Personalized Academic Thesis Management

Eythymios Tsatsaris, E. Sakkopoulos
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Abstract

Academic thesis is an integral part of most academic programs across disciplines. In lots of disciplines actually is also the longest and most important project during students’ life. However, our research finds that thesis management has not received the proper attention in learning management systems beyond mainly the inclusion of thesis in grading management systems. Grading of thesis is only the last step. Actually, thesis management should start when students shall choose the subject for their thesis and their thesis supervisor, it continues with thesis task and milestone progress management and finally closes with thesis submission, defense and grading. The proposed personalized software solution is a four-step system that (a) proposes thesis topics based on personalized profiles, (b) manages applications for thesis to the supervisor, (c) allows thesis progress management and (d) provides functions for its final delivery.The solution is scalable and dynamic so it can support a whole academic institution, any specific department, any laboratory or research group of scientists/supervisors and individual thesis supervisors. The approach has been evaluated in a real life online service by graduate and postgraduate students during their thesis selection process. Analysis of user experience has shown that the proposed approach matches thesis topics to student’s actual choices with accuracy greater than 75% within just the top 5 thesis topics suggested to each student. Moreover, usability of the system has been graded as “A” or “Excellent” based on SUS methodology.
个性化学术论文管理
学术论文是大多数跨学科学术课程的一个组成部分。在许多学科中,实际上也是学生一生中最长、最重要的项目。然而,我们的研究发现,除了主要将论文纳入成绩管理系统之外,论文管理在学习管理系统中并没有得到应有的重视。论文评分只是最后一步。实际上,论文管理应该从学生选择论文主题和论文导师开始,接着是论文任务和里程碑进度管理,最后是论文提交、答辩和评分。提出的个性化软件解决方案是一个四步系统,(a)根据个性化的配置文件提出论文主题,(b)管理导师的论文申请,(c)允许论文进度管理,(d)提供最终交付的功能。该解决方案是可扩展和动态的,因此它可以支持整个学术机构,任何特定部门,任何实验室或科学家/主管的研究小组和个人论文主管。该方法已经在现实生活中的在线服务中被研究生和研究生在他们的论文选择过程中进行了评估。对用户体验的分析表明,所提出的方法将论文主题与学生的实际选择相匹配,在推荐给每个学生的前5个论文主题中,准确率超过75%。此外,系统的可用性根据SUS方法被评为“A”或“优秀”。
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