoun, H. M. (2018). Towards a framework definition to increase collaboration and achieve group cognition. In P. Zaphiris & A. Ioannou (Eds.), Learning and Collaboration Technologies: Design, Development, and Technological Innovation (pp. 337-349). Springer. https://doi.org/10.1007/978-3-319-91743-6_26
dc.relation/*ref*/Antonenko, P. D., Toy, S., & Niederhauser, D. S. (2012). Using cluster analysis for data mining in educational technology research. Educational Technology Research and Development, 60(3), 383-398. https://doi.org/10.1007/s11423-012-9235-8
dc.relation/*ref*/Asproth, V., Nyström, C. A., Olsson, H., & Oberg, L.-M. (2011). Team syntegrity in a triple loop learning model for course development. Issues in Information Science and Information Technology, 8, 1-11. https://doi.org/10.28945/1400
dc.relation/*ref*/Bandrowski, A., Brush, M., Grethe, J. S., Haendel, M. A., Kennedy, D. N., Hill, S., Hof, P. R., Martone, M. E., Pol, M., Tan, S. C., Washington, N., Zudilova-Seinstra, E., & Vasilevsky, N. (2016). The resource identification initiative: A cultural shift in publishing. Journal of Comparative Neurology, 524(1), 8-22. https://doi.org/10.1002/cne.23913
dc.relation/*ref*/Barab, S. A., Bowdish, B. E., & Lawless, K. A. (1997). Hypermedia navigation: Profiles of hypermedia users. Educational Technology Research and Development, 45(3), 23-41. https://doi.org/10.1007/BF02299727
dc.relation/*ref*/Bennedsen, J., Caspersen, M. E., & Kolling, M. (Eds.) (2008). Reflections on the teaching of programming. Springer. http://link.springer.com/10.1007/978-3-540-77934-6
dc.relation/*ref*/Bevan, J., Werner, L., & McDowell, C. (2002). Guidelines for the use of pair programming in a freshman programming class. In IEEE (Eds.), Proceedings of the 15th Conference on Software Engineering Education and Training (CSEE&T 2002) (pp. 100-107). IEEE. https://doi.org/10.1109/CSEE.2002.995202
dc.relation/*ref*/Black, P., & Wiliam, D. (1998). Assessment and classroom learning. International Journal of Phytoremediation, 21(1), 7-74. https://doi.org/10.1080/0969595980050102
dc.relation/*ref*/Blank, D., Kay, J. S., Marshall, J. B., O’Hara, K., & Russo, M. (2012). Calico: A multi-programming-language, multi-context framework designed for computer science education. In ACM (Eds.), Proceedings of the 43rd ACM Technical Symposium on Computer Science Education (pp. 63-68). https://doi.org/10.1145/2157136.2157158
dc.relation/*ref*/Bratitsis, T., & Demetriadis, S. (2012). Perspectives on tools for computer-supported collaborative learning. International Journal of e-Collaboration, 8(4), 73653. https://doi.org/10.4018/jec.2012100101
dc.relation/*ref*/Bravo, C., Marcelino, M. J., Gomes, A., Esteves, M., & Mendes, A. J. (2005). Integrating educational tools for collaborative. Journal of Universal Computer Science, 11(9), 1505-1517. https://lib.jucs.org/article/28475/
dc.relation/*ref*/Burch, C. (2009). Jigsaw, a programming environment for java in CS1. Journal of Computing Sciences in Colleges, 24(5), 37-43. https://dl.acm.org/doi/10.5555/1516595.1516604
dc.relation/*ref*/Capelo, C., & Dias, J. F. (2009). A feedback learning and mental models perspective on strategic decision making. Educational Technology Research and Development, 57(5), 629-644. https://doi.org/10.1007/s11423-009-9123-z
dc.relation/*ref*/Casamayor, A., Amandi, A., & Campo, M. (2009). Intelligent assistance for teachers in collaborative e-learning environments. Computers and Education, 53(4), 1147-1154. https://doi.org/10.1016/j.compedu.2009.05.025
dc.relation/*ref*/chamilo (n.d.). chamilo-lms: Chamilo is a learning management system focused on ease of use and accessibility. https://github.com/chamilo/chamilo-lms
dc.relation/*ref*/Cheek, J. (2019). JoshCheek/ruby-kickstart. https://github.com/JoshCheek/ruby-kickstart
dc.relation/*ref*/Choi, S., Park, H., Kang, D., Lee, J. Y., & Kim, K. (2012). An SAO-based text mining approach to building a technology tree for technology planning. Expert Systems with Applications, 39(13), 11443-11455. https://doi.org/10.1016/j.eswa.2012.04.014
dc.relation/*ref*/codebuddies (n.d.). codebuddies/codebuddies: CodeBuddies.org: Community-organized hangouts for learning programming together – community-built using MeteorJS. https://github.com/codebuddies/
dc.relation/*ref*/Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Computers in Human Behavior, 73(C), 247-256. https://doi.org/10.1016/j.chb.2017.01.047
dc.relation/*ref*/Costaguta, R., & de los Angeles Menini, M. (2014). An assistant agent for group formation in CSCL based on student learning styles. In ACM (Eds.), EATIS '14: Proceedings of the 7th Euro American Conference on Telematics and Information Systems (art. 24). ACM. https://doi.org/10.1145/2590651.2590674
dc.relation/*ref*/Coursera (2014). An introduction to interactive programming in Python (part 1). https://www.coursera.org/learn/interactive-python-1
dc.relation/*ref*/cqlzx (2017). Collaborative online judger. https://github.com/cqlzx/collaborative-online-judger
dc.relation/*ref*/D3 (n.d.). d3 fishbone. http://bl.ocks.org/bollwyvl/9239214
dc.relation/*ref*/Damasevicius, R. (2009). Analysis of academic results for informatics course improvement using association rule mining. In G. A Papadopoulos, W. Wojtkowski, G. Wojtkowski, S. Wrycza, & J. Zupancic (Eds.), Information Systems Development (pp. 357–363). Springer. https://doi.org/10.1007/b137171_37
dc.relation/*ref*/Debdi, O., Paredes-Velasco, M., & Velázquez-Iturbide, J. A. (2015). GreedExCol, A CSCL tool for experimenting with greedy algorithms. Computer Applications in Engineering Education, 23(5), 790-804. https://doi.org/10.1002/cae.21655
dc.relation/*ref*/Desmarais, M. C., & Baker, R. S. (2012). A review of recent advances in learner and skill modeling in intelligent learning environments. User Modeling and User-Adapted Interaction, 22, 9-38. https://doi.org/10.1007/s11257-011-9106-8
dc.relation/*ref*/django (n.d.). The Web framework for perfectionists with deadlines. https://www.djangoproject.com/
dc.relation/*ref*/dmlc (2019, November). minerva. Distributed (Deep) Machine Learning Community. https://github.com/dmlc/minerva
dc.relation/*ref*/Docq, F., & Daele, A. (2001). Uses of ICT tools for CSCL: How do students make as their’s own the designed environment? https://dial.uclouvain.be/pr/boreal/object/boreal:75948
dc.relation/*ref*/Drupal (2014). letscode. http://www.letscode.com/
dc.relation/*ref*/Echeverría, L., Cobos, R., Machuca, L., & Claros, I. (2017). Using collaborative learning scenarios to teach programming to non-CS majors. Computer Applications in Engineering Education, 25(5), 719-731. https://doi.org/10.1002/cae.21832
dc.relation/*ref*/Edgarjcfn (2014). Weblet importer. http://edgarjcfn.github.io/pylearn/#level01
dc.relation/*ref*/Entropy-xcy. (2017). Rankface. https://github.com/Entropy-xcy/RankFace
