Mejora de los procesos de evaluación mediante analítica visual del aprendizaje

  1. Álvarez-Arana, Ainhoa 1
  2. Villamañe-Gironés, Mikel 1
  3. Larrañaga-Olagaray, Mikel 1
  1. 1 Universidad del País Vasco UPV/EHU
Zeitschrift:
Education in the knowledge society (EKS)

ISSN: 2444-8729 1138-9737

Datum der Publikation: 2020

Nummer: 21

Art: Artikel

DOI: 10.14201/EKS.22914 DIALNET GOOGLE SCHOLAR lock_openOpen Access editor

Andere Publikationen in: Education in the knowledge society (EKS)

Ziele für nachhaltige Entwicklung

Zusammenfassung

Current trends in higher education involve the integration of face-to-face learning, online learning and the use of information technologies that support diverse aspects of the learning-teaching process. One of the main aspects considered in all the educational tendencies is the use of effective assessment processes that entail a continuous analysis of students’ results in order to detect anomalies and provide adequate feedback so as to solve them. However, in learning-teaching environments where there is a great amount of heterogeneous information, this must be integrated in order to be adequately analyzed. This paper presents COBLE, a tool that makes possible the integration of information from different sources to analyze it using visual learning analytics techniques. COBLE incorporates a feedback module that provides both teachers and students with visual information related to the assessment process in order to facilitate their decision-making processes. The provided visualizations can be adapted to the requirements of each course or user. The evaluation of COBLE has been carried out using it in a real blended learning environment where different teachers have integrated information from different sources (Moodle, personal spreadsheets, etc.), related to the performance of students. Next, both teachers and students used COBLE and its visualizations to extract information regarding the assessment process. The result of the system’s evaluation has been very satisfactory, obtaining a good acceptance of the diverse users involved.

Informationen zur Finanzierung

Este trabajo ha sido financiado por el Gobierno Vasco (IT980-16), y el Vicerrectorado de Innovación, Compromiso Social y Acción Cultural de la Universidad del País Vasco a través del SAE-HELAZ (HBT-Adituak 2018-19/6).

Geldgeber

  • icerrectorado de Innovación, Compromiso Social y Acción Cultural de la Universidad del País Vasco Spain
    • HBT-Adituak 2018-19/6
  • Gobierno Vasco Spain
    • HBT-Adituak 2018-19/6

