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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.

Water bodies in urban parks are important for scenic and recreational areas, yet algal bloom problems seriously affect public use; therefore, it is important to study the features of algal density (AD) changes and environmental driving factors (EDFs) for water body management. In this study, five scenic water bodies in urban parks of Xinxiang City are taken as the objects for studying the AD and nine environmental indicators from March to October 2021, in accordance with time-series monitoring. The features of AD change in different layers of the water bodies are analyzed, and the main environmental impact factors of AD changes are screened by Pearson correlation analysis and principal components analysis (PCA), with main EDFs further extracted according to multiple linear regression analysis (MLRA), and multiple regression equation established. According to the data, ADs at different depth layers increase at first and then decrease with time, reaching the peak in August. According to the PCA, three principal components (PCs) are extracted in the 0.5 m and 1.0 m water layer, the variance contribution of which is 87.8% and 87.3%, respectively. The variance contribution of four PCs extracted in the 1.5 m water layer is 81.7%. After MLRA, it is found that the main EDFs of algal density in the 0.5 m water layer are electrical conductivity (EC), dissolved oxygen (DO), and water temperature (WT), in the 1.0 m water layer are WT and DO, and in the 1.5 m water layer are WT, DO, total nitrogen (TN), and EC. Generally speaking, WT and DO are decisive factors affecting AD. The EDFs’ leads to the AD changes in different water layers are analyzed, and it is proved that stratification occurs in scenic water bodies in urban parks. This study is expected to provide basic data and a theoretical basis for ecosystem system protection and water quality management of scenic water bodies in urban parks.

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China has been implementing a brand-new reform in agricultural education and teaching, and the construction of a first-class curriculum is an important guarantee for improving teaching quality and talent training. In line with the survey results of senior students from the field of landscape architecture, 15 frequent elements are selected, namely, teaching team, teaching strategy, teaching method, curriculum ideology and politics, online teaching, offline teaching informatization, teaching material resources, hardware resources, social resources, curriculum structure, teaching process, curriculum organization, applicability, foresight, innovation, and practicality. According to affiliation relationships, they are then classified into five clusters, which are curriculum intelligence support, informatization, resources, normalization, and content. By adopting the analytic network process method and using the super decision software, the hierarchical network model reflecting the dependence and feedback relationship between elements is established. The research results show that, among the five clusters, curriculum content and intelligence support weight relatively are high, which account for 67% of the total weight. The elements of the teaching team, online teaching, teaching material resources, teaching process normalization, and applicability and practicality of curriculum content weigh high, respectively, among the clusters. In the overall ranking of the system weight, the weights of three elements exceed 0.1, namely, the teaching team, content practicability, and teaching process normalization. The weights of the top eight elements account for approximately 85% of the total weight. This study can be used as a reference for the optimal allocation of curriculum construction resources.