Learner-User Satisfaction Survey in the AdaptWeb Platform using the Learner Choices from Learner-Driven Learning
DOI:
https://doi.org/10.5753/rbie.2020.28.0.420Keywords:
Learner-driven Learning, Ubiquitous e-Learning Systems, AdaptWeb, Satisfaction SurveyAbstract
In this paper, we review the definition of the learner choices from the Learner-driven Learning paradigm for e-learning systems. After this, we analyze how different categories of e-learning systems enable the user to make these choices, such as Serious Games. We present in detail how AdaptWeb platform makes available these choices to learner users. Additionally, we present a satisfaction survey performed after an online course on AdaptWeb platform. The survey questions were about making choices during learning and about the way AdaptWeb makes the choices available to learner-users. Summarizing the results, students enjoyed being able to make choices about their own learning and felt that this possibility was beneficial to their learning. Moreover, they liked the way AdaptWeb makes the choices available to students. Most of the students found the system easy to use, intuitive, and the student's choices were explicit and easy to take.
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Alexander, S., Kernohan, G., & Mccullagh, P. (2004) Self-directed and lifelong learning. Global Health Informatics Education, 109, 152–66.
Dias, S. A., & Wives, L. K. (2018). Definition of Learner Choices from Learner-Driven Learning for Ubiquitous e-Learning Systems and its Application in the AdaptWeb Platform. Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), v. 29, n. 1, p. 118. doi:10.5753/cbie.sbie.2018.118 [GS Search]
Dias, S. A., & Wives, L. K. (2018-b). Assessment of the Most Relevant Learning Object Metadata. Proceedings of the 10th International Conference on Computer Supported Education (CSEDU 2018), p. 175-182. doi:10.5220/0006660601750182 [GS Search]
Dias, S. A., & Wives, L. K. (2019). Recommender system for learning objects based in the fusion of social signals, interests, and preferences of learner users in ubiquitous e-learning systems. Personal and Ubiquitous Computing, 23(2), 249-268. doi: 10.1007/s00779-018-01197-7 [GS Search]
Downes, S. (2008). Connectivism: A Theory of Personal Learning. Retrieved from [Link]
Drachsler, H., Verbert, K., Santos, O., & Manouselis, N. (2015) Panorama of Recommender Systems to Support Learning. In: Ricci F., Rokach L., Shapira B. (eds) Recommender Systems Handbook (pp.421–451) Boston, MA: Springer.
Gates-Dell-EDUCAUSE - Bill & Melinda Gates Foundation, the Michael and Susan Dell Foundation, and EDUCAUSE (2017) A working definition of personalized learning. Retrieved from [Link]
Ginsberg, M. B. (2015) Excited to Learn: Motivation and Culturally Responsive Teaching. Corwin Press.
iNACOL - International Association for K-12 Online Learning; Patrick, S. Kennedy, K. Powell, A (2013) Mean What You Say: Defining and Integrating Personalized, Blended and Competency Education. Retrieved from: [Link]
Intayoad, W., Kamyod, C., & Temdee, P. (2020). Reinforcement Learning Based on Contextual Bandits for Personalized Online Learning Recommendation Systems. Wireless Personal Communications, 1-16. doi: 10.1007/s11277-020-07199-0
LEADLAB - The LEADLAB Project (2010) Retrieved from: [Link]
Manouselis, N., Vuorikari, R., & Assche, F. V. (2010) Collaborative recommendation of e-learning resources: an experimental investigation. Journal of Computer Assisted Learning, 26(4), 227–242. doi:10.1111/j.1365-2729.2010.00362.x
Mackness, J., Sui, M., & Roy, W. (2010) The ideals and reality of participating in a MOOC. Proceedings of the 7th International Conference on Networked Learning. University of Lancaster. [GS Search]
McClaskey, K. (2016). Developing the expert learner through the stages of personalized learning. Retrieved December, 21, 2018. [GS Search]
Miliband, D. (2006) Choice and voice in personalized learning. In: OECD, Personalizing education. doi: 10.1787/9789264036604-2-en
Passe, J. (1996) When students choose content: A guide to increasing motivation, autonomy, and achievement. Washington, DC: National Academies Press.
Reja, U., Manfreda, K. L., Hlebec, V., & Vehovar, V. (2003) Open-ended vs. Close-ended Questions in Web Questionnaires, Developments in Applied Statistics, 19, 159-77. [GS Search]
Song, Y. (2017) Personalized Learning on MOOCs. MS thesis. University of Lapland, Finland. Retrieved from: [Link]
Watkins, C., Carnell, E. & Lodge, C. (2007) Effective Learning in Classrooms. London, Paul Chapman Publishing. doi:10.4135/9781446211472
Zheng, Y., Mobasher, B. & Burke, R. (2014) Context Recommendation Using Multi-label Classification. Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). doi: 10.1109/WI-IAT.2014.110
Zhuhadar, L., & Butterfield, J. (2014) Analyzing Students Logs in Open Online Courses Using SNA Techniques. Proceedings of the Americas Conference on Information Systems, AMCIS, Savannah, Georgia, USA.
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Copyright (c) 2020 Alessandro da Silveira Dias, Leandro Krug Wives
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
