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首页> 外文期刊>International Journal of Managing Information Technology >Classification of Questions and Learning Outcome Statements (LOS) into Bloom's Taxonomy (BT) by Similarity Measurements Towards Extracting of Learning Outcome from Learning Material
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Classification of Questions and Learning Outcome Statements (LOS) into Bloom's Taxonomy (BT) by Similarity Measurements Towards Extracting of Learning Outcome from Learning Material

机译:通过从学习材料中提取学习成果的相似性度量,将问题和学习成果陈述(LOS)分类到Bloom的分类法(BT)中

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Bloom's Taxonomy (BT) have been used to classify the objectives of learning outcome by dividing the learning into three different domains; the cognitive domain, the effective domain and the psychomotor domain. In this paper, we are introducing a new approach to classify the questions and learning outcome statements (LOS) into Blooms taxonomy (BT) and to verify BT verb lists, which are being cited and used by academicians to write questions and (LOS). An experiment was designed to investigate the semantic relationship between the action verbs used in both questions and LOS to obtain more accurate classification of the levels of BT. A sample of 775 different action verbs collected from different universities allows us to measure an accurate and clear-cut cognitive level for the action verb. It is worth mentioning that natural language processing techniques were used to develop our rules as to induce the questions into chunks in order to extract the action verbs. Our proposed solution was able to classify the action verb into a precise level of the cognitive domain. We, on our side, have tested and evaluated our proposed solution using confusion matrix. The results of evaluation tests yielded 97% for the macro average of precision and 90% for F1. Thus, the outcome of the research suggests that it is crucial to analyse and verify the action verbs cited and used by academicians to write LOS and classify their questions based on blooms taxonomy in order to obtain a definite and more accurate classification.
机译:Bloom的分类法(BT)已通过将学习划分为三个不同的领域来对学习结果的目标进行分类。认知领域,有效领域和精神运动领域。在本文中,我们将介绍一种将问题和学习结果陈述(LOS)归类为Blooms分类法(BT)并验证BT动词列表的新方法,这些方法已被院士引用并用于撰写问题和(LOS)。设计了一个实验来调查在问题和LOS中使用的动作动词之间的语义关系,以获得BT水平的更准确分类。从不同大学收集的775个不同动作动词的样本使我们能够测量动作动词的准确而清晰的认知水平。值得一提的是,自然语言处理技术被用来发展我们的规则,以便将问题归纳为大块以便提取动作动词。我们提出的解决方案能够将动作动词分类为认知域的精确级别。我们已经使用混淆矩阵对我们提出的解决方案进行了测试和评估。评估测试的结果表明,宏的平均精度为97%,F1为90%。因此,研究结果表明,至关重要的是分析和验证院士引用和使用的动作动词来编写LOS并基于大花生物分类法对他们的问题进行分类,以便获得确定且更准确的分类。

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