dc.relation/*ref*/EpistasisLab (n.d.). tpot: A Python automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. https://github.com/EpistasisLab/tpot
dc.relation/*ref*/Fadde, P. J. (2009). Instructional design for advanced learners: Training recognition skills to hasten expertise. Educational Technology Research and Development, 57(3), 359-376. https://doi.org/10.1007/s11423-007-9046-5
dc.relation/*ref*/Figueiredo, J., & García-Peñalvo, F. J. (2018). Building skills in introductory programming. In F. J. García-Peñalvo (Ed.) Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality – TEEM’18 (pp. 46-50). ACM. https://doi.org/10.1145/3284179.3284190
dc.relation/*ref*/GitHub (2018). GitHub Octoverse. https://octoverse.github.com/
dc.relation/*ref*/Google Inc. (n.d.). Treemaps | Charts. Retrieved 2019-10-17, from https://developers.google.com/chart/interactive/docs/gallery/treemap
dc.relation/*ref*/Gutwin, C., Ochoa, S. F., Vassileva, J., & Inoue, T. (Eds.). (2013). Collaboration and technology (vol. 8224). Springer. http://link.springer.com/10.1007/978-3-319-63874-4
dc.relation/*ref*/Haghighatlari, M., Vishwakarma, G., Altarawy, D., Subramanian, R., Kota, B. U., Sonpal, A., & Hachmann, J. (2020). ChemML: A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data. WIREs, Computational Molecular Science, 10(4), e1458. https://doi.org/10.1002/wcms.1458
dc.relation/*ref*/Hazzan, O., & Dubinsky, Y. (2003). Teaching a software development methodology: The case of extreme programming. In IEEE (Eds.), Proceedings 16th Conference on Software Engineering Education and Training, 2003. (CSEE&T 2003) (pp. 176-184). IEEE. https://doi.org/10.1109/CSEE.2003.1191375
dc.relation/*ref*/hnshhslsh (2016). virtual-judge. https://github.com/hnshhslsh/virtual-judge integeruser (n.d.). jgltut: Learning modern 3D graphics programming with LWJGL 3 and JOML. https://github.com/integeruser/jgltut
dc.relation/*ref*/johnlee175 (n.d.). dex. https://github.com/johnlee175/dex
dc.relation/*ref*/Jonassen, D. H. (2012). Designing for decision making. Educational Technology Research and Development, 60(2), 341-359. https://doi.org/10.1007/s11423-011-9230-5
dc.relation/*ref*/jvm (n.d.). modern-jogl-examples. https://github.com/jvm-graphics-labs/modern-jogl-examples
dc.relation/*ref*/Karanval (n.d.). EVEA: Virtual environment for teaching and learning. https://github.com/Karanval/EVEA
dc.relation/*ref*/Khandaker, N., Soh, L.-K., & Jiang, H. (2006). Student learning and team formation in a structured CSCL environment. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.130.2501&rep=rep1&type=pdf
dc.relation/*ref*/Khandaker, N., & Soh, L.-K. (2010). ClassroomWiki: A collaborative Wiki for instructional use with multiagent group formation. IEEE Transactions on Learning Technologies, 3(3), 190-202. https://doi.org/10.1109/TLT.2009.50
dc.relation/*ref*/Kotlin (n.d.). Kotlin programming language. https://kotlinlang.org/
dc.relation/*ref*/Kozma, R. (2000). Reflections on the state of educational technology research and development. Educational Technology Research and Development, 48(1), 5-15. https://doi.org/10.1007/BF02313481
dc.relation/*ref*/Lafleur, J. (2017). How to share code and make it shine. codeburst. https://codeburst.io/how-to-share-code-and-make-it-shine-f5ffcea1794f
dc.relation/*ref*/Leocardoso94 (n.d.). Free-Courses: A collection of free courses about programming. https://github.com/Leocardoso94/Free-Courses
