WebGIS课程教学评价是一个多目标非线性高维NP-难问题.首先,基于2013年到2017年的问卷调查,建立了包括面向教师和面向学生评价两个子系统的WebGIS课程教学评价指标体系.其次,构建了多目标非线性高维评价模型,并通过动态自适应教学优化算法(DSATLBO)进行求解.DSATLBO对传统的基于教与学优化算法进行了5个策略的改进:动态非线性自适应教学因子、课外辅导因子、动态自适应学习因子、多向学习因子和非支配排序因子.然后,利用调查问卷数据和DSATLBO算法对WebGIS教学绩效进行了全面评价.最后,利用Pareto前沿、两集合覆盖率和非支配解集间距3个指标分别比较了DSATLBO和NSGA-II算法所得的最优权值和加权分值,以验证了DSATLBO的性能.结果表明,DSATLBO可以沿Pareto前沿均匀分布.因此,DSATLBO可以有效地解决关于WebGIS课程的多目标非线性高维教学评价模型.本研究所提出的教学评价方法有助于全面反映课堂教学质量,指导学生的专业发展.%Teaching evaluation on a WebGIS course is a multi-objective nonlinear high-dimensional NP-hard problem. The index system for the teaching evaluation of a WebGIS course, including teacher- and student-oriented sub-systems, is first established and used for questionnaires from 2013 to 2017. The multi-objective nonlinear high-dimensional evaluation model is constructed and then solved via dynamic self-adaptive teaching-learning-based optimization (DSATLBO). DSATLBO is based on teaching-learning-based optimization with five improvements: dynamic nonlinear self-adaptive teaching factor, extracurricular tutorship factor, dynamic self-adaptive learning factor, multi-way learning factor, and non-dominated sorting factor. WebGIS teaching performance is fully evaluated based on questionnaires and DSATLBO. Optimal weights and weighted scores from DSATLBO are compared with those from the non-dominated sorting genetic algorithm-II using the Pareto front, coverage to two sets, and spacing of the non-dominated solution sets to validate the performance of DSATLBO. The results show that DSATLBO can be uniformly distributed along the Pareto front. Therefore, DSATLBO can efficiently and feasibly solve the multi-objective nonlinear high-dimensional teaching evaluation model of a WebGIS course. The proposed teaching evaluation method can help reflecting the quality of all aspects of classroom teaching and guide the professional development of students.
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