Learning analytics in the EFL classroom: Technology as a forecasting tool
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Abstract
Ideal e-learning management systems (eLMSs) offer many applications given their huge dataset. They are a repository of information needed for the smooth functioning of the teaching-learning process in tech-enabled spaces. Databases created in these analytical systems permit not only the retrieval of data but also the updation of changing learning and pedagogical needs. This study aims to provide insights into predictive educational benefits to EFL classrooms as a result of the application of analytic methods by using grades in final exam scores of 25 EFL female learners of an undergraduate course at Prince Sattam bin Abdulaziz University (PSAU), Saudi Arabia. The study also analyzed the learning behaviours on Blackboard. Results show that lesson videos have the highest hit out of the three parameters (lesson videos, textual notes, practice exercises) offered on the e-learning platform (BlackBoard), followed by practice questions, and the least visitations were registered for the textual notes. Correlation between the learning videos (related to the three literature lessons taught) and final grades showed statistical significance at 95% confidence level, in addition to the factor of topic completion by the learners being a highly influencing factor in final grades. As a predictive model, Learning Analytics (LA) established that videos are a preferred way of learning English in the Saudi EFL context. The results of the study are likely to be of significance to course developers, teachers, and learners of EFL.
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Al-Ahdal, A. A. M. H. (2020). EBook interaction logs as a tool in predicting learner performance in reading. Asiatic: IIUM Journal of English Language and Literature, 14(1), 174-188.
Aghagolzadeh, F., Davari, H. (2017). English education in Iran: From ambivalent policies to paradoxical practices. In: Kirkpatrick, R. (eds) English Language Education Policy in the Middle East and North Africa. Language Policy, (Vol 13, pp 47–62). Springer, Cham. https://doi.org/10.1007/978-3-319-46778-8_4
Agudo-Peregrina, Á. F.,. Hernández-García, Á & Iglesias-Pradas, S. (2012). Predicting academic performance with learning analytics in virtual learning environments: A comparative study of three interaction classifications. International Symposium on Computers in Education (SIIE), Andorra la Vella, Andorra (pp. 1-6).
Al-khresheh, M. H. (2022). Revisiting the effectiveness of blackboard learning management system in Teaching English in the Era of COVID-19. World, 12(1), 1-14.
Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education, 17 (1). https://doi.org/10.1186/s41239-020-0177-
Amrieh, E. A., Hamtini, T., & Aljarah, I. (2016). Mining educational data to predict student’s academic performance using ensemble methods. International Journal of Database Theory and Application, 9(8), 119–136.
Athani, S. S., Kodli, S. A., Banavasi, M. N., & Hiremath, P. G. S. (2017). Student performance predictor using multiclass support vector classification algorithm. International Conference on Signal Processing and Communication (ICSPC),(pp. 341–346).
Bajpai, P., Chaturvedi, R., & Singh, A. (2019). Conjecture of scholars academic performance using machine learning techniques. International Conference on Cutting-Edge Technologies in Engineering (ICon-CuTE), (pp141–146).
Baker, R. S., Martin, T., & Rossi, L. M. (2016). Educational data mining and learning analytics. In André A. Rupp, Jacqueline P. Leighton (Eds), The Wiley handbook of cognition and assessment: Frameworks, methodologies, and applications, (pp. 379-396). Willy. https://doi.org/10.1002/9781118956588.ch16
Beer, C., Clark, K., & Jones, D. (2010). Indicators of engagement. In ascilite Sydney (pp. 75–86). Sydney.
Dietz-Uhler, B., & Hurn, J. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12(1), 17-26.
El Koshery, A., Abd El-Hafeez, T, Omar, A, & Eliwa, E. (2023). A prediction system using ai techniques to predict students’ learning difficulties using LMS for sustainable development at KFU. https://doi.org/10.1007/978-3-031-21438-7_2.
Erez, Miriam. (2012). Miron-Spector, E., Erez, M. and Naveh, E. (2012). To drive creativity add some conformity. Harvard business review.
Ghorbani, R., & Ghousi, R. (2020). Comparing different resampling methods in predicting students performance using machine learning techniques. IEEE Access, 8, 67899–67911.
Jayaprakash, S., Krishnan, S., & Jaiganesh, V. (2020). Predicting students academic performance using an improved random forest classifier. International Conference on Emerging Smart Computing and Informatics (ESCI), (pp. 238–243).
Jo, I.-H., Kim, D., & Yoon, M. (2014). Analyzing the log patterns of adult learners in LMS using learning analytics. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (LAK ’14) (183-187). https://doi.org/10.1145/2567574.2567616
Kotsiantis, S., Tselios, N., Filippidi, A., & Komis, V. (2013). Using learning analytics to identify successful learners in a blended learning course. International Journal of Technology Enhanced Learning, 5(2), 133-150.
Lee, D., Yeol, H., Chun-Yi, L & Reigeluth, C. (2018). Technology functions for personalized learning in learner-centered schools (197). Educational Technology Research and Development, 66, 1269-1302. https://doi.org/10.1007/s11423-018-9615-9.
Macfadyen, L., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54, 588-599. https://doi.org/10.1016/j.compedu.2009.09.008.
Mödritscher, F., Andergassen, M., & Neumann, G. (2013). Dependencies between eLearning Usage Patterns and Learning Results. In Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies (1-8). Graz, Austri. https://doi.org/10.1145/2494188.2494206
Mwalumbwe, I. & Mtebe, J. (2017). Using learning analytics to predict students’ performance in moodle learning management system: A case of Mbeya University of Science and Technology. Electronic Journal of Information Systems in Developing Countries. 79, 1-13. https://doi.org/10.1002/j.1681-4835.2017.tb00577.x.
Namoun, A., & Alshanqiti, A. (2021). Predicting Student performance using data mining and learning analytics techniques: a systematic literature review. Applied Sciences, 11(1), 237. https://doi.org/10.3390/app11010237
Naveh, G., Tubin, D., & Pliskin, N. (2012). Student satisfaction with learning management systems: a lens of critical success factors. Technology, Pedagogy and Education, 21(3), 337-350.
Reigeluth, C. M., Myers, R. D., & Lee, D. (2016). The learner-centered paradigm of education. In instructional-design theories and models, (Vol IV, pp. 5-32). Routledge.
Reinders, H. (2018). Learning analytics for language learning and teaching. Jalt Call Journal, 14(1), 77-86.
Saqr, M. & Fors (2017). How learning analytics can early predict under-achieving students in a blended medical education course. Medical Teacher, 39, 1-11. https://doi.org.10.1080/0142159X.2017.1309376.
Scheffel, M., Niemann, K., Pardo, A., Leony, D., Friedrich, M., Schmidt, K., Wolpers, M. & Kloos, C. (2011). Usage pattern recognition in student activities. In C. Kloos, D. Gillet, R. Crespo García, F. Wild, & M. Wolpers (Eds.), Towards Ubiquitous Learning (Vol. 6964, pp. 341–355). Springer Berlin Heidelberg. https://doi.org.10.1007/978-3-642-23985- 4_27
Whitmer, J., Fernandes, K., & Allen, W. R. (2012, August 13). Analytics in progress: Technology use, student characteristics, and student achievement. Educause Review. Retrieved from http://www.educause.edu/ero/article/analytics-progress-technology-usestudent-characteristics-and-student-achievement
Yu, T., & Jo, I. (2014). Educational technology approach toward learning analytics : relationship between student online behavior and learning performance in higher education. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (269-270). Indianapolis, IN, USA.
Zeide, E., & Nissenbaum, H. (2018). Learner privacy in MOOCs and virtual education. Theory and Research in Education, 16(3), 280-307.