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Vol. 9 Núm. 1 (2021): Enero-Junio

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Analítica de aprendizaje como estrategia de apoyo al aula invertida en cursos de programación: una revisión sistemática de literatura

https://doi.org/10.17081/invinno.9.1.4464
José Miguel Llanos Mosquera
Universidad del Valle, Colombia
https://orcid.org/0000-0003-4642-2770
Víctor Andrés Bucheli Guerrero
Universidad del Valle, Colombia
https://orcid.org/0000-0002-0885-8699

Corresponding Author(s) : José Miguel Llanos Mosquera

jose.llanos@correounivalle.edu.co

Investigación e Innovación en Ingenierías, Vol. 9 Núm. 1 (2021): Enero-Junio

Resumen

Objetivo: Identificar los métodos y las técnicas que se están utilizando en la analítica de aprendizaje, como apoyo al aula invertida para los cursos de programación. Metodología: Se realizó la búsqueda de artículos publicados en revistas y conferencias científicas, a partir de herramientas bibliográficas que ayudaron a construir el corpus de referencias. Luego se analizó la información más significativa de los documentos, para el desarrollo de la revisión sistemática. Resultados: Esta revisión identificó que los métodos y las técnicas han contribuido en la preparación de los estudiantes, la definición de estrategias para el aprendizaje de la programación, herramientas de detección de errores en el código fuente y sistemas de tutorías inteligentes. Mejorando la retención de los estudiantes, los hábitos de estudio y las experiencias de aprendizaje. Conclusiones: Las investigaciones han analizado los datos almacenados en sistemas de gestión de aprendizaje (LMS), con el fin de generar modelos de predicción que permitan mejorar la formación en línea, el rendimiento académico y la retroalimentación personalizada. Todos estos aportes han apoyado a las áreas de conocimiento de aprendizaje automático y minería de datos, con la solución de problemas relacionados al aprendizaje supervisado, no supervisado y la minería de datos educativa.

