在线学习行为评价框架:基于模糊层次分析法和模糊综合评价法

An online learning behavior evaluation framework:Based on the fuzzy analytic hierarchy process and the fuzzy synthetic evaluation method

针对智慧教育场景下,在线学习评价的全面性和有效性所面临的不足,构建了一个基于模糊层次分析法(fuzzy analytic hierarchy process,FAHP)和模糊综合评价法(fuzzy synthetic evaluation method,FSEM)的在线学习行为指标评价框架.框架以CIPP(context,input,process,product)教育评价模型为指导,融合教育评价标签类目体系,确立了学习探索、编程实践、知识掌握、创新协作和沟通交互这5个维度,并细化为二级指标和三级指标,从而实现了评价的全面覆盖.通过FAHP-FSEM计算各级指标权重,并进行一致性检验,确保了评价的科学性和合理性.以水杉在线平台为案例,利用大规模多源的过程化学习数据,从多维度、多视角进行了综合评价,并通过可视化大屏呈现学生画像和学习行为特征.所提框架为个性化学习效果的提升和在线教育改革提供了有力数据支持,具有广泛的应用前景.

To address the limitations currently experienced regarding the comprehensiveness and effectiveness of online learning evaluation in the smart education context,this paper proposes a novel framework for assessing online learning behavior based on the fuzzy analytic hierarchy process(FAHP)and the fuzzy synthetic evaluation method(FSEM).Drawing upon the CIPP(context,input,process,product)educational evaluation model and integrating the educational evaluation tag taxonomy system,the framework identifies five key dimensions:learning exploration,programming practice,knowledge acquisition,collaborative innovation,and communication interaction.These dimensions are further delineated into secondary and tertiary indicators to ensure comprehensive evaluation coverage.The framework utilizes FAHP-FSEM to determine the weights of each indicator level and employs consistency testing to validate the scientific and rational nature of the evaluation process.Implemented on the Shuishan Online platform,the framework leverages extensive multi-source process learning data to facilitate comprehensive evaluation from multiple perspectives and across various dimensions.Student profiles and learning behavior patterns are presented via a visual dashboard.This framework provides robust data support for enhancing personalized learning outcomes and advancing educational reform,demonstrating its broad applicability and potential.