Bibliographische Referenzen

  • Biel, C., Cierniak, G., D. Johnson, M., Bull, S., & Hesse, F. W. (2016). Influencing Cognitive Density and Enhancing Classroom Orchestration. En P. Reimann, S. Bull, M. Kickmeier-Rust, R. Vatrapu, & B. Wasson (Eds.), Measuring and Visualizing Learning in the Information-Rich Classroom. doi:https://doi.org/10.4324/9781315777979
  • Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253-278. doi:https://doi.org/10.1007/BF01099821
  • Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. doi:https://doi.org/10.2307/249008
  • Djoub, Z. (2017). Assessment for learning: Feeding back and feeding forward. En E. Cano & G. Ion (Eds.), Innovative Practices for Higher Education Assessment and Measurement (pp. 19-35). Hershey PA, USA: IGI Global. doi:https://doi.org/10.4018/978-1-5225-0531-0
  • Dunlap, K., & Piro, J. S. (2016). Diving into data: Developing the capacity for data literacy in teacher education. Cogent Education, 3(1), 1132526. doi:https://doi.org/10.1080/2331186X.2015.1132526
  • Garrison, D. R., & Vaughan, N. D. (2007). Blended Learning in Higher Education: Framework, Principles, and Guidelines. San Francisco, CA, USA: John Wiley & Sons, Inc. doi:https://doi.org/10.1002/9781118269558
  • Gómez-Aguilar, D. A., García-Peñalvo, F.-J., & Therón, R. (2014). Analítica visual en e-learning. El Profesional de la Informacion, 23(3), 236-245. doi:https://doi.org/10.3145/epi.2014.may.03
  • Gómez-Aguilar, D. A., Hernández-García, Á., García-Peñalvo, F. J., & Therón, R. (2015). Tap into visual analysis of customization of grouping of activities in eLearning. Computers in Human Behavior, 47, 60-67. doi:https://doi.org/10.1016/j.chb.2014.11.001
  • Guàrdia, L., Crisp, G., & Alsina, I. (2017). Trends and Challenges of E-Assessment to Enhance Student Learning in Higher Education. En E. Cano & G. Ion (Eds.), Innovative Practices for Higher Education Assessment and Measurement (pp. 36-56). Hershey PA, USA: IGI Global. doi:https://doi.org/10.4018/978-1-5225-0531-0.ch003
  • Hattie, J. (2008). Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement. Abingdon, UK: Routledge.
  • Henri, M., Johnson, M. D., & Nepal, B. (2017). A Review of Competency-Based Learning: Tools, Assessments, and Recommendations: A Review of Competency-Based Learning. Journal of Engineering Education, 106(4), 607-638. doi:https://doi.org/10.1002/jee.20180
  • Idrissi, M. K., Hnida, M., & Bennani, S. (2017). Competency-Based Assessment: From Conceptual Model to Operational Tool. En E. Cano & G. Ion (Eds.), Innovative Practices for Higher Education Assessment and Measurement (pp. 55-78). Hershey PA, USA: IGI Global. doi:https://doi.org/10.4018/978-1-5225-0531-0.ch004
  • Kay, J., & Bull, S. (2015). New Opportunities with Open Learner Models and Visual Learning Analytics. En C. Conati, N. Heffernan, A. Mitrovic, & M. F. Verdejo, (Eds.), Artificial Intelligence in Education. AIED 2015 (pp. 666-669). Cham: Springer. doi:https://doi.org/10.1007/978-3-319-19773-9_87
  • Miller, H. G., & Mork, P. (2013). From Data to Decisions: A Value Chain for Big Data. IT Professional, 15(1), 57-59. doi:https://doi.org/10.1109/MITP.2013.11
  • Pardo, A., & Dawson, S. (2016). Learning Analytics: How can Data be used to Improve Learning Practice. En P. Reimann, S. Bull, M. Kickmeier-Rust, R. Vatrapu, & B. Wasson (Eds.), Measuring and Visualizing Learning in the Information-Rich Classroom (pp. 41-55). New York, USA: Routledge. doi:https://doi.org/10.4324/9781315777979
  • Romero, C., Romero, J. R., & Ventura, S. (2014). A Survey on Pre-Processing Educational Data. En A. Peña-Ayala (Ed.), Educational Data Mining (Vol. 524, pp. 29-64). Cham: Springer. doi:https://doi.org/10.1007/978-3-319-02738-8_2
  • Shields, M. (2005). Information Literacy, Statistical Literacy, Data Literacy. IASSIST Quarterly, 28(2), 7-14. doi:https://doi.org/10.29173/iq790
  • Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960-967. doi:https://doi.org/10.1016/j.promfg.2018.03.137
  • Vaughan, N. (2014). Student Engagement and Blended Learning: Making the Assessment Connection. Education Sciences, 4(4), 247-264. doi:https://doi.org/10.3390/educsci4040247
  • Villamañe, M., Alvarez, A., & Larrañaga, M. (2018). Supporting competence-based learning with visual learning analytics and recommendations. 2018 IEEE Global Engineering Education Conference (EDUCON) (pp. 1572-1575). USA: IEEE. doi:https://doi.org/10.1109/EDUCON.2018.8363421
  • Wahdain, E. A., & Ahmad, M. N. (2014). User Acceptance of Information Technology: Factors, Theories and Applications. Journal of Research and Innovation in Information Systems, 6, 17-25.
  • Yudelson, M. V., Koedinger, K. R., & Gordon, G. J. (2013). Individualized Bayesian Knowledge Tracing Models. En H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial Intelligence in Education. AIED 2013, (pp. 171-180). Berlin: Springer. doi:https://doi.org/10.1007/978-3-642-39112-5_18