Una propuesta de evaluación de Recursos Educativos Digitales a través de la metodología fsQCA longitudinal

  1. Cristina Mendaña-Cuervo
  2. Nieves Remo-Diez
  3. Enrique López-González
Journal:
Pixel-Bit: Revista de medios y educación

ISSN: 1133-8482

Year of publication: 2024

Issue: 69

Pages: 195-226

Type: Article

More publications in: Pixel-Bit: Revista de medios y educación

Abstract

The use of Information and Communication Technologies in teaching has led to the proliferation of Digital Educational Resources (DERs) which seek to promote autonomous and asynchronous learning by students in order to improve academic results. However, theimpact of these resources on the learning process is rarely evaluated.In this paper, the fsQCA methodology is proposed to establish the combinations of DERs which enable students to achieve better performance, as opposed to methodologies based on the study of the net effects of each resource. The study is complemented with an analysis for several academic years through the longitudinal fsQCA methodology, which helps to conduct an analysis over time, providing a dynamic perspective of the opportunity and relevance of the DERs. The results of the research suggest that there is no single combination of DERs leading to success, but that the use of these resources in different ways combined allows students to achieve their academic goals, concluding that the methodology proposed can be useful for the evaluation of DERs regardless of their typology

Bibliographic References

  • Barhate, B., & Dirani, K. M. (2022). Career aspirations of generation Z: a systematic literature review. European Journal of Training and Development, 46(1/2), 139–157. https://doi.org/10.1108/EJTD-07-2020-0124
  • Bendjebar, S., Djebarnia, N. E. I., Mehenaoui, Z., & Lafifi, Y. (2023). Recommendation of pedagogical resources based on learners’ profiles. International Journal of Informatics and Applied Mathematics. https://doi.org/10.53508/ijiam.1213949
  • Bergmann, J., & Sams, A. (2012). Flip Your Classroom: Reach Every Student in Every Class Every Day. International Society for Technology in Education.
  • Campbell, J. T., Sirmon, D. G., & Schijven, M. (2015). Fuzzy Logic and the Market: A Configurational Approach to Investor Perceptions of Acquisition Announcements. Academy of Management Journal, 59(1), 163–187. https://doi.org/10.5465/amj.2013.0663
  • Cickovska, E. (2020). Understanding and Teaching Gen Z in Higher Education. Horizons Serie A, 26, 275–290. https://doi.org/10.20544/HORIZONS.A.26.3.20.P22
  • Di Meo, F., & Martí-Ballester, C.-P. (2020). Effects of the perceptions of online quizzes and electronic devices on student performance. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.4888
  • Drozdikova-Zaripova, A. R., & Sabirova, E. G. (2020). Usage of Digital Educational Resources in Teaching Students with Application of “Flipped Classroom” Technology. Contemporary Educational Technology, 12(2), ep278. https://doi.org/10.30935/cedtech/8582
  • Estrada-Molina, O., Fuentes-Cancell, D. R., & Morales, A. A. (2022). The assessment of the usability of digital educational resources: An interdisciplinary analysis from two systematic reviews. Education and Information Technologies, 27(3), 4037–4063. https://doi.org/10.1007/s10639-021-10727-5
  • Federo, R., & Saz-Carranza, A. (2018). A configurational analysis of board involvement in intergovernmental organizations. Corporate Governance: An International Review, 26(6), 414–428. https://doi.org/https://doi.org/10.1111/corg.12241
  • Ferrando Rodríguez, L., Gabarda-Mendez, V., Marin Suelves, D., & Ramón-Llin Más, J. (2023). ¿Crea contenidos digitales el profesorado universitario? Un diseño mixto de investigación. Pixel-Bit. Revista de Medios y Educación, 66, 137–172. https://doi.org/10.12795/pixelbit.96309
  • Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organizational research. Academy of Management Journal, 54, 393–420. https://doi.org/10.5465/AMJ.2011.60263120
  • Greckhamer, T., Misangyi, V. F., & Fiss, P. C. (2013). Chapter 3 The Two QCAs: From a Small-N to a Large-N Set Theoretic Approach. In P. C. Fiss, B. Cambré, & A. Marx (Eds.), Configurational Theory and Methods in Organizational Research (Vol. 38, pp. 49–75). Emerald Group Publishing Limited. https://doi.org/10.1108/S0733-558X(2013)0000038007
  • Gutiérrez-González, C., Montero, L., Espitia, L., & Torres, Y. (2023). Análisis de la producción científica relacionada con Recursos Educativos Digitales (RED) y Objetos Virtuales de Aprendizaje (OVA), entre 2000 – 2021. Revista de Investigación Educativa, 41(1), 263–280. https://doi.org/10.6018/rie.518741
  • Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A., Sarker, K. U., & Sattar, M. U. (2020). Predicting Student Performance in Higher Educational Institutions Using Video Learning Analytics and Data Mining Techniques. Applied Sciences, 10(11), 3894. https://doi.org/10.3390/app10113894
  • Haxhi, I., & Aguilera, R. V. (2017). An Institutional Configurational Approach to Cross-National Diversity in Corporate Governance. Journal of Management Studies, 54(3), 261–303. https://doi.org/https://doi.org/10.1111/joms.12247
