Digital Teaching Design and Implementation Strategies for Plant Landscape Courses in Colleges and Universities in Smart Education Environments

Abstract

As a core course in horticulture-related majors, plant landscape courses play an important role in the cultivation of horticultural professionals. To address issues such as insufficient knowledge point connections in plant landscape course teaching under smart education environments, this paper constructs a plant landscape course knowledge graph and, based on this, builds a plant landscape course resource recommendation model using knowledge graphs and convolutional neural networks. Using a feature extraction module, feature vectors representing course attributes and user attributes are extracted, and higher-order aggregation between vectors is performed from multiple perspectives to achieve more precise plant landscape course resource recommendations for users. A digital teaching practice for plant landscape courses was conducted at a university in Y City, T Province. Among the experimental class students who applied this model for digital teaching, only one student had a teaching satisfaction score below 60, significantly fewer than in the control class. After the experiment, the average score of the experimental class students reached 101.18 points, which was 13.46 points higher than that of the control class, proving that the model proposed in this paper has a positive promotional effect on student learning in plant landscape course teaching.