计算机科学 ›› 2026, Vol. 53 ›› Issue (8): 9-19.doi: 10.11896/jsjkx.250600231
赖英旭, 张雨薇, 庄俊玺
LAI Yingxu, ZHANG Yuwei, ZHUANG Junxi
摘要： 随着在线教育和个性化教育的不断发展,如何利用海量在线学习数据构建准确、个性化的学习者画像,成为当前的研究热点。针对现有学习者画像相关研究存在忽略个体差异性、缺乏系统的理论指导等问题,提出一种面向学习者个性化画像的SOR标签体系构建与生成方法。该方法首先结合SOR理论构建个性化标签体系,提取个性化标签,挖掘学习者的个体差异性;其次,基于多标签分类模型,学习特征和个性化标签之间的复杂关系,实现对个性化标签的精准预测以及动态生成画像标签;最后,基于SOR理论构建学习者画像,揭示环境刺激、个体认知与学习行为之间的关系。实验基于公开MOOC数据集展开,通过对教学真实场景——辍学率的预测来验证本文标签体系的有效性,结果表明,所提方法的预测准确率较次优标签体系提升了0.91%,验证了所提方法在学习者个性化标签挖掘与标签体系构建方面的有效性。
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