Hierarchical Collaborative Optimization Model for Landscape Engineering Curriculum Oriented to Cultivating Competency in Complex Systems
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Abstract
Addressing the contradiction between heterogeneous student backgrounds and the industry demand for interdisciplinary talents, as well as fragmented knowledge modules in current curriculum reform, this paper constructs a hierarchical collaborative optimization model for landscape engineering curriculum oriented to complex-system competency cultivation. The model consists of four interconnected modules: knowledge analysis, collaborative optimization, front-end feedback, and backend optimization. The knowledge parsing module uses a knowledge-element semantic network and the Louvain algorithm to deconstruct the course into three logical levels: basic construction, system integration, and complex decision-making. The collaborative optimization module employs PageRank and Jaccard coefficients with dynamic weight allocation to generate a horizontal collaborative matrix and bind highly correlated knowledge elements. The front-end feedback module runs K-means clustering each teaching week, while the back-end feedback module runs a multi-constraint genetic algorithm after each course module. The experimental group achieves an average score of 88.58, and cross-knowledge collaborative calls increase by 174.8% compared with the control group. The model offers a transferable engineering-education optimization framework for complex-system courses, including electromagnetic and communication-engineering curricula.
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