E-self-assessment and e-feedback questionnaires: an application in Moodle
E-self-assessment and e-feedback questionnaires: an application in Moodle
Main Article Content
Abstract
In Learning Management Systems (LMS), the learning resources that students value most highly are selfassessment questionnaires (quizzes) that include automatic feedback to their responses in real-time. These systems can facilitate the development of conceptual and procedural competences. Our study therefore sets out to establish whether this tool and the frequency with which it is used will increase student learning outcomes and student satisfaction with the teaching process. A longitudinal study is conducted with a sample of 179 Health Science students on the Moodle Platform v.3.1. Both quantitative and qualitative techniques are applied to the analysis of the data. The results indicated that when the self-assessment questions were incorporated in instructional videos, the learners not only obtained better results but also increased their level of satisfaction with the degree course. In addition, students in both (the experimental and the control) groups considered that the use of videos incorporated in the LMS facilitated their conceptual understanding and respected their pace of learning. In summary, students valued the inclusion of automatic feedback (both within videos and self-assessment questionnaires) in real time as a good technique for the personalization of learning.
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NY, US: Routledge/Taylor & Francis Group.
Cerezo, R., Sánchez-Santillan, M., Paule-Ruiz, M.P., y Núñez, J.C. (2016). Students’ LMS
interaction patterns and their relationship with achievement: a case study in higher
education. Computer and Education, 96, 42-54. doi: 10.1016/j.compedu.2016.02.006
Churches, A. (2009). La Taxonomía de Bloom para la Era Digital. Recuperado de
http://eduteka.icesi.edu.co/articulos/TaxonomiaBloomDigital.
Cloude, E.B., Taub M., y Azevedo R. (2018) Investigating the Role of Goal Orientation:
Metacognitive and Cognitive Strategy Use and Learning with Intelligent Tutoring
Systems. En R. Nkambou., R. Azevedo., y J. Vassileva (Eds.), Intelligent Tutoring
Systems. ITS 2018. Lecture Notes in Computer Science (Vol. 10858, pp.44-53).
Cham: Springer. doi: 10.1007/978-3-319-91464-0_5
D. Hacker, J. Dunlosky, A. Graesser (Eds.), Handbook of Metacognition in Education (pp. 299-
315). New York: Routledge.
Friese, S. (2017). ATLAS.ti. 8 Windows Quick Tour. Software for Qualitative and Mixed Methods
Data Analysis. Berlin: Scientific Software Development GmbH.
Harley, J.M., Taub, M., Azevedo, R., y Bouchet, F. (2018). “Let’s Set Up Some Subgoals”:
Understanding Human-Pedagogical Agent Collaborations and Their Implications for
Learning and Prompt and Feedback Compliance. IEEE Transactions on Learning
Technologies, 11(1), 54-66 doi: 10.1109/TLT.2017.2756629
Hattie, J., y Gan, M (2011). Instructions based on feedback. En R.E. Mayer y P. A. Alexander,
Handbook of Research on Learning and Instruction: Educational Psychology Handbook
series (pp. 249-271). New York: Routledge.
Hattie, J., y Timperley, H. (2007). The power of feedback. Review of Educational Research, 77,
(81–112). doi: 10.3102/003465430298487
Lau, C., Sinclair, J., Taub, M., Azevedo, R., y Jang, E.E. (2017). Transitioning Self-Regulated
Learning Profiles in Hypermedia-learning Environments. Proceedings of the Seventh
International Learning Analytics & Knowledge Conference. LAK,'17 (pp. 198-202).
Vancouver: Canada. doi: 10.1145/3027385.3027443.
