Calidad y dificultad de los cuestionarios de un MOOC

Palabras clave: MOOC, Evaluación de un MOOC, Calidad de Cuestionarios


La oferta de cursos abiertos, gratuitos, masivos en línea (MOOC por sus siglas en inglés) ha tenido un crecimiento importante en los últimos años. La mayoría de estos cursos incluye cuestionarios de evaluación que sirven como guía para establecer un baremo para su aprobación. Estos cuestionarios son una herramienta de aprendizaje porque ponen en juego el conocimiento del participante y le dan la oportunidad para identificar sus dificultades y superarlas. También pueden convertirse en un impedimento para el progreso cuando aquellos participantes que no los aprueban deciden retirarse. En este sentido, resulta relevante tener procedimientos para establecer la calidad y dificultad de los cuestionarios de un MOOC, cuestiones que se abordan en este artículo. Ejemplificamos estas ideas y procedimientos con los datos del primer curso del programa PriMat1, Educación Matemática para profesores de primaria. Los resultados ponen de manifiesto la importancia de evaluar la calidad relativa y la dificultad de los cuestionarios de los cursos MOOC con el propósito de contribuir al aprendizaje y la retención de los participantes.


La descarga de datos todavía no está disponible.

Biografía del autor/a

Carlos Velasco, Universidad de los Andes
Carlos VelascoGestor de proyectos académicos“una empresa docente”Facultad de EducaciónUniversidad de los Andes


Aldon, G., Arzarello, F., Panero, M., Robutti, O., Taranto, E., & Trgalová, J. (2017). MOOC for mathematics teacher training: design principles and assessment. In G. Aldon & J. Trgalova (Eds.), The 13th International Conference on Technology in Mathematics Teaching–ICTMT 13 (pp. 1-8). Lyon: ENS.

Boaler, J. (2014). How to learn math: for teachers and parents. Resource document. Stanford University. Downloaded from

Borba, M. C., Askar, P., Engelbrecht, J., Gadanidis, G., Llinares, S., & Aguilar, M. S. (2016). Blended learning, e-learning and mobile learning in mathematics education. ZDM, 48(5), 589-610.

Chaw, L., & Tang, C. M. (2019). Driving high inclination to complete massive open online courses (MOOCs): motivation and engagement factors for learners. Electronic Journal of e-Learning, 17, 118-130.

Donitsa-Schmidt, S., & Topaz, B. (2018). Massive open online courses as a knowledge base for teachers. Journal of Education for Teaching, 44(5), 608-620.

Franzen, M. D. (2011). Item Difficulty. In J. S. Kreutzer, J. DeLuca y B. Caplan (Eds.), Encyclopedia of Clinical Neuropsychology (pp. 100-100). New York, NY: Springer.

Fyle, C. O. (2013). Teacher education MOOCs for developing world contexts: Issues and design considerations. Work presented in Sixth International Conference of MIT’s Learning International Networks Consortium (LINC).

Gadanidis, G. (2014). Mathematics-for-Teachers using a MOOC. Society for Information Technology & Teacher Education International Conference 2014, 2014, 310-314.

Gadanidis, G., & Namukas, I. (2007). Mathematics-for-Teachers (and Students). Journal of Teaching and Learning, 5(1), 13-22.

Galofré, A., y Wright, A. C. (2010). Índice de calidad para evaluar preguntas de opción múltiple. Revista de Educación en Ciencias de la Salud, 7(2), 141-145.

Gómez, P., y Gutiérrez, A. (2014). Conocimiento matemático y conocimiento didáctico del futuro profesor español de primaria. resultados del estudio TEDS-M. En M. T. González, M. Codes, D. Arnau y T. Ortega (Eds.), Investigación en Educación Matemática XVIII (pp. 99-114). Salamanca, España: SEIEM.

Gonçalves, V., Chumbo, I., Torres, E., & Gonçalves, B. M. F. (2016). Teacher education through MOOC: a case study. In L. Gómez, A. López & I. Candel Torres (Eds.), Proceedings of iCERi2016: 9th International Conference of Education, Research and Innovation (pp. 8350-8358). Sevilla: IATED Academy.

Greene, J. A., Oswald, C. A., & Pomerantz, J. (2015). Predictors of retention and achievement in a massive open online course. American Educational Research Journal, 52(5), 925-955.

Hew, K. F., Hu, X., Qiao, C., & Tang, Y. (2020). What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Computers & Education, 145, 1-15.

Hodges, C., Lowenthal, P., & Grant, M. (2016). Teacher Professional Development in the Digital Age: Design Considerations for MOOCs for Teachers. Society for Information Technology & Teacher Education International Conference 2016, 2016(1), 2075-2081.

Huang, L., Zhang, J., & Liu, Y. (2017). Antecedents of student MOOC revisit intention: Moderation effect of course difficulty. International Journal of Information Management, 37(2), 84-91.

Jordan, K. (2015). Massive open online course completion rates revisited: Assessment, length and attrition. International Review of Research in Open and Distance Learning, 16(3), 341-358.

Khalil, H., & Ebner, M. (2014). MOOCs completion rates and possible methods to improve retention- A literature review. In J. Herrington, J. Viteli & M. Leikomaa (Eds.), EdMedia+ Innovate Learning (pp. 1236-1244). Chesapeake, VA: AACE.

Kim, T.-d., Yang, M.-y., Bae, J., Min, B.-a., Lee, I., & Kim, J. (2017). Escape from infinite freedom: Effects of constraining user freedom on the prevention of dropout in an online learning context. Computers in Human Behavior, 66, 217-231.

