EDUCATIONAL ANALYTICS OF AN OPENCOURSEWARE
Analytics as one of the recent fields in technology-based learning offers many benefits to educators, instructors, and administrators to improve the efficiency and quality of alternative educational materials, and learning experience through tracking and storing students’ log data on web platforms over an extended period of time. This mixed-method study investigates students’ log data retrieved from the opencourseware (OCW) specifically launched for a required academic English speaking skills course offered at Middle East Technical University in Turkey with the aim of enhancing the quality and efficiency of the materials available for the course. By understanding the reasons behind students’ behaviors via the interviews conducted with 50 students on this online courseware, this study also aims to provide useful practical hints to the instructors and guide them to act on future decisions. The analyzed data is based on learner behavior with a specific emphasis on average view duration, likes and dislikes, and comments. This study can serve as a starting point to guide and provide the instructors and administrators about the future of the aforementioned course which is also offered in a rotational hybrid learning format where the effectiveness of online materials gain even more importance.
Agudo-Peregrina, A. F., Iglesias-Padas, S., Conde-Gonzalez, M. A., & Hernandez-Garcia, A. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542-550. http://dx.doi.org/10.1016/j.chb.2013.05.031.
American Psychological Association. (2010). Publication manual of the American Psychological Association (6th ed.). Washington, DC: American Psychological Association.
Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In LAK ‘12 Proceedings of the 2nd international conference on learning analytics and knowledge. New York: ACM.
Arroyo, I., Cooper, D. G., Burleson, W., Woolf, B. P., Muldner, K., & Christopherson, R. (2009). Emotion sensors go to school. AIED, 17-24.
Removed for review
Barber, R., & Sharkey, M. (2012). Course correction: Using analytics to predict course success. In LAK ‘12 Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 259–262). New York: ACM.
Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. US Department of Education, Office of Educational Technology (pp. 1-57).
Buckingham Shum, S., & Ferguson, R. (2012). Social learning analytics. Journal of Educational Technology & Society, 15(3). http://dx.doi.org/10.1145/2330601.2330616.
Bull, S., & Kay, J. (2016). SMILI☺: A framework for interfaces to learning data in Open Learner Models, learning analytics and related fields. Artificial Intelligence in Education, 26(1), 293-331.
Campbell, J.P., Oblinger, D.G. (2007). Academic Analytics, Educause.
Campus Backbone Network METU-NET | Computer Center. Retrieved 22 May 2018, from https://bidb.metu.edu.tr/en/campus-backbone-network-metu-net
Chapelle, C., & Douglas, D. (2006). Assessing language through computer technology. Cambridge, UK: Cambridge University Press.
Crede, M., & Niehorster, S. (2012). Adjustment to college as measured by the student adaptation to college questionnaire: A quantitative review of its structure and relationships with correlates and consequences. Educational Psychology Review, 24(1), 133-165. http://dx.doi.org/10.1007/s10648-011-9185-5.
Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research. Pearson Education, Inc.
Creswell, J. W. & Plano Clark, V. L. (2011). Designing and conducting mixed methods research. SAGE Publications, Inc.
Dörnyei, Z. (1998). Motivation in second and foreign language learning. Language Teaching, 31(3), 117-135. http://dx.doi.org/10.1017/S026144480001315X
Dörnyei, Z. (2007). Research methods in applied linguistics: Quantitative, qualitative, and mixed methodologies. Oxford, UK: Oxford University Press.
Dörnyei, Z., & Csizér, K. (2002). Some dynamics of language attitudes and motivation: Results of a longitudinal nationwide survey. Applied Linguistics, 23, 421–462. http://dx.doi.org/10.1093/applin/23.4.421
Dudeney, G., & Hockly, N. (2012). ICT in ELT: How did we get here and where are we going? ELT Journal, 66(4), 533-542. http://dx.doi.org/10.1093/elt/ccs050
Erten, I. H. (2014). The relationship between academic self-concept, attributions, and L2 achievement. System, 42, 391–401. http://dx.doi.org/10.1016/j.system.2014.01.006
Field, A. (2009). Discovering statistics for SPSS (3rd ed.). Los Angeles, CA: SAGE Publications.
Ghonsooly, B., Khajavy, G. H., & Asadpour, S. F. (2012). Willingness to communicate in English among Iranian non–English major university students. Journal of Language and Social Psychology, 3, 197-211. http://doi.org/10.1177/0261927X12438538.
Gibson, D., & de Freitas, S. (2015). Exploratory analysis in learning analytics. Technology, Knowledge and Learning, 21(1), 5–19.
Goggins, S. P., Galyen, K. D., Petakovic, E., & Laffey, J. M. (2016). Connecting performance to social structure and pedagogy as a pathway to scaling learning analytics in MOOCs: an exploratory study. Journal of Computer Assisted Learning, 32(3), 244-266.
Hockly, N. (2013). Interactive whiteboards. ELT Journal, 67(3), 354-358. http://doi.org/10.1093/elt/cct021
Long, P., & Siemens, G. (2011). Penetrating the fog, analytics in learning and education. Educause Review, 46(5), 31-40.
Macfadyen, L., P., & Dawson, S (2010). Mining LMS data to develop and “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588-599. http://dx.doi.org/10.1016/j.compedu.2009.09.008.
Mah, D. K. (2016). Learning analytics and digital badges: potential impact on student retention in higher education. Technology, Knowledge and Learning, 21(3), 285-305.
McCroskey, J. C., & Richmond, V. P. (1987). Willingness to communicate and interpersonal communication. In J. C. McCroskey & J. A. Daly (Eds.), Personality and interpersonal communication (129-156). Beverly Hills, CA: Sage.
Peng, J. E. (2011). Towards an ecological understanding of willingness to communicate in EFL classrooms in China. System, 40, 203–213. http://dx.doi.org/10.1016/j.system.2012.02.002.
Rienties, B., Hernandez Nanclares, N., Hommes, J., & Veermans, K. (2014). Understanding emerging knowledge spillovers in small-group learning settings: a networked learning perspective. In V. Hodgson, M. De Laat, D. McConnell, & T. Ryberg (Eds.). The Design, Experience and Practice of Networked Learning (Vol. 7, pp. 127-148). Dordrecht: Springer.
Sclater, N., A. Peasgood, & J. Mullan (2016). Learning analytics in higher education: A review of UK and international practice. Technical Report April, Jisc, London.
Shum, B., S., & Crick, D., R. (2012). Learning Dispositions and Transferable Competencies: Pedagogy, Modelling and Learning Analytics. Proc. 2nd International Conference on Learning Analytics & Knowledge, (29 Apr-2 May, Vancouver, BC). ACM Press: New York
Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior, 47, 157-167.
Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-driven research to support learning and knowledge analytics. Educational Technology & Society, 15(3), 133-148.
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