Palabras clave
MOOC, aprendizaje MOOC, autoevaluación, estructura interpretativa, aprendizaje permanente, cogniciones del aprendizaje
Resumen
Los estudios han propuesto varios tipos de métodos de autoevaluación, sin embargo, muchos profesores, en el país, todavía consideran que la autoevaluación de estudiantes es «difícil de implementar». El objetivo de este artículo es optimizar la evaluación del método MOOC y establecer un paradigma integrado de autoevaluación para los estudiantes, en base de «centrado en estudiantes, asistido por profesores y compañeros». Se han seleccionado nueve factores clave que influyen en la implementación de autoevaluación del MOOC, y sobre esta base, a través del modelo de estructura interpretativa ISM y el método de análisis MICMAC, se han definido las relaciones entre estos factores y se ha establecido un paradigma integrado de seis niveles de la autoevaluación de estudiantes. Además, se han dado unas proposiciones para optimizar la autoevaluación del MOOC. En primer lugar, se necesitan utilizar la autoevaluación del MOOC como un método de evaluación formativa. En segundo lugar, las universidades deberían, mediante la publicidad, aumentar la conciencia de los estudiantes sobre la autoevaluación. En tercer lugar, las universidades pueden ofrecer programas de evaluación para mejorar la calidad de la evaluación de los estudiantes. En cuarto lugar, se utilizan los medios tecnológicos para optimizar el entorno de autoevaluación de estudiantes. Este estudio es significativo para hacer la autoevaluación como una base del aprendizaje online, y así, promover los efectos del MOOC.
Referencias
Abbas, H., Mehdi, M., Azad, I., & Frederico, G.F. (2022). Modelling the abstract knots in supply chains using interpretive structural modeling (ISM) approaches: A review-based comprehensive toolkit. Benchmarking: An International Journal, 29(10), 3251-3274. https://doi.org/10.1108/BIJ-08-2021-0459
Link DOI | Link Google Scholar
Admiraal, W., Huisman, B., & Pilli, O. (2015). Assessment in massive open online courses. Electron. J. e Learn. 13(4), 207- 216.
Link Google Scholar
Alonso-Tapia, J., & Panadero, E. (2010). Effects of Self-assessment Scripts on Self-regulation and Learning. Infancia y Aprendizaje, 33(3), 385-397. https://doi.org/10.1174/021037010792215145
Link DOI | Link Google Scholar
Andrade, H.L., & Du, Y. (2007). Student responses to criteria referenced self-assessment. Assessment & Evaluation in Higher Education, 32(2), 159-181. https://doi.org/10.1080/02602930600801928
Link DOI | Link Google Scholar
Ashton, S., & Davies, R.S. (2015). Using scaffolded rubrics to improve peer assessment in a MOOC writing course. Distance Education, 36(3), 312-334. https://doi.org/10.1080/01587919.2015.1081733
Link DOI | Link Google Scholar
Barak, M., & Rafaeli, S. (2004). Online question-posing and peer-assessment as means for web-based knowledge sharing in learning. International Journal of Human-Computer Studies, 61(1), 84-103. https://doi.org/10.1016/j.ijhcs.2003.12.005
Link DOI | Link Google Scholar
Bayne, S., & Ross, J. (2013). The pedagogy of the Massive Open Online Course: The UK view. The report, UK. https://bit.ly/3YdUFYd
Link Google Scholar
Beg, A., Alhemeiri, M., & Beg, A. (2020). A tool for facilitating the automated assessment of engineering/science courses. The International Journal of Electrical Engineering & Education. https://doi.org/10.1177/0020720920953134
Link DOI | Link Google Scholar
Boud, D., & Brew, A. (1995). Developing a typology for learner self-assessment practices. Research and Development in Higher Education, 18, 130-135. https://bit.ly/3uG0iRx
Link Google Scholar
Boud, D., & Falchikov, N. (1989). Quantitative studies of student self-assessment in higher education: A critical analysis of findings. Higher Education, 18, 529-549. https://doi.org/10.1007/BF00138746
Link DOI | Link Google Scholar
Brown, G.T.L., & Harris, L.R. (2014). The future of self-assessment in classroom practice: Reframing self-assessment as a core competency. Frontline Learning Research, 3(11), 22-30. https://doi.org/10.14786/flr.v2i1.24
Link DOI | Link Google Scholar
Burns, J.M. (1996). Leadership. Harper & Row.
Link Google Scholar
Capuano, N., & Caballé, S. (2018). Multi-criteria fuzzy ordinal peer assessment for MOOC. In F. Xhafa, L. Barolli, M. Greguš (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2018. Lecture Notes on Data Engineering and Communications Technologies (pp. 373-383). Springer. https://doi.org/10.1007/978-3-319-98557-2_34
Link DOI | Link Google Scholar
Cho, Y.H., & Cho, K., (2011). Peer reviewers learn from giving comments. Instructional Science, 39(5), 629-643. https://doi.org/10.1007/s11251-010-9146-1
Link DOI | Link Google Scholar
Chudowsky, N.P., & James, W. (2003). Large-scale assessment that supports learning: What will it take? Theory into Practice, 42(1), 75-83. https://doi.org/10.1207/s15430421tip4201_10
Link DOI | Link Google Scholar
Chunwijitra, S., Khanti, P., Suntiwichaya, S., Krairaksa, K., Tummarattamamont, P., Buranarach, M., & Wutiwiwatchai, C. (2020). Development of MOOC service framework for life long learning: A case study of Thai MOOC. IEICE Transactions on Information and Systems, 5, 1078-1087. https://doi.org/10.1587/transinf.2019EDP7262
Link DOI | Link Google Scholar
Cristianti, M., Utomo, C.B., & Murwatiningsi, M. (2020). The analysis of reflective learning toward the development of students’ attitude. Educational Management, 9(2), 191-199. https://bit.ly/3FlroCm
Link Google Scholar
Deng, R., Benckendorff, P., & Gannaway, D. (2020). Linking learner factors, teaching context, and engagement patterns with MOOC learning outcomes. Journal of computer-assisted learning, 36(5), 688-708. https://doi.org/10.1111/jcal.12437
Link DOI | Link Google Scholar
Dunning, D., Heath, C., & Suls, J. M. (2004). Flawed self-assessment: Implications for Health, education, and the Workplace. Psychological Science in the Public Interest, 5(3), 69-106. https://doi.org/10.1111/j.1529-1006.2004.00018.x
Link DOI | Link Google Scholar
Earl, L., & Torrance, N. (2000). Embedding accountability and improvement into large-scale assessment: What difference does it make? Peabody Journal of Education, 75(4), 114-41. https://doi.org/10.1207/S15327930PJE7504_6
Link DOI | Link Google Scholar
Earl, L.M. (2003). Assessment as learning: Using classroom assessment to maximize student learning. Corwin Press, Inc. https://bit.ly/3USuEdY
Link Google Scholar
Eschenbrenner, B., & Nah, F. (2007). Mobile technology in education: Uses and benefits. International Journal of Mobile Learning and Organisation, 1(2), 159-183. https://doi.org/10.1504/IJMLO.2007.012676
Link DOI | Link Google Scholar
Falchikov, N. (2004). Involving students in assessment. Psychology Learning & Teaching, 3(2), 102-108. https://doi.org/10.2304/plat.2003.3.2.102
Link DOI | Link Google Scholar
Hew, K.F., & Cheung, W.S., (2014). Students and instructors’ use of massive open online courses (MOOC): Motivations and challenges. Educational Research Review, 12, 45-58. https://doi.org/10.1016/j.edurev.2014.05.001
Link DOI | Link Google Scholar
Ivaniushin, D.A., Lyamin, A.V., & Kopylov, D.S. (2016). Assessment of outcomes in collaborative project based learning in online courses. In R.J. Howlett & C.J. Lakhmi (Eds.), Smart innovation, systems and technologies. Springer. https://doi.org/10.1007/978-3-319-39690 3_31
Link DOI | Link Google Scholar
Kitsantas, A., Reiser, R.A., & Doster, J. (2004). Developing self-regulated learners: Goal setting, self-evaluation, and organizational signals during the acquisition of procedural skills. The Journal of Experimental Education, 12(4), 269-287. https://doi.org/10.3200/JEXE.72.4.269-287
Link DOI | Link Google Scholar
Kulkarni, C., Wei, K.P., Le, H., Chia, D., Papadopoulos, K., Cheng, J., Koller, D., & R. Klemmer, S. (2013). Peer and self-assessment in massive online classes. ACM Transactions on Computer-Human Interaction, 20(6). https://doi.org/10.1145/2505057
Link DOI | Link Google Scholar
Lepp, M., Luik, P., Palts, T., Papli, K., Suviste, R., Säde, M., Hollo, A., Vaherpuu, V., & Tõnisson, E. (2017). Self and automated assessment in programming MOOC. Communications in Computer and Information Science. Springer. https://doi.org/10.1007/978-3-319-57744-9_7
Link DOI | Link Google Scholar
Li, Y.L. (2017). Literature review oil chinese students’self-evaluation over the past decade. Educational Perspective, 3, 41-47.
