Analysis of MOOC Quality Requirements for Landscape Architecture Based on the KANO Model in the Context of the COVID-19 Epidemic

Abstract

COVID-19 has had a severe impact on higher education worldwide, and Massive Open Online Courses (MOOCs) have become the best solution to reduce the impact of the COVID-19 on student learning. In order to improve the quality of MOOCs for Landscape Architecture, it is essential to fully understand the psychological needs of students learning online. A total of 119 undergraduates and postgraduates majoring in landscape architecture were selected as the research subjects, and 18 indicators falling into 5 functions, including course organization, course resources, learning environment, learning experience, and learning support were screened. Questionnaires based on the KANO model were prepared at wjx.cn for investigation through WeChat. Attributes were classified according to the traditional KANO model and the KANO model based on Better-Worse coefficients. The research showed that based on the classification results of the traditional KANO model, 17 of the 18 indicators were of the attractive quality factor and the rest were of the must-be quality factor. After reclassification using the KANO model based on Better-Worse coefficients, 4 of the 18 indicators were must-be quality factors, 6 were one-dimensional quality factors, 4 were attractive quality factors, and the rest 4 were indifferent quality factors. Compared to the traditional KANO model, the KANO model based on Better-Worse coefficients has better quality element classification discrimination. According to the KANO-based analysis, appropriate strategies for indicators shall be adopted for MOOC development according to the four types of quality requirements. The research can provide a basis for the development and optimization of MOOCs for landscape architecture so as to better meet the learning needs of students and achieve better learning effects.