Similarity measure for retrieval of question items with multi-variable data sets
In designing test question items assessment, similarity measures have a great influence in determining whether the test question items generated semantically match to the learning outcomes and the instructional objectives. It has been realized that to carry out an effective case retrieval of questi...
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| Format: | Thesis |
| Language: | English |
| Published: |
2008
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| Online Access: | http://eprints.utm.my/9463/ http://eprints.utm.my/9463/1/SitiHasrinafasyaFSKSM2008.pdf |
| _version_ | 1848891854104297472 |
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| author | Che Hassan, Siti Hasrinafasya |
| author_facet | Che Hassan, Siti Hasrinafasya |
| author_sort | Che Hassan, Siti Hasrinafasya |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | In designing test question items assessment, similarity measures have a great influence in determining whether the test question items generated semantically match to the learning outcomes and the instructional objectives. It has been realized that to carry out an effective case retrieval of question items, there must be selection criteria of questions’ features that considerably meet the specifications and requirements of learning outcomes as well as instructional objectives that are set by academician. In this case, each question item consists of multi-variables data type namely, Bloom level, question type, discrimination index and difficulty index. To retrieve the semantic similar question items, it strongly depends on the correct definition of the case representation as well as similarity measure. In other words, there presentation of data must reflect the characteristic of data type before the appropriate adapted similarity measure approach can be applied to ensure the degree of similarity values. In this case, Bloom was transformed into normalized rank data before Euclidean distance similarity measure was applied. Meanwhile, question type was converted into binary, 0 and 1 before Hamming distance was applied to calculate its similarity value. Both difficulty index and discrimination index used the concept of fuzzy similarity measure, where by their index ranges were adjusted and expressed in trapezoidal fuzzy numbers, respectively. Lastly, these approaches were aggregated together to produce one single similarity value of question item. |
| first_indexed | 2025-11-15T21:04:35Z |
| format | Thesis |
| id | utm-9463 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T21:04:35Z |
| publishDate | 2008 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-94632018-10-14T07:19:52Z http://eprints.utm.my/9463/ Similarity measure for retrieval of question items with multi-variable data sets Che Hassan, Siti Hasrinafasya QA75 Electronic computers. Computer science In designing test question items assessment, similarity measures have a great influence in determining whether the test question items generated semantically match to the learning outcomes and the instructional objectives. It has been realized that to carry out an effective case retrieval of question items, there must be selection criteria of questions’ features that considerably meet the specifications and requirements of learning outcomes as well as instructional objectives that are set by academician. In this case, each question item consists of multi-variables data type namely, Bloom level, question type, discrimination index and difficulty index. To retrieve the semantic similar question items, it strongly depends on the correct definition of the case representation as well as similarity measure. In other words, there presentation of data must reflect the characteristic of data type before the appropriate adