Analyzing the quality of artificial intelligence final examination questions according to bloom's taxonomy and syllabus contents / Syazatul Nor Azah Mohamed Mahtar

Mohamed Mahtar, Syazatul Nor Azah (2015) Analyzing the quality of artificial intelligence final examination questions according to bloom's taxonomy and syllabus contents / Syazatul Nor Azah Mohamed Mahtar. Degree thesis, Universiti Teknologi MARA, Perak.

Abstract

As the technology of Artificial Intelligence (AI) expanded widely, AI has been used in many fields. It has become one of the most critical courses in the area of Computer Science and thus being offered in many universities worldwide. To ensure Al knowledge is mastered well by students, their understanding on this course should be measured efficiently. To achieve this, the preparations of the examination questions should follow certain guidelines or requirements for example the syllabus contents and the Bloom's Taxonomy model. The main objective of this project is to develop a system that acts as an analyzer to analyze the quality of the final examination question papers according to the Bloom's Taxonomy and syllabus contents. In UiTM, the process of analyzing the final examination question papers is currently done manually by the Examination Unit staff. Problems can occur because there are many sets of final examination questions at one time and obviously a manual check will not give a 100% accurate results. Therefore, there is a need of a system that can analyze the quality of the final examination questions according to the syllabus contents and Bloom's Taxonomy model. The methods used in this proposed project are the Fuzzy Logic and Keyword Matching Technique. Fuzzy Logic is used to classify the keywords to six different levels in Bloom's Taxonomy model and different topics in the Fundamentals of Artificial Intelligence course (UiTM Computer Science Degree Programme). The Keyword Matching Technique is used to find the matching keyword in the proposed final examination questions. The keyword found in the final examination questions were compared with the keyword of Bloom's Taxonomy and syllabus contents that were stored in the database. After that, the compliance percentage of the final examination questions based on the Bloom's Taxonomy model and syllabus contents were generated. High quality final examination question papers will follow closely the Bloom's Taxonomy and have a fair distribution of questions based on the syllabus contents. In this project, it was observed that Bloom's Taxonomy conformity percentage results for the analyzed examination questions papers did not obtain high percentages. As for the syllabus contents result, not any of the proposed examination papers have a fair distribution of questions based on syllabus contents.

Metadata

Item Type: Thesis (Degree)
Creators:
Creators
Email / ID Num.
Mohamed Mahtar, Syazatul Nor Azah
2012960269
Contributors:
Contribution
Name
Email / ID Num.
Thesis advisor
Mohamed Ariff, Dr Mohamed lmran
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science
Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Operating systems (Computers)
Q Science > QA Mathematics > Fuzzy logic
Divisions: Universiti Teknologi MARA, Perak > Tapah Campus > Faculty of Computer and Mathematical Sciences
Programme: Computer Science
Journal or Publication Title: Journal of Administrative Science
UiTM Journal Collections: UiTM Journal > Journal of Administrative Science (JAS)
ISSN: 1675-1302
Volume: 16
Number: 1
Page Range: pp. 1-9
Keywords: artificial intelligence; bloom's taxonomy; syllabus contents
Date: 2015
URI: https://ir.uitm.edu.my/id/eprint/49236
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