【作 者】：
郑隆威， 冯园园， 顾小清
【关 键 词 】：
学习分析； 学习成果； 布鲁姆分类学； 自然语言处理； 词向量
【栏 目】：
课程与教学
【中文摘要】：
学习成果（Learning Outcome）描述了期望学习者在完成课程后所习得的知识、技能和能力。众多研究呼吁课程管理者需要依照布鲁姆分类学制定学习成果，从而使学习成果在认知维度上是“可测量的”，但很少有研究关注学习成果的描述与所测量的结果之间是否匹配。本文试图借助学习分析方法对这一问题进行探究。本文利用自然语言处理方法测试动词、情境信息等能否有效地标示学习成果的认知类型。研究发现，动词依然是布鲁姆分类学中最关键的特征，当该特征与学习内容、情境信息结合时，能够更准确地标示学习成果的认知类型。此外，本文讨论了学习成果在布鲁姆分类学中存在的不明确性，发现这种不明确性能够带来更多的上下文信息，从而更全面地辅助学习成果的制定。
【英文摘要】：
Learning outcome describes the knowledge, skills, and abilities that learners are expected to acquire at the end of the courses. Many studies have advocated that learning outcomes need to be formulated in accordance with Bloom's taxonomy so that learning outcomes are "measurable" in cognitive domain, but few studies have focused on whether the description of learning outcomes matches the measured results. This paper attempts to explore this problem by means of learning analytics. This paper uses natural language processing to test whether verbs and contextual information etc. can mark the cognitive types of learning outcomes effectively. It is found that verbs are still the most critical feature in Bloom's taxonomy. When combined with learning content and contextual information, verbs can indicate the cognitive types of learning outcomes more accurately. In addition, this paper discusses the ambiguity of learning outcomes in Bloom's taxonomy, and finds that this ambiguity can bring more contextual information, thus assisting the formulation of learning outcomes more comprehensively.