Palabras clave

Analítica de aprendizaje, aula invertida, cursos de programación, aprendizaje automático, minería de datos, minería de datos educativa
[1]
J. M. Llanos Mosquera y V. A. Bucheli Guerrero, «Analítica de aprendizaje como estrategia de apoyo al aula invertida en cursos de programación: una revisión sistemática de literatura», Investigación e Innovación en Ingenierías, vol. 9, n.º 1, pp. 114–135, abr. 2021.
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Referencias
  • “El e-Learning dominará la educación a partir de 2019 | VIU.” https://www.universidadviu.com/el-e-learning-dominara-la-educacion-a-partir-de-2019/ (accessed Jul. 14, 2020).
  • G. Siemens, “Learning Analytics: The Emergence of a Discipline,” Am. Behav. Sci., vol. 57, no. 10, pp. 1380–1400, Oct. 2013, doi: 10.1177/0002764213498851.
  • P. Rojas Castro, “Learning Analytics. Una Revisión de la Literatura,” Educ. Educ., vol. 20, no. 1, pp. 106–128, Feb. 2017, doi: 10.5294/edu.2017.20.1.6.
  • J. P. Campbell, “Academic Analytics: A New Tool for a New Era,” p. 10, 2007.
  • S. Nunn, J. T. Avella, T. Kanai, and M. Kebritchi, “Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review,” Online Learn., vol. 20, no. 2, Jan. 2016, doi: 10.24059/olj.v20i2.790.
  • J. L. Bishop, U. S. University, D. M. A. Verleger, E.-R. A. Univ, and D. Beach, “The Flipped Classroom: A Survey of the Research,” vol. 30, pp. 1–18, 2013.
  • H. N. Mok, “Teaching tip: The flipped classroom,” J. Inf. Syst. Educ., vol. 25, pp. 7–11, 2014.
  • A. Y. Q. Huang, O. H. T. Lu, J. C. H. Huang, C. J. Yin, and S. J. H. Yang, “Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs,” Interact. Learn. Environ., vol. 28, no. 2, pp. 206–230, Feb. 2020, doi: 10.1080/10494820.2019.1636086.
  • E. A. Kolog, “Detecting Emotions in Students’ Generated Content: An Evaluation of EmoTect System,” in Technology in Education. Innovative Solutions and Practices, Singapore, 2018, vol. 843, pp. 235–248, doi: https://doi.org/10.1007/978-981-13-0008-0_22.
  • F. Ghaffar, N. Peirce, and A. Serlie, “Assessing Leadership Competencies Through Social Network Analysis,” in Lifelong Technology-Enhanced Learning, Cham, 2018, vol. 11082, pp. 452–466, doi: https://doi.org/10.1007/978-3-319-98572-5_35.
  • L. Wang and T. Wang, “Automatic Correction of Definite Article Redundancy Error in the English Compositions of College Students,” in Technology in Education. Innovative Solutions and Practices, Singapore, 2018, vol. 843, pp. 77–85, doi: https://doi.org/10.1007/978-981-13-0008-0_8.
  • Mbaye Baba, “Organisation of knowledge from traces of humen learning,” Portugal, 2019, doi: 10.33965/el2019_201909D055.
  • V. Neto et al., “Automated Analysis of Cognitive Presence in Online Discussions Written in Portuguese,” in Lifelong Technology-Enhanced Learning, Cham, 2018, vol. 11082, pp. 245–261, doi: https://doi.org/10.1007/978-3-319-98572-5_19.
  • B. Burke, P. Weßeler, and J. te Vrugt, “A Programming Language Independent Platform for Algorithm Learning,” in Lifelong Technology-Enhanced Learning, Cham, 2018, vol. 11082, pp. 652–655, doi: https://doi.org/10.1007/978-3-319-98572-5_66.
  • Y. Yamada and M. Hirakawa, “A Case Study of Analyzing Logs of LMS in Flipped Classroom,” in 2015 IIAI 4th International Congress on Advanced Applied Informatics, Okayama, Japan, Jul. 2015, pp. 374–378, doi: 10.1109/IIAI-AAI.2015.255.
  • M. C. Saiz Manzanares, R. Marticorena Sánchez, Á. Arnaiz González, M. del C. Escolar Llamazares, and M. Á. Queiruga Dios, “Detección del alumno en riesgo en titulaciones de Ciencias de la Salud: aplicación de técnicas de Learning Analytics,” Eur. J. Investig. Health Psychol. Educ., vol. 8, no. 3, p. 129, Dec. 2018, doi: 10.30552/ejihpe.v8i3.273.
  • I. Šarić-Grgić, A. Grubišić, L. Šerić, and T. J. Robinson, “Student Clustering Based on Learning Behavior Data in the Intelligent Tutoring System:,” Int. J. Distance Educ. Technol., vol. 18, no. 2, pp. 73–89, Apr. 2020, doi: 10.4018/IJDET.2020040105.
