Research on Digital Reform Strategy and Teaching Platform Development for Plant Landscape Courses in Colleges and Universities for Smart Education

The problems of knowledge overload and learning disorientation triggered by knowledge fragmentation are the focus and difficulty of digital reform in higher education. In this paper, learning objects consist of knowledge points and courses, and the learning path is measured as a sequence of knowledge points. Combined with the results of predicting the answers to the exercises, we model the knowledge points and courses, set the ontology inference rules, and construct the online platform of learning paths and resources based on ant colony algorithm for plant landscape courses. A university plant landscape program was selected, and student data were collected for learning path planning application and teaching experiments and questionnaire surveys were conducted. When analyzing the students’ potential knowledge mastery status, it was found that 143 of them were mastering all knowledge attributes, while a total of 36 students failed to master all knowledge attributes. Finally, an efficient learning path of KS-6→ KS-4→ KS-5→ KS-3→ KS-1 was given based on the analysis results. In the dimensions of “knowledge and skills”, “process and method” and “affective attitude” of the students in the experimental class and the control class, the comparison of the different modes of teaching yielded significant differences. The mean values of the experimental class are 3.90, 3.93 and 3.83, which are all greater than the mean values of the traditional teaching class. It can be proved that compared with the traditional teaching mode, the blended teaching mode based on the “learning path planning platform” proposed in this paper is more capable of providing students with scientific, reasonable and efficient learning path recommendations.