Classification of Questions and Learning Outcome Statements (LOS) into Bloom's Taxonomy (BT) by Similarity Measurements Towards Extracting of Learning Outcome from Learning Material

Shadi Diab1 and Badie Sartawi2 1 Information and Communication Technology Center, Al-Quds Open University, Ramallah - Palestine 2Associate Professor of Computer Science, Al-Quds University, Jerusalem - Palestine 

ABSTRACT 

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. 

KEYWORDS 

Learning outcome; Natural Language Processing, Similarity Measurement; Questions Classification

More Details:  

http://aircconline.com/ijmit/V9N2/9217ijmit01.pdf