Link Google Scholar
Liyanagunawardena, T.R., Adams, A.A., & Williams, S.A. (2013). MOOCs: A systematic study of the published literature 2008–2012. The International Review of Open and Distance Learning, 14(3), 202-227. https://doi.org/10.19173/irrodl.v14i3.1455
Link DOI | Link Google Scholar
Motycka, C. A., Rose, R.L., Ried, L.D., & Brazeau, G.(2010). Self-assessment in pharmacy and health science education and professional practice. American Journal of Pharmaceutical Education, 74(5), 1-7. https://doi.org/10.5688/aj740585
Link DOI | Link Google Scholar
Olivares, S.L., Hernández, R.I.E., & Corolla, M.L.T. (2021). MOOC learning assessment in clinical settings: Analysis from quality dimensions. Medical Science Educator, 31, 447-455. https://doi.org/10.1007/s40670-020-01178-7
Link DOI | Link Google Scholar
Panadero, E., Alonso-Tapia, J., & Reche, E. (2013). Rubrics vs. self-assessment scripts affect self-regulation? performance and self-efficacy in pre-service teachers. Studies in Educational Assessment, 39(3), 125-132. https://doi.org/10.1016/j.stueduc.2013.04.001
Link DOI | Link Google Scholar
Papathoma-Köhle, M., Zischg, A., Fuchs, S., Glade, T., & Keiler, M. (2015). Loss estimation for landslides in mountain areas-an integrated toolbox for vulnerability assessment and damage documentation. Environ Model Softw, 62, 156-169. https://doi.org/10.1016/j.envsoft.2014.10.003
Link DOI | Link Google Scholar
Pfohl, H.C., Gallus, P., & Thomas, D. (2011). Interpretive structural modeling of supply chain risks. Int. J. Phys. Distrib. Logist. Manag, 41(9), 839-859. https://doi.org/10.1108/09600031111175816
Link DOI | Link Google Scholar
Pieterse, V. (2013). Automated assessment of programming assignments. In M. van-Eekelen, E. Barendsen, P. Sloep, G. van-der-Veer (Eds.), Proceedings of the 3rd Computer Science Education Research Conference on Computer Science Education Research (pp. 45-56). CSERC. https://bit.ly/3uFnhw2
Link Google Scholar
Ravi, V., & Shankar, R. (2005). Analysis of interactions among the barriers of reverse logistics. Technol. Forecast. Soc. Chang, 72(8), 1011-1029. https://doi.org/10.1016/j.techfore.2004.07.002
Link DOI | Link Google Scholar
Reinholz, D. (2016). The assessment cycle: A model for learning through peer assessment. Assessment & Evaluation in Higher Education, 41(2), 301-315. https://doi.org/10.1080/02602938.2015.1008982
Link DOI | Link Google Scholar
Rolheiser, C., & Ross, J. (2000). Student self-evaluation: What do we know. Orbit, 30(4), 33-36.
Link Google Scholar
Sadler, P.M., & Good, E. (2006). The impact of self and peer grading on student learning. Educational Assessment?11(1), 1-31. https://doi.org/10.1207/s15326977ea1101_1
Link DOI | Link Google Scholar
Sánchez-Vera, M. M., & Prendes-Espinosa, M. (2015). Beyond objective testing and peer assessment: alternative ways of assessment in MOOCs. Revista de Universidad y Sociedad del Conocimiento, 12(1), 119-129. https://doi.org/10.7238/rusc.v12i1.2262
Link DOI | Link Google Scholar
Sandeen, S.K. (2021). A typology of disclosure. Akron Law Review, 27, 31. https://bit.ly/3HDP5bJ
Link Google Scholar
Shahabadkar, P. (2012). Deployment of interpretive structural modelling methodology in supply chain management—An overview. Int. J. Ind. Eng. Prod. Res, 23, 195-205.
