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Classification of Questions and Learning Outcome Statements (LOS) Into Blooms Taxonomy (BT) By Similarity Measurements Towards Extracting Of Learning Outcome from Learning Material

机译:问题分类和学习成果声明(LOs)   Blooms Taxonomy(BT)通过相似性测量提取   学习材料的学习成果

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摘要

Blooms Taxonomy (BT) have been used to classify the objectives of learningoutcome by dividing the learning into three different domains; the cognitivedomain, the effective domain and the psychomotor domain. In this paper, we areintroducing a new approach to classify the questions and learning outcomestatements (LOS) into Blooms taxonomy (BT) and to verify BT verb lists, whichare being cited and used by academicians to write questions and (LOS). Anexperiment was designed to investigate the semantic relationship between theaction verbs used in both questions and LOS to obtain more accurateclassification of the levels of BT. A sample of 775 different action verbscollected from different universities allows us to measure an accurate andclear-cut cognitive level for the action verb. It is worth mentioning thatnatural language processing techniques were used to develop our rules as toinduce the questions into chunks in order to extract the action verbs. Ourproposed solution was able to classify the action verb into a precise level ofthe cognitive domain. We, on our side, have tested and evaluated our proposedsolution using confusion matrix. The results of evaluation tests yielded 97%for the macro average of precision and 90% for F1. Thus, the outcome of theresearch suggests that it is crucial to analyse and verify the action verbscited and used by academicians to write LOS and classify their questions basedon blooms taxonomy in order to obtain a definite and more accurateclassification.
机译:Blooms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编写问题和(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,以便将问题分解成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,分析和验证院士用语言动词并使用的动作来编写LOS,并根据Blooms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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    Diab, Shadi; Sartawi, Badie;

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