Understanding E-Learning Features in Online Courses

研究系统设置完成（具有必要功能的动态CMS网站）。实施了多个在线课程（算法和编程入门，人工智能的社会变革，软件工程），并为两个班级收集和分析数据。设计并分发了事前和事后匿名问卷。基于卡诺模型分析和人工智能语言模型分析的研究成果发表在两个国际会议和一个期刊上。学生对电子学习和在线课程的看法可以为课程设计和用户体验提供有价值的见解。学生们在每门课程之前和之后都对与在线课程满意度相关的12个特征进行了调查。还收集了文本评论。从客户满意度研究的卡诺模型进行事前和事后的比较分析的12个功能的短期和长期的课程。一个简单的神经网络在两门课程的自由形式评论上进行了训练，以创建语言模型（Word2Vec），并将结果与Kano模型结果进行比较。宏观转变（期望与消费经验）和微观转变（个别学生的要求内的转变）的结果进行了比较。

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.