  • Kirschner, P. A., & De Bruyckere, P. (2017). The myths of the digital native and the multitasker. Teaching and Teacher Education, 67, 135–142. https://doi.org/10.1016/J.TATE.2017.06.001
  • Latif, E., & Miles, S. (2020). The Impact of Assignments and Quizzes on Exam Grades: A Difference-in-Difference Approach. Journal of Statistics Education, 28(3), 289–294. https://doi.org/10.1080/10691898.2020.1807429
  • Maquilón Sánchez, J. J., Mirete Ruz, A. B., García Sánchez, F. A., & Hernández Pina, F. (2013). Valoración de las TIC por los estudiantes universitarios y su relación con los enfoques de aprendizaje. Revista de Investigación Educativa, 31(2), 537–554. https://doi.org/10.6018/rie.31.2.151891
  • Ndiyae, N. M., Chaabi, Y., Lekdioui, K., & Lishou, C. (2019). Recommending system for digital educational resources based on learning analysis. Proceedings of the New Challenges in Data Sciences: Acts of the Second Conference of the Moroccan Classification Society. https://api.semanticscholar.org/CorpusID:85519277
  • Noetel, M., Griffith, S., Delaney, O., Sanders, T., Parker, P., del Pozo Cruz, B., & Lonsdale, C. (2021). Video Improves Learning in Higher Education: A Systematic Review. Review of Educational Research, 91(2), 204–236. https://doi.org/10.3102/0034654321990713
  • Okike, E. U., & Mogorosi, M. (2020). Educational Data Mining for Monitoring and Improving Academic Performance at University Levels. International Journal of Advanced Computer Science and Applications, 11(11). https://doi.org/10.14569/IJACSA.2020.0111171
  • Pappas, I. O., & Woodside, A. G. (2021). Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines for research practice in Information Systems and marketing. International Journal of Information Management, 58, 102310. https://doi.org/10.1016/J.IJINFOMGT.2021.102310
  • Pérez de Albéniz Iturriaga, A., Escolano Pérez, E., Pascual Sufrate, M. T., Lucas Molina, B., & Sastre i Riba, S. (2015). Metacognición en un proceso de aprendizaje autónomo y cooperativo en el aula universitaria. Contextos Educativos, 18, 95–108. https://doi.org/10.18172/con.2576
  • Prensky, M. (2001). Digital Natives, Digital Immigrants Part 1. On the Horizon, 9(5), 1–6. https://doi.org/10.1108/10748120110424816
  • Ragin, C. C. (2000). Fuzzy-Set Social Science. University of Chicago Press.
  • Ragin, C. C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. University of Chicago Press. https://doi.org/10.7208/chicago/9780226702797.001.0001
  • Ragin, C. C., & Davey, S. (2022). Fuzzy-set/Qualitative comparative analysis 4.0. In Department of Sociology, University of California. http://www.socsci.uci.edu/~cragin/fsQCA/software.shtml
  • Ragin, C. C., & Rihoux, B. (2004). Qualitative Comparative Analysis (QCA): State of the Art and Prospects. Qualitative Methods, 3–13. https://doi.org/10.5281/zenodo.998222
  • Rozo, H., & Real, M. (2019). Pedagogical guidelines for the creation of adaptive digital educational resources: A review of the literature. Journal of Technology and Science Education, 9(3), 308. https://doi.org/10.3926/jotse.652
  • Russo, I., & Confente, I. (2019). From dataset to qualitative comparative analysis (QCA)—Challenges and tricky points: A research note on contrarian case analysis and data calibration. Australasian Marketing Journal, 27(2), 129–135. https://doi.org/https://doi.org/10.1016/j.ausmj.2018.11.001
  • Schneider, C. Q., & Wagemann, C. (2010). Standards of Good Practice in Qualitative Comparative Analysis (QCA) and Fuzzy-Sets. Comparative Sociology, 9(3), 397–418. https://doi.org/https://doi.org/10.1163/156913210X12493538729793
  • Schwieger, D., & Ladwig, C. (2018). Reaching and Retaining the Next Generation: Adapting to the Expectations of Gen Z in the Classroom. Information Systems Education Journal, 3, 16. http://iscap.info;http://isedj.org
  • Segura-Robles, A., Parra-González, M., & Gallardo-Vigil, M. (2020). Bibliometric and Collaborative Network Analysis on Active Methodologies in Education. Journal of New Approaches in Educational Research, 9(2), 259–274.
  • Soffer, T., & Cohen, A. (2019). Students’ engagement characteristics predict success and completion of online courses. Journal of Computer Assisted Learning, 35(3), 378–389. https://doi.org/10.1111/jcal.12340
  • Sotola, L. K., & Crede, M. (2021). Regarding Class Quizzes: a Meta-analytic Synthesis of Studies on the Relationship Between Frequent Low-Stakes Testing and Class Performance. Educational Psychology Review, 33(2), 407–426. https://doi.org/10.1007/s10648-020-09563-9
  • Woodside, A. G. (2013). Moving beyond multiple regression analysis to algorithms: Calling for a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. Journal of Business Research, 66, 463–472. https://doi.org/10.1016/j.jbusres.2012.12.021
  • Woodside, A. G. (2014). Embrace perform model: Complexity theory, contrarian case analysis, and multiple realities. Journal of Business Research, 67(12), 2495–2503. https://doi.org/10.1016/j.jbusres.2014.07.006
  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X