Loken, E., Oravecz, Z., Tucker, C., & Linder, F. J. (2015). Psychometric analysis of residence and MOOC assessments. In E. L. Usher, N. A. Mamaril, C. Li, D. R. Economy & M. S. Kennedy (Eds.), 122nd ASEE Annual Conference and Exposition: Making Value for Society (pp. 2612841-261284114): American Society for Engineering Education.

Lortie-Forgues, H., Tian, J., & Siegler, R. S. (2015). Why is learning fraction and decimal arithmetic so difficult? Developmental Review, 38, 201-221.

Misra, P. (2018). MOOCs for Teacher Professional Development: Reflections and Suggested Actions. Open Praxis, 10(1), 67-77.

Moreno-Marcos, P. M., Muñoz-Merino, P. J., Maldonado-Mahauad, J., Pérez-Sanagustín, M., Alario-Hoyos, C., & Delgado Kloos, C. (2020). Temporal analysis for dropout prediction using self regulated learning strategies in self-paced MOOCs. Computers & Education, 145, 1-15.

Mourdi, Y., Sadgal, M., El Kabtane, H., & Berrada Fathi, W. (2019). A machine learning-based methodology to predict learners’ dropout, success or failure in MOOCs. International Journal of Web Information Systems, 15(5), 489-509.

Normandi Atiaja Atiaja, L., & Segundo Guerrero Proenza, R. (2016). MOOCs: Origin, characterization principal problems and challenges in higher education. Journal of E-Learning and Knowledge Society, 12(1), 65-76.

Özgür, A., & Yurdugül, H. (2015). The investigation of learner-assessment interaction in learning management systems. Master's thesis not published, Hacettepe University, Ankara.

Perez-Parras, J., & Gomez-Galan, J. (2015). Knowledge and Influence of MOOC Courses on Initial Teacher Training. International Journal of Educational Excellence, 1(2), 81-99.

Perna, L. W., Ruby, A., Boruch, R. F., Wang, N., Scull, J., Ahmad, S., et al. (2014). Moving through MOOCs: Understanding the progression of users in massive open online courses. Educational Researcher, 43(9), 421-432.

Pilli, O., Admiraal, W., & Salli, A. (2018). Moocs: Innovation or stagnation? Turkish Online Journal of Distance Education, 19(3), 169-181.

Ruiz, A. (2013). La reforma de la Educación Matemática en Costa Rica. Perspectiva de la praxis. Cuadernos de investigación y formación en educación matemática, 8(Especial), 7-111.

Salinas, P., Quintero, E., & Sanchez, X. (2015). Math and motion: A (Coursera) MOOC to rethink math assessment. Lecture Notes in Computer Science, 9192, 313-324.

Samuelsen, J., & Khalil, M. (2020). Study Effort and Student Success: A MOOC Case Study. In T. Tsiatsos & M. E. Auer (Eds.), Proceedings of the 21th International Conference on Interactive Collaborative Learning (Vol. 1, pp. 215-226). Kos Island, Greece: Springer Verlag.

Segovia, I., & Rico, L. (2011). Matemáticas para maestros de educación primaria. Madrid: Pirámide.

Thompson, N. (2019). What is classical item difficulty (P value)? Downloaded on 9/14/2019 from

Tømte, C. E. (2019). MOOCs in teacher education: institutional and pedagogical change? European Journal of Teacher Education, 42(1), 65-81.

Wambugu, P. W. (2018). Massive open online courses (MOOCs) for professional teacher and teacher educator development: A case of TESSA MOOC in Kenya. Universal Journal of Educational Research, 6(6), 1153-1157.

Wang, X., Hall, A. H., & Wang, Q. (2019). Investigating the implementation of accredited massive online open courses (MOOCs) in higher education: The boon and the bane. Australasian Journal of Educational Technology, 35(3), 1-14.

Wen, Y., Tian, Y., Wen, B., Zhou, Q., Cai, G. y Liu, S. (2020). Consideration of the local correlation of learning behaviors to predict dropouts from MOOCs. Tsinghua Science and Technology, 25(3),

Xiao, C., Qiu, H., & Cheng, S. M. (2019). Challenges and opportunities for effective assessments within a quality assurance framework for MOOCs. Journal of Hospitality, Leisure, Sport and Tourism Education, 24, 1-16.

Xie, Z. (2020). Modelling the dropout patterns of MOOC learners. Tsinghua Science and Technology, 25(3), 313-324.

Yousef, A. M. F., Chatti, M. A., Schroeder, U. y Wosnitza, M. (2014). What drives a successful MOOC? An empirical examination of criteria to assure design quality of MOOCs. In R. Huang, Kinshuk, D. G. Sampson, M. J. Spector & N. S. Chen (Eds.), IEEE 14th International Conference on Advanced Learning Technologies (pp. 44-48). Athens, Greece: Institute of Electrical and Electronics Engineers Inc.

Youssef, M., Mohammed, S., Hamada, E. K., & Wafaa, B. F. (2019). A predictive approach based on efficient feature selection and learning algorithms’ competition: Case of learners’ dropout in MOOCs. Education and Information Technologies, 24(6), 3591-3618.

Yuan, Q., Gao, Q., & Chen, Y. (2017). A preliminary study on the learning assessment in massive open online courses. Lecture Notes in Computer Science, 10281, 592-602.