Price, M.J., Mudrick, N.V., Taub, M., y Azevedo, R. (2018). The Role of Negative Emotions and
Emotion Regulation on Self-Regulated Learning with MetaTutor. En R. Nkambou.,
Azevedo R., J. Vassileva (Eds.), Intelligent Tutoring Systems. ITS 2018. Lecture Notes in
Computer Science (Vol. 10858, pp. 170-179). Springer: Cham
Sáiz, M.C. (2018), E- Project Based Learning en Terapia Ocupacional: una aplicación en la
asignatura “Estimulación Temprana. Servicio de Publicaciones de la Universidad de
Burgos, Burgos.
Sáiz, M. C., y Marticorena, R. (2016). Metacognition. Self-Regulation and Feedback for ObjectOriented
Programming Problem-Solving. En J. Benson (Ed.), Metacognition: Theory.
Performance and Current Research (pp. 43-94). New York: Nova.
Sáiz, M.C., Marticorena, R., García-Osorio, C.I., y Díez-Pastor, J.F. (2017). How do B-learning
and learning patterns influence learning outcomes? Frontiers in Psychology, 8(745), 1-
13. doi: 10.3389/fpsyg.2017.00745
Sáiz, M. C., y Montero, E. (2015). Metacognition, self-regulation and assessment in problemsolving
processes at university. A. Peña-Ayala (Ed.), In Metacognition: Fundaments,
Applications, and Trends (pp. 107-133). Cham: Springer. doi: 10.1007/978-3-319-
11062-2_5
Sinclair, J., Jang E.E., Azevedo R., Lau C., Taub M., y Mudrick N.V. (2018) Changes in Emotion
and Their Relationship with Learning Gains in the Context of MetaTutor. En R.
Nkambou., R. Azevedo., y J. Vassileva. (Eds.), Intelligent Tutoring Systems. ITS 2018.
Lecture Notes in Computer Science (Vol. 10858, pp.202-211). Cham: Springer. doi.
10.1007/978-3-319-91464-0_20
Saeed, N., Yang, Y., y Sinnappan, S. (2009). Emerging Web Technologies in Higher Education: A
Case of Incorporating Blogs, Podcasts and Social Bookmarks in a Web Programming
Course based on Students’ Learning Styles and Technology Preferences. Educational
Technology & Society, 12(4), 98-109. Recuperado de https://goo.gl/wuA1e
Statistical Package for the Social Sciences (SPSS) (Version 24). IBM. Madrid. Spain:
Taub, M., y Azevedo, R. (2016) Using Eye-Tracking to Determine the Impact of Prior Knowledge
on Self-Regulated Learning with an Adaptive Hypermedia-Learning Environment. En A.
Micarelli., J. Stamper., y K. Panourgia (Eds.), Intelligent Tutoring Systems. ITS 2016.
Lecture Notes in Computer Science (Vol. 9684, pp.34-47). Cham: Springer. doi:
10.1007/978-3-319-39583-8_4
Taub, M., Azevedo, R., Mudrick, N.V., Clodfelter, E., y Bouchet, F. (2014). Can Scaffolds from
Pedagogical Agents Influence Effective Completion of Sub-Goals during Learning with
a Multi-Agent Hypermedia-Learning Environment? Learning and becoming in practice:
The International Conference of the Learning Sciences (ICLS). International Society of
the Learning Sciences, 2 (pp.1052-1056). Boulder, CO: United States.
Taub M., Mudrick N.V., Rajendran R., Dong Y., Biswas G., y Azevedo R. (2018). How Are
Students’ Emotions Associated with the Accuracy of Their Note Taking and
Summarizing During Learning with ITSs? En R. Nkambou., R. Azevedo., y J. Vassileva
(Eds.), Intelligent Tutoring Systems. ITS 2018. Lecture Notes in Computer Science (Vol.
10858, pp.233-242). Cham: Springer. doi: 10.1007/978-3-319-91464-0_23
Zimmerman, B.J., y Moylan, A. (2009). Self-regulation: Where metacognition and motivation
intersect. En D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), The educational
psychology series. Handbook of metacognition in education (pp. 299-315). New York,
NY, US: Routledge/Taylor & Francis Group.