OU Analyse is a system powered by machine learning methods for early identification of students at risk of failing. All students with their risk of failure in their next assignment are updated weekly and made available to the course tutors and the Student Support Teams to consider appropriate support. The overall objective is to significantly improve the retention of OU students.
This article focuses on the success of African American students at black colleges and universities and how remedial programs can help students to be more successful in college.
HT2 Labs announces a 3-part Open Learning Experience (OLX) exploring how to use data to create engaging learning experiences that boost retention and help drive ROI.
Students interacting with universities often leave behind a virtual footprint that is used to gauge how well the university has managed to help and prepare these students. Learning analytics is using this data to analyze, measure, collate data, and more about the progress made by both students and educators.
Following the Data Matters conference, Paul Bailey from Jisc looks at the potential for learning analytics to improve teaching practice and the student experience.
This blog contains text and some of the images used for the invited seminar, UNESCO Chair on Open Distance Learning, University of South Africa (UNISA), Wednesday 12 August 2020
Thousands of students and teachers across Wales will benefit from cutting-edge data analytics technology to improve student engagement, retention and performance as a result of a funding boost to be announced today by the Higher Education Funding Council for Wales (HEFCW) and Jisc.
P. Adamopoulos. ICIS, Association for Information Systems, (2013)The findings of our analysis illustrate that Professor(s) is the most important factor in online course retention and has the largest positive effect on the probability of a student to successfully complete a course. The sentiment of students for Assignments and Course Material also has positive effects on the successful completeness of a course whereas the Discussion Forum has a positive effect on the probability to partially complete a course. Furthermore, self-paced courses have a negative effect, compared to courses that follow a specific timetable. In addition, the difficulty, the workload, and the duration of a course have a negative effect. On the other hand, for the more difficult courses, self-paced timetable, longer duration in weeks, and more workload have a positive effect on the probability to successfully complete a course. Besides, final exams and projects, open textbooks, and peer assessment have also positive effects. Moreover, whether a certificate is awarded upon the successful completion of a course also affects retention. Additionally, the better a university is considered (i.e. higher ranking), the more likely that a student will successfully complete a course. Further, our results illustrate that the courses which belong to the academic disciplines of Business and Management, Computer Science, and Science have a positive significant effect in contrast to courses in other disciplines (i.e. Engineering, Humanities, and Mathematics). Finally, attrition was not found to be related with student characteristics (i.e. gender, formal education)..
M. Allen, D. Armstrong, M. Reid, and C. Riemenschneider. Proceedings of the special interest group on management information system's 47th annual conference on Computer personnel research, page 95--100. New York, NY, USA, ACM, (2009)