Recommendations as a Key Aspect for Online Learning Personalization: Perceptions of Teachers and Students
Higher education students are increasingly enrolling in online courses, they are, at the same time, generating data about their learning process in the courses. Data collected in those technology enhanced learning spaces can be used to identify patterns and therefore, offer recommendations/personalized courses to future online students. Moreover, recommendations are considered key aspects for personalization in online learning. Taking into account the above mentioned context, the aim of this paper is to explore the perception of higher education students and teachers towards receiving recommendations in online courses. The study was carried out with 322 students and 10 teachers from two different faculties (Engineering and Education) from Mondragon University. Online questionnaires and face to face interviews were used to gather data from the participants. Results from the questionnaires show that most of the students would like to receive recommendations in their online courses as a guide in their learning process. Findings from the interviews also show that teachers see recommendations useful for their students’ learning process. However, teachers believe that specific pedagogical training is required. Conclusions can also be drawn as regards the importance of personalization in technology enhanced learning. These findings have significant implications for those who train online teachers due to the fact that pedagogy should be the driven force and further training on the topic could be required. Therefore, further research is needed to better understand the impact of recommendations on online students’ learning process and draw some conclusion on pedagogical concerns.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1127511Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 778
 B. Means. Learning Online. What research tells us about whether, when and how. New York: Routledge, 2014.
 S. Kopeinik, D. Kowald & E. Lex. Which algorithm suit which learning environments? A comparative study of recommender systems in EC-TEl, 2016, Lyon.
 C. Gunn, et al. A practitioner’s guide to learning analytics. In T. Reiners, B. R. von Konsky, D.Gibson, V. Chang, L. Irving, & K. Clarke (Eds.), Globally connected, digitally enabled. Proceedings ascilite, 2015, pp. 672-675.
 Z. Papamitsiou & A. Economides. Learning Analytics and Educational Data Mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society, 2014, 17(4), 49-64.
 J. Baalsrud Hauge et al. Learning Analytics architecture to scaffold learning experience through technology-based methods. International Journal of Serious Games, 2015, 2(1), 29-44.
 W. Greller & H. Drachsler Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 2012, 15 (3), 42-57.
 M.K. Khribi, M. Jemni & O. Automatic Recommendations for E-learning Personalization Based on Web Usage Mining Techniques and Information Retrieval. Educational Technology & Society, 2009, 12 (4), 30-42.
 D. Gasevic, S. Dawson & G. Siemens. Let’s not forget: Learning analytics are about learning. TechTrends, in press
 A. Mavroudi, M.Giannakos & J. Krogstie. Combining adaptive learning with learning analytics: precedents and directions., EC-TEl 2016 , Lyon
 Y. Chen, E.K. Garcia, M.R. Gupta, A. Rahimi, & L. Cazzanti. Similarity-based Classification: Concepts and Algorithms. Journal of Machine Learning Research, 2009, 10, 747-776.