Link Google Scholar
Shen, L.Y., Song, X.N., Wu, Y., Liao, S.J., & Zhang, X.L. (2016). Interpretive structural modeling based factor analysis on the implementation of emission trading system in the Chinese building sector. Journal of Cleaner Production, 127, 214-227. https://doi.org/10.1016/j.jclepro.2016.03.151
Link DOI | Link Google Scholar
Shrader, S., Wu, M., Owens, D., & Ana, K. (2016). Massive open online courses (MOOCs): Participant activity, demographics, and satisfaction. Online Learning, 20(2), 199-216. https://doi.org/10.24059/olj.v20i2.596
Link DOI | Link Google Scholar
Stan?i?, M. (2020). Peer assessment as a learning and self-assessment tool: A look inside the black box. Assessment & Assessment in Higher Education, 1-13. https://doi.org/10.1080/02602938.2020.1828267
Link DOI | Link Google Scholar
Tapia, J.A., & Panadero, E. (2010). Effect of self-assessment scripts on self-regulation and learning. Journal for the Study of Education and Development, 33(3), 385-397. https://doi.org/10.1174/021037010792215145
Link DOI | Link Google Scholar
Taras, M. (2016). Situating power potentials and dynamics of learners and tutors within self-assessment models. Journal of Further and Higher Education, 40(6), 846-863. https://doi.org/10.1080/0309877X.2014.1000283
Link DOI | Link Google Scholar
Tauber, T. (2013). The dirty little secret of online learning: Students are bored and dropping out [EB/OL]. https://bit.ly/3G0ohS1
Link Google Scholar
Valdivia-Vázquez, J.A., Ramirez-Montoya, M.S., & Valenzuela-González J.R. (2021). Psychometric assessment of a tool to evaluate motivation and knowledge of an energy-related topic MOOC. Educational Media International, 58(3), 280-295. https://doi.org/10.1080/09523987.2021.1976827
Link DOI | Link Google Scholar
Wang, M., Yuan, B., & Kirschner, P.A. (2018). Reflective learning with complex problems in a visualization-based learning environment with expert support. Computers in Human Behavior, 87, 406-415. https://doi.org/10.1016/j.chb.2018.01.025
Link DOI | Link Google Scholar
Wang, Y.F., & Sun, S.Y. (2002). Students’ self-identification and self-assessment. Subject Education, 3, 45-49.
Link Google Scholar
Watson, S.L., Watson, W., Yu, J.H., Alamri, H., & Mueller, C.(2017). Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study. Computers & Education, 114, 274-285. https://doi.org/10.1016/j.compedu.2017.07.005
Link DOI | Link Google Scholar
Wilkowski, J., Russell, D.M., & Deutsch, A. (2014). Self-evaluation in advanced power searching and mapping with google MOOC. In M. Sahami, A. Fox, M.A. Hearst, M.T.H. Chi (Eds.), L@S '14: Proceedings of the first ACM Conference on Learning (pp. 109-116). ACM. https://doi.org/10.1145/2556325.2566241
Link DOI | Link Google Scholar
Wong, B.T.M. (2016). Factors leading to effective teaching of MOOCs. Asian Association of Open Universities Journal, 11(1), 105-118. https://doi.org/10.1108/AAOUJ-07-2016-0023
Link DOI | Link Google Scholar
Zeng, W. J. (2017). On the philosophy of learning: Research on the deepening path of the construction of learning society. People's Education Press, 231-232.
Link Google Scholar
Zhao, C., Bhalla, S., Halliday, L., Travaglia, J., & Kennedy, J. (2017). Exploring the role of assessment in developing learners’ critical thinking in massive open online courses. In C. Delgado-Kloos, P. Jermann, M. Pérez-Sanagustín, D. Seaton, & S. White (Eds), Digital education: Out to the world and back to the campus. EMOOCs 2017 (pp. 280-289). Springer. https://doi.org/10.1007/978-3-319-59044-8_33
Link DOI | Link Google Scholar
Ficha técnica
Recibido: 01-09-2022
Revisado: 06-10-2022
Aceptado: 29-11-2022
OnlineFirst: 30-01-2023
Fecha publicación: 01-04-2023
Tiempo de revisión del artículo : 35 (en días) | Media de tiempo de revisión de los manuscritos del número 75: 32 (en días)
Tiempo de aceptación del artículo: 89 (en días) | Media tiempo aceptación de los manuscritos del número 75: 93 (en días)
Tiempo de edición OnlineFirst: 167 (en días) | Media tiempo edición de los OnlineFirst del número 75: 171 (en días)
Tiempo de publicacicón final del artículo: 212 (en días) | Media tiempo de publicación final de los articulos del número 75: 216 (en días)
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