Measuring Engagement to Assess Performance of Students in Distance Learning

Organizations continuously invest in the analysis of accumulated data by linking their exploitation to more effective decision making. Higher education and especially distance learning as a large data tank, follows the technological and financial needs to involve more flexible data analysis environments and better data-informed decision capabilities. Shrinking public subsidies drives higher education to form a more competitive learning environment. Satisfactory user experience and personalized services require a quantitative and qualitative analysis of students' daily action in learning environments. The increasing adoption of Learning Analytics (LA) and Educational Data Mining (EDM) push the development of novel approaches and advancements in education sector. At the same time, the rapid disclosure of hidden knowledge and the immediate presentation of results to optimize personalized decision making is a challenge for the competitiveness of distance learning. The objective of this study is the analysis of processes, technologies and resources used in an annual module at the Hellenic Open University to provide stakeholders with the visibility of interactions and hidden value in students' interactions. LA technologies are used on large set of data that has been collected by a Moodle platform and carefully analyzed. Students' logins, replies and quizzes are blended with the average grade of the main written exercises during the academic year. The contribution of this work is the useful observations from the students' educational on-line activities as predictive factors for their academic performance.