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• Without the feedback from face-to-face interactions, lecturers using virtual learning environments may find it difficult to identify and focus on students who are struggling in class.
• The Open University uses data from its virtual learning environment to pinpoint students who have an increased risk of dropping out of a class, as well as to study class structure and content with the goal of tailoring the learning experience to each student's unique profile.
• Predictive data can help instructors not only identify "at-risk" students but also use this enhanced feedback to improve the virtual learning experience.
Improving student retention is a key area where many universities can bolster student satisfaction, graduation rates, and make financial savings. To achieve these goals, it is necessary to understand both why and when students drop out, even when they do not make their problems or intentions clear to the lecturer. Post-module analysis can identify general problems with course structure or content that may be rectified for future sessions. In addition, during a session, a number of the failing students could be retained if they were offered appropriate assistance. The problem is how to identify these students in time to help them, even when they do not seek assistance.
|Item Type:||Journal Article|
|Copyright Holders:||2012 Annika Wolff and Zdenek Zdrahal|
|Keywords:||student retention; drop-out;student satisfaction; graduation; funding|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Kay Dave|
|Date Deposited:||26 Oct 2012 11:28|
|Last Modified:||04 Oct 2016 11:21|
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