dc.relation/*ref*/Leonard, A. (2011). Team syntegrity: A new methodology for group work. European Management Journal, 14(4), 407-413. https://doi.org/10.1016/0263-2373(96)00028-X
dc.relation/*ref*/Loizzo, J., & Ertmer, P. A. (2016). MOOCocracy: The learning culture of massive open online courses. Educational Technology Research and Development, 64(6), 1013-1032. https://doi.org/10.1007/s11423-016-9444-7
dc.relation/*ref*/luvoain (n.d.). Installation and deployment — INGInious 0.5.dev0 documentation. https://docs.inginious.org/en/v0.5/install_doc/installation.html
dc.relation/*ref*/Magnisalis, I., Demetriadis, S., & Karakostas, A. (2011). Adaptive and intelligent systems for collaborative learning support: A review of the field. IEEE Transactions on Learning Technologies, 4(1), 5-20. https://doi.org/10.1109/TLT.2011.2
dc.relation/*ref*/Mansilla, P. S., Costaguta, R., & Schiaffino, S. (2014). Multi agent model for skills training of CSCL e-tutors. In ACM (Eds.), EATIS '14: Proceedings of the 7th Euro American Conference on Telematics and Information Systems (art. 30). ACM Press. https://doi.org/10.1145/2590651.2590680
dc.relation/*ref*/Mohammadi, E. (2012). Knowledge mapping of the Iranian nanoscience and technology: A text mining approach. Scientometrics, 92(3), 593-608. https://doi.org/10.1007/s11192-012-0644-6
dc.relation/*ref*/Moodle (n.d.). Moodle - Open-source learning platform. https://moodle.org/?lang=es
dc.relation/*ref*/Munson, J. P., & Zitovsky, J. P. (2018). Models for early identification of struggling novice programmers. In ACM (Eds.), SIGCSE '18: Proceedings of the 49th ACM Technical Symposium on Computer Science Education (pp. 699-704). ACM. https://doi.org/10.1145/3159450.3159476
dc.relation/*ref*/nsoojin (n.d.). coursera-ml-py: Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera. https://github.com/nsoojin/coursera-ml-py
dc.relation/*ref*/Pathrabe, U. A. (2019). UtkarshPathrabe/Machine-Learning-Stanford-University-Coursera. https://github.com/UtkarshPathrabe/Machine-Learning-Stanford-University-Coursera
dc.relation/*ref*/Pea, R. D., Tinker, R., Linn, M., Means, B., Bransford, J., Roschelle, J., Hsi, S., Brophy, S., & Songer, N. (1999). Toward a learning technologies knowledge network. Educational Technology Research and Development, 47(2), 19-38. https://doi.org/10.1007/BF02299463
dc.relation/*ref*/philss (n.d.). Elixir School. https://elixirschool.com/es/
dc.relation/*ref*/PHP5.3 (n.d.). CakePHP – Build fast, grow solid – PHP Framework – Home. https://cakephp.org/
dc.relation/*ref*/pkulchenko (n.d.). ZeroBraneEduPack: A collection of simple lessons, scripts, and demos in Lua, suitable for learning programming concepts. https://github.com/pkulchenko/ZeroBraneEduPack
dc.relation/*ref*/Porras, J., Heikkinen, K., & Ikonen, J. (2007). Code camp: A setting for collaborative learning of programming. Advanced Technology for Learning, 4(1), 43-52. https://doi.org/10.2316/Journal.208.2007.1.208-0906
dc.relation/*ref*/Qiu, J., Tang, J., Liu, T. X., Gong, J., Zhang, C., Zhang, Q., & Xue, Y. (2016). Modeling and predicting learning behavior in MOOCs. In ACM (Eds.), WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining (pp. 93-102). ACM Press. https://doi.org/10.1145/2835776.2835842
dc.relation/*ref*/Rahwan, T. (2007). Algorithms for coalition formation in multi-agent systems [Unpublished doctoral dissertation, University of Southampton].