  • S. Nizam Ismail, S. Hamid, and H. Chiroma, “The utilization of learning analytics to develop student engagement model in learning management system,” J. Phys. Conf. Ser., vol. 1339, p. 012096, Dec. 2019, doi: 10.1088/1742-6596/1339/1/012096.
  • M. Rahmani Hanzaki and C. Demmans Epp, “The Effect of Personality and Course Attributes on Academic Performance in MOOCs,” in Lifelong Technology-Enhanced Learning, Cham, 2018, vol. 11082, pp. 497–509, doi: https://doi.org/10.1007/978-3-319-98572-5_38.
  • S. Praharaj, M. Scheffel, H. Drachsler, and M. Specht, “Multimodal Analytics for Real-Time Feedback in Co-located Collaboration,” in Lifelong Technology-Enhanced Learning, Cham, 2018, vol. 11082, pp. 187–201, doi: https://doi.org/10.1007/978-3-319-98572-5_15.
  • P. Pham and J. Wang, “Attentivelearner: Improving mobile MOOC learning via implicit heart rate tracking,” Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma., vol. 9112, pp. 367–376, 2015, doi: 10.1007/978-3-319-19773-9_37.
  • M. Masala, S. Ruseti, G. Gutu-Robu, T. Rebedea, M. Dascalu, and S. Trausan-Matu, “Help Me Understand This Conversation: Methods of Identifying Implicit Links Between CSCL Contributions,” in Lifelong Technology-Enhanced Learning, Cham, 2018, vol. 11082, pp. 482–496, doi: https://doi.org/10.1007/978-3-319-98572-5_37.
  • K. Sharma, K. Mangaroska, H. Trætteberg, S. Lee-Cultura, and M. Giannakos, “Evidence for Programming Strategies in University Coding Exercises,” in Lifelong Technology-Enhanced Learning, Cham, 2018, vol. 11082, pp. 326–339, doi: https://doi.org/10.1007/978-3-319-98572-5_25.
  • J. Maldonado-Mahauad, M. Pérez-Sanagustín, P. M. Moreno-Marcos, C. Alario-Hoyos, P. J. Muñoz-Merino, and C. Delgado-Kloos, “Predicting Learners’ Success in a Self-paced MOOC Through Sequence Patterns of Self-regulated Learning,” in Lifelong Technology-Enhanced Learning, Cham, 2018, vol. 11082, pp. 355–369, doi: https://doi.org/10.1007/978-3-319-98572-5_27.
  • F. H. Wang, “An exploration of online behaviour engagement and achievement in flipped classroom supported by learning management system,” Comput. Educ., vol. 114, pp. 79–91, Nov. 2017, doi: 10.1016/j.compedu.2017.06.012.
  • Z. Tao, B. Zhang, and I. K. W. Lai, “Perceived Online Learning Environment and Students’ Learning Performance in Higher Education: Mediating Role of Student Engagement,” in Technology in Education. Innovative Solutions and Practices, Singapore, 2018, vol. 843, pp. 56–64, doi: https://doi.org/10.1007/978-981-13-0008-0_6.
  • Z. Sun, L. Lu, and K. Xie, “The Effects of Self-Regulated Learning on Students’ Performance Trajectory in the Flipped Math Classroom,” Singap. Int. Soc. Learn. Sci., p. 8, 2016, doi: 10.22318/icls2016.11.
  • Su Chien-Yuan, Hu Yue, and Li Yu-Hang, “Teacher attitudes regarding the use of game-based programming tools in K-12 education,” in MULTI CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS 2019, Portugal, 2019, p. 73, doi: 10.33965/el2019_201909F010.
  • D. Gašević, S. Dawson, T. Rogers, and D. Gasevic, “Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success,” Internet High. Educ., vol. 28, pp. 68–84, Jan. 2016, doi: 10.1016/j.iheduc.2015.10.002.
  • C.-H. Luk, K.-K. Ng, and W.-M. Lam, “The Acceptance of Using Open-Source Learning Platform (Moodle) for Learning in Hong Kong’s Higher Education,” in Technology in Education. Innovative Solutions and Practices, Singapore, 2018, vol. 843, pp. 249–257, doi: https://doi.org/10.1007/978-981-13-0008-0_23.
  • L. Gren, “A Flipped Classroom Approach to Teaching Empirical Software Engineering,” IEEE Trans. Educ., 2020, doi: 10.1109/TE.2019.2960264.
  • C. C. Hsiao, J. C. H. Huang, A. Y. Q. Huang, O. H. T. Lu, C. J. Yin, and S. J. H. Yang, “Exploring the effects of online learning behaviors on short-term and long-term learning outcomes in flipped classrooms,” Interact. Learn. Environ., vol. 27, no. 8, pp. 1160–1177, 2019, doi: 10.1080/10494820.2018.1522651.
