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Analyzing Learners Behavior and Resources Effectiveness in a Distance Learning Course: A Case Study of the Hellenic Open University  

Alachiotis, Nikolaos S. (Educational Content, Methodology and Technology Laboratory, Hellenic Open University)
Stavropoulos, Elias C. (Educational Content, Methodology and Technology Laboratory, Hellenic Open University)
Verykios, Vassilios S. (Big Data Analytics and Anonymization Laboratory, School of Science and Technology, Hellenic Open University)
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Journal of Information Science Theory and Practice / v.7, no.3, 2019 , pp. 6-20 More about this Journal
Learning analytics, or educational data mining, is an emerging field that applies data mining methods and tools for the exploitation of data coming from educational environments. Learning management systems, like Moodle, offer large amounts of data concerning students' activity, performance, behavior, and interaction with their peers and their tutors. The analysis of these data can be elaborated to make decisions that will assist stakeholders (students, faculty, and administration) to elevate the learning process in higher education. In this work, the power of Excel is exploited to analyze data in Moodle, utilizing an e-learning course developed for enhancing the information computer technology skills of school teachers in primary and secondary education in Greece. Moodle log files are appropriately manipulated in order to trace daily and weekly activity of the learners concerning distribution of access to resources, forum participation, and quizzes and assignments submission. Learners' activity was visualized for every hour of the day and for every day of the week. The visualization of access to every activity or resource during the course is also obtained. In this fashion teachers can schedule online synchronous lectures or discussions more effectively in order to maximize the learners' participation. Results depict the interest of learners for each structural component, their dedication to the course, their participation in the fora, and how it affects the submission of quizzes and assignments. Instructional designers may take advice and redesign the course according to the popularity of the educational material and learners' dedication. Moreover, the final grade of the learners is predicted according to their previous grades using multiple linear regression and sensitivity analysis. These outcomes can be suitably exploited in order for instructors to improve the design of their courses, faculty to alter their educational methodology, and administration to make decisions that will improve the educational services provided.
distance learning; learning analytics; course dedication; forum effectiveness; Excel; Moodle;
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