dc.relation/*ref*/Restrepo-Calle, F., Ramírez-Echeverry, J. J., & González, F. A. (2018, July 2-4). UNCODE: Interactive system for learning and automatic evaluation of computer programming skills [Conference presentation]. 10th International Conference on Education and New Learning Technologies, Palma, Spain. https://doi.org/10.21125/edulearn.2018.1632
dc.relation/*ref*/sainuguri (n.d.). Muse. https://github.com/sainuguri/Muse
dc.relation/*ref*/Salcedo, S. L., & Idrobo, A. M. O. (2011, October 12-15). New tools and methodologies for programming languages learning using the scribbler robot and Alice [Conference presentation]. 2011 Frontiers in Education Conference (FIE), Rapid City, SD, USA. https://doi.org/10.1109/FIE.2011.6142923
dc.relation/*ref*/Soh, L.-K., Khandaker, N., Liu, X., & Jiang, H. (2005). Computer-supported structured cooperative learning. In C.-K. Looi, D. Jonassen, & M. Ikeda (Eds.), Proceedings of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation (pp. 428-435). ACM. https://dl.acm.org/doi/10.5555/1563334.1563390
dc.relation/*ref*/Soh, L.-K., Khandaker, N., Liu, X., & Jiang, H. (2006a). A computer-supported cooperative learning system with multiagent intelligence. In ACM (Eds), AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (pp. 1556-1563). ACM Press. https://doi.org/10.1145/1160633.1160933
dc.relation/*ref*/Soh, L.-K., Khandaker, N., Liu, X., & Jiang, H. (2006b). Multiagent coalition formation for computer-supported cooperative learning. IAAI'06: Proceedings of the 18th conference on Innovative applications of artificial intelligence, 2, 1844-1851. http://dl.acm.org/citation.cfm?id=1597122.1597146
dc.relation/*ref*/Soh, L.-K., Khandaker, N., Liu, X., & Jiang, H. (2008). I-MINDS: A multiagent system for intelligent computer- supported collaborative learning and classroom management. International Journal of Artificial Intelligence in Education, 18(2), 119-151. http://digitalcommons.unl.edu/csearticles/61
dc.relation/*ref*/Solarte-Pabón, O., & Machuca-Villegas, L. (2019). Fostering motivation and improving student performance in an introductory programming course: An integrated teaching approach. Revista EIA, 16(31), 65. https://doi.org/10.24050/reia.v16i31.1230
dc.relation/*ref*/Suárez, C. G. H., Guerrero, V. A. B., Calle, F. R., & Osorio, F. A. G. (2021). Estrategia de enseñanza basada en la colaboración y la evaluación automática de código fuente en un curso de programación CS1. Investigación e Innovación en Ingenierías, 9(1), 50-60. https://doi.org/10.17081/invinno.9.1.4185
dc.relation/*ref*/Thomas, L., Ratcliffe, M., Woodbury, J., & Jarman, E. (2002). Learning styles and performance in the introductory programming sequence. ACM SIGCSE Bulletin, 34(1), 33-37. https://doi.org/10.1145/563517.563352
dc.relation/*ref*/tokers (2016). SABO. https://github.com/tokers/sabo
dc.relation/*ref*/tparisi (2012). WebVR. https://github.com/tparisi/WebVR
dc.relation/*ref*/trakla (n.d.). WWW-TRAKLA. http://www.cs.hut.fi/tred/WWW-TRAKLA/WWW-TRAKLA.html
dc.relation/*ref*/Triantafillou, E., Pomportsis, A., & Georgiadou, E. (2002). AES-CS: Adaptive educational system based on cognitive styles. https://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=5E362A162F2DFC505C4EB10D5E600A54?doi=10.1.1.2.5116&rep=rep1&type=pdf
dc.relation/*ref*/University of Quebec (2015). Onlinejudge. https://onlinejudge.org/
dc.relation/*ref*/van Gorp, J., & Grissom, S. (2001). An empirical evaluation of using constructive classroom activities to teach introductory programming. Computer Science Education, 11(3), 247-260. https://doi.org/10.1076/csed.11.3.247.3837
dc.relation/*ref*/Varier, D., Dumke, E. K., Abrams, L. M., Conklin, S. B., Barnes, J. S., & Hoover, N. R. (2017). Potential of one-to-one technologies in the classroom: Teachers and students weigh in. Educational Technology Research and Development, 65(4), 967–992. https://doi.org/10.1007/s11423-017-9509-2
dc.relation/*ref*/Vasiliev, Y. (2020). Natural language processing with Python and spaCy: A practical introduction. No Starch Press. https://www.overdrive.com/search?q=F7C72EAA-7BBD-4B45-8957-9B44182DF5B0
dc.relation/*ref*/vega (n.d.). Radial Tree Layout example. https://vega.github.io/vega/examples/radial-tree-layout/
dc.relation/*ref*/Vesin, B., Ivanović, M., Klašnja-Milićević, A., & Budimac, Z. (2011, October 14-16). Rule-based reasoning for altering pattern navigation in programming tutoring system [Conference presentation]. 15th International Conference on System Theory, Control and Computing, Sinaia, Romania.
dc.relation/*ref*/vfleaking (2016). Uoj (universal online judge). https://github.com/vfleaking/uoj
dc.relation/*ref*/vieiraeduardos (n.d.). Classroom: Virtual learning environment. https://github.com/vieiraeduardos/classroom
dc.relation/*ref*/Weber, G., & Brusilovsky, P. (2001). Elm-art: An adaptive versatile system for web-based instruction. International Journal of Artificial Intelligence in Education (IJAIED), 12, 351-384. https://sites.pitt.edu/~peterb/papers/JAIEDFinal.pdf
dc.relation/*ref*/Wiggins, J. B., Boyer, K. E., Baikadi, A., Ezen-Can, A., Grafsgaard, J. F., Ha, E. Y., Lester, J. C., Mitchell, C. M., & Wiebe, E. N. (2015). JavaTutor. In ACM (Eds.), SIGCSE '15: Proceedings of the 46th ACM Technical Symposium on Computer Science Education (p. 599).ACM Press. https://doi.org/10.1145/2676723.2691877
dc.relation/*ref*/Williams, J. (2019). html5-game-book. https://github.com/jwill/html5-game-book
dc.relation/*ref*/Williams, L., Wiebe, E., Yang, K., Ferzli, M., & Miller, C. (2002). In support of pair programming in the introductory computer science course. Computer Science Education, 12(3), 197-212. https://doi.org/10.1076/csed.12.3.197.8618
dc.relation/*ref*/Yang, J., & Luo, Z. (2007). Coalition formation mechanism in multi-agent systems based on genetic algorithms. Applied Soft Computing Journal, 7(2), 561-568. https://doi.org/10.1016/j.asoc.2006.04.004
dc.relation/*ref*/Yannibelli, V., & Amandi, A. (2012). A memetic algorithm for collaborative learning team formation in the context of software engineering courses. In F. Cipolla-Ficarra, K. Veltman, D. Verber, M. Cipolla-Ficarra, & Florian Kammüller (Eds.), Advances in New Technologies, Interactive Interfaces and Communicability (pp. 92-103). Springer. https://doi.org/10.1007/978-3-642-34010-9_9
dc.relation/*ref*/yrojha4ever (2015). JavaStud. https://github.com/yrojha4ever/JavaStud
dc.relation/*ref*/yudazilian (2017). Sunnyjudge. https://github.com/yudazilian/SunnyJudge
dc.relation/*ref*/Yuuta (2019, October). go-book. https://github.com/initpy/go-book
dc.relation/*ref*/Zingaro, D., Taylor, C., Porter, L., Clancy, M., Lee, C., Nam Liao, S., & Webb, K. C. (2018). Identifying student difficulties with basic data structures. In ACM (Eds.), ICER '18: Proceedings of the 2018 ACM Conference on International Computing Education Research (pp. 169-177). ACM. https://doi.org/10.1145/3230977.3231005
dc.relation.institutionUniversidad Distrital Francisco José de Caldas
dc.rightsDerechos de autor 2022 Tecnuraes-ES
dc.sourceTecnura; Vol. 27 No. 75 (2023): January - March ; 175 - 206en-US
dc.sourceTecnura; Vol. 27 Núm. 75 (2023): Enero - Marzo; 175 - 206es-ES
dc.source2248-7638
dc.source0123-921X
dc.subjectartificial intelligenceen-US
dc.subjectcomputer programmingen-US
dc.subjectcomputer-supported collaborative learningen-US
dc.subjectlearning computer programmingen-US
dc.subjectinteligencia artificiales-ES
dc.subjectprogramación de computadorases-ES
dc.subjectaprendizaje colaborativo asistido por computadoraes-ES
dc.subjectaprendizaje de programaciónes-ES
dc.titleArtificial Intelligence and Computer-Supported Collaborative Learning in Programming: A Systematic Mapping Studyen-US
dc.titleInteligencia artificial y aprendizaje colaborativo asistido por computadora en la programación: un estudio de mapeo sistemáticoes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion

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