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

TL;DR
This paper presents a new NLP-based method to classify questions and learning outcome statements into Bloom's Taxonomy levels by analyzing action verbs, achieving high precision and F1 scores.
Contribution
Introduces a semantic relationship approach using NLP techniques to accurately classify questions and LOS into Bloom's Taxonomy levels, verifying the validity of BT verb lists.
Findings
Achieved 97% precision in classification
Attained 90% F1 score in evaluation
Validated the importance of verb analysis for accurate classification
Abstract
Blooms 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…
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