  • I. Khan and A. Pardo, “Data2U: scalable real time student feedback in active learning environments,” in Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK ’16, Edinburgh, United Kingdom, 2016, pp. 249–253, doi: 10.1145/2883851.2883911.
  • H. Le and J. Jia, “Analysis of Learner Timeout Behavior in Online Tests of a Bigdata Set Based on the OLAI Concept,” in Technology in Education. Innovative Solutions and Practices, Singapore, 2018, vol. 843, pp. 285–294, doi: https://doi.org/10.1007/978-981-13-0008-0_27.
  • J. Jia, “Design, Implementation and Evaluation of Blended Learning for the Undergraduate Course ‘Education and Artificial Intelligence,’” in Technology in Education. Innovative Solutions and Practices, Singapore, 2018, vol. 843, pp. 211–222, doi: https://doi.org/10.1007/978-981-13-0008-0_20.
  • A. Pardo, Y. Zhao, N. Mirriahi, A. Zhao, S. Dawson, and D. Gašević, “Identifying learning strategies associated with active use of video annotation software,” in ACM International Conference Proceeding Series, 2015, vol. 16-20-March-2015, pp. 255–259, doi: 10.1145/2723576.2723611.
  • P. Schwarzenberg, J. Navon, and M. Pérez-Sanagustín, “Models to provide guidance in flipped classes using online activity,” J. Comput. High. Educ., 2019, doi: 10.1007/s12528-019-09233-y.
  • J. Jovanović, D. Gašević, S. Dawson, A. Pardo, and N. Mirriahi, “Learning analytics to unveil learning strategies in a flipped classroom,” Internet High. Educ., vol. 33, pp. 74–85, 2017, doi: 10.1016/j.iheduc.2017.02.001.
  • J. Saint, D. Gašević, and A. Pardo, “Detecting Learning Strategies Through Process Mining,” Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma., vol. 11082 LNCS, pp. 385–398, 2018, doi: 10.1007/978-3-319-98572-5_29.
  • N. Ahmad Uzir, D. Gašević, W. Matcha, J. Jovanović, and A. Pardo, “Analytics of time management strategies in a flipped classroom,” J. Comput. Assist. Learn., vol. 36, no. 1, pp. 70–88, 2020, doi: 10.1111/jcal.12392.
  • Z. Sun, L. Lu, and K. Xie, “The effects of self-regulated learning on students’ performance trajectory in the flipped math classroom,” Proc. Int. Conf. Learn. Sci. ICLS, vol. 1, pp. 66–73, 2016, doi: 10.22318/icls2016.11.
  • E. Fincham, D. Gašević, J. Jovanović, and A. Pardo, “From Study Tactics to Learning Strategies: An Analytical Method for Extracting Interpretable Representations,” IEEE Trans. Learn. Technol., vol. 12, no. 1, pp. 59–72, 2019, doi: 10.1109/TLT.2018.2823317.
  • M. Dascalu, M.-D. Sirbu, G. Gutu-Robu, S. Ruseti, S. A. Crossley, and S. Trausan-Matu, “Cohesion-Centered Analysis of Sociograms for Online Communities and Courses Using ReaderBench,” in Lifelong Technology-Enhanced Learning, Cham, 2018, vol. 11082, pp. 622–626, doi: https://doi.org/10.1007/978-3-319-98572-5_59.
  • W. Matcha, D. Gašević, N. A. Uzir, J. Jovanović, and A. Pardo, “Analytics of learning strategies: Associations with academic performance and feedback,” in ACM International Conference Proceeding Series, 2019, pp. 461–470, doi: 10.1145/3303772.3303787.
  • A. AlJarrah, M. K. Thomas, and M. Shehab, “Investigating temporal access in a flipped classroom: procrastination persists,” Int. J. Educ. Technol. High. Educ., vol. 15, no. 1, 2018, doi: 10.1186/s41239-017-0083-9.
  • I. Šarić, A. Grubišić, L. Šerić, and T. J. Robinson, “Data-Driven Student Clusters Based on Online Learning Behavior in a Flipped Classroom with an Intelligent Tutoring System,” in Intelligent Tutoring Systems, Cham, 2019, vol. 11528, pp. 72–81, doi: https://doi.org/10.1007/978-3-030-22244-4_10.
  • F.-R. Sun, H.-Z. Hu, R.-G. Wan, X. Fu, and S.-J. Wu, “A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroom,” Interact. Learn. Environ., 2019, doi: 10.1080/10494820.2019.1660996.
  • M. Henderikx, K. Kreijns, and M. Kalz, “A Classification of Barriers that Influence Intention Achievement in MOOCs,” in Lifelong Technology-Enhanced Learning, Cham, 2018, vol. 11082, pp. 3–15, doi: https://doi.org/10.1007/978-3-319-98572-5_1.
  • M. Dascalu, I. C. Paraschiv, D. S. McNamara, and S. Trausan-Matu, “Towards an Automated Model of Comprehension (AMoC),” in Lifelong Technology-Enhanced Learning, Cham, 2018, vol. 11082, pp. 427–436, doi: https://doi.org/10.1007/978-3-319-98572-5_33.
  • Y. V. Paredes, D. Azcona, I.-H. Hsiao, and A. Smeaton, “Learning by Reviewing Paper-Based Programming Assessments,” in Lifelong Technology-Enhanced Learning, Cham, 2018, vol. 11082, pp. 510–523, doi: https://doi.org/10.1007/978-3-319-98572-5_39.
  • J. C. S. Silva, E. Zambom, R. L. Rodrigues, J. L. C. Ramos, and F. Da Fonseca De Souza, “Effects of learning analytics on students’ self-regulated learning in flipped classroom,” Int. J. Inf. Commun. Technol. Educ., vol. 14, no. 3, pp. 91–107, 2018, doi: 10.4018/IJICTE.2018070108.
  • G. Alexandron, J. A. Ruipérez-Valiente, S. Lee, and D. E. Pritchard, “Evaluating the Robustness of Learning Analytics Results Against Fake Learners,” in Lifelong Technology-Enhanced Learning, Cham, 2018, vol. 11082, pp. 74–87, doi: https://doi.org/10.1007/978-3-319-98572-5_6.
  • B. Yousuf, A. Staikopoulos, and O. Conlan, “Motivating Students to Enhance Their Knowledge Levels Through Personalized and Scrutable Visual Narratives,” in Lifelong Technology-Enhanced Learning, Cham, 2018, vol. 11082, pp. 136–150, doi: https://doi.org/10.1007/978-3-319-98572-5_11.
  • M. Apiola, T. Karunaratne, E. Kaila, and M.-J. Laakso, “Experiences from Digital Learning Analytics in Finland and Sweden: A Collaborative Approach,” in 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, May 2019, pp. 627–632, doi: 10.23919/MIPRO.2019.8757204.
  • E. Doko and L. A. Bexheti, “A systematic mapping study of educational technologies based on educational data mining and learning analytics,” in 2018 7th Mediterranean Conference on Embedded Computing, MECO 2018 - Including ECYPS 2018, Proceedings, 2018, pp. 1–4, doi: 10.1109/MECO.2018.8406052.
  • J. Vaclavek, J. Kuzilek, J. Skocilas, Z. Zdrahal, and V. Fuglik, “Learning Analytics Dashboard Analysing First-Year Engineering Students,” in Lifelong Technology-Enhanced Learning, Cham, 2018, vol. 11082, pp. 575–578, doi: https://doi.org/10.1007/978-3-319-98572-5_48.
  • A. Gelan et al., “Affordances and limitations of learning analytics for computer-assisted language learning: a case study of the VITAL project,” Comput. Assist. Lang. Learn., vol. 31, no. 3, pp. 294–319, 2018, doi: 10.1080/09588221.2017.1418382.
  • S. Sosnovsky, L. Müter, M. Valkenier, M. Brinkhuis, and A. Hofman, “Detection of Student Modelling Anomalies,” in Lifelong Technology-Enhanced Learning, Cham, 2018, vol. 11082, pp. 531–536, doi: https://doi.org/10.1007/978-3-319-98572-5_41.
  • D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, “Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement,” Int. J. Surg., vol. 8, no. 5, pp. 336–341, Jan. 2010, doi: 10.1016/j.ijsu.2010.02.007.
  • R. Avellaneda, S. Cabrera, P. A. Martínez, y C. G. Donoso Albarracín, “Apoyo tecnológico para la fidelización y captación de nuevos clientes por medio de una aplicación móvil”, Investigación e Innovación en Ingenierías, vol. 5, n.º 1, pp. 92-101, 2017. DOI: https://doi.org/10.17081/invinno.5.1.2618
  • J. A. Calderón Velasco, G. A. Amarillo Cárdenas, L. A. Silva Bahamon, y C. G. Donoso Albarracín, “Biometría dactilar: una nueva alternativa de controlar efectivamente la asistencia a clases”, Investigación e Innovación en Ingenierías, vol. 6, n.º 1, pp. 27 - 39, 2017. DOI: https://doi.org/10.17081/invinno.6.1.2773
  • “Zotero | Your personal research assistant.” https://www.zotero.org/ (accessed Jun. 23, 2020).
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