Understanding E-Learning Features in Online Courses

点击翻译按钮获取中文摘要

The research system setup was completed (dynamic CMS website with necessary functionality). Multiple online courses (Introduction to Algorithms and Programming, Social Change with AI, Software Engineering) were implemented and data was collected and analyzed for two classes. Anonymous ex-ante and ex-post questionnaires were designed and distributed. The research results, stemming from the Kano model analysis and AI language model analysis, were published in two international conferences and one journal.The perception of e-learning and online courses by students can provide valuable insights into course design and user experience. Students were surveyed on the same 12 features related to online course satisfaction before and after each course. Textual comments were also gathered. The Kano model from customer satisfaction research was used to perform an ex-ante and ex-post comparative analysis for the 12 features of both short-term and long-term courses. A simple neural network was trained on freeform comments for both courses to create language models (Word2Vec) and compare the findings with the Kano model results. The results of the macro shift (expectations vs. consumption experience) and micro shift (individual student’s shift within a requirement) were compared.