三、研究内容
整个研究分为以下几个方面内容:
第一章阐述了认知负荷测评的意义、研究现状、存在的问题和研究目标及内容。
第二章着重探讨多任务作业中任务特征、个体特征因素对认知负荷的综合影响;确定对认知负荷有显著影响的因素和对认知负荷变化敏感的评估指标。
首先，探讨多任务作业中任务特征对认知负荷在各评估指标上变化的影响。任务特征包括任务复杂性和时间压力两个方面，认知负荷评估指标包括心理努力、任务主观难度、主任务反应时、主任务正确率、次任务正确反应时变化、注视时间、注视次数、眼跳距离、瞳孔直径共九个指标。利用方差分析和相关分析来探讨任务特征对认知负荷在评估指标上变化的显著性。
其次，探讨多任务作业中个体特征对认知负荷在各评估指标上变化的影响。个体特征包括作业自我效能、成就目标定向、认知风格和状态元认知，利用方差分析和相关分析探讨个体特征对上述九个认知负荷评估指标变化影响的显著性。
最后，分析多任务作业中任务特征和个体特征对认知负荷的综合影响。利用相关分析和结构方程建模分析方法，深入考察任务特征、个体特征对认知负荷评估指标的直接效应、间接效应和总效应;以及任务特征、个体特征对认知负荷的作用途径;确定影响认知负荷的主要因素和对认知负荷变化敏感的评估指标。在此基础上，对Sweller、Paas等人提出的认知负荷理论和结构模型进行验证。
第三章基于认知负荷敏感评估指标测试结果，开展多任务作业中认知负荷综合评估方法以及不同方法之间的差异比较，探讨多任务作业中认知负荷综合评估模型的构建方法研究。
首先，基于对认知负荷变化敏感的单维度评估指标的测量，运用因素分析、BP神经网络和自组织神经网络三种建模技术，对多任务作业中认知负荷进行多维度综合评估。
其次，对多任务作业中认知负荷测评方法的比较研究。以绝对误差、相对误差和均方误差作为评判标准，对单个评估指标与综合评估模型之间的测试结果进行比较，从中优选出最适合于多任务作业中认知负荷测评的方法。
第四章重点探讨持续作业过程中认知负荷变化预测模型构建方法。基于不同作业时间段的认知负荷评估指标测量数据，构建BP神经网络模型和Elman神经网络模型，对认知负荷在各评估指标上发生的变化进行预测。
第五章为研究结论和未来研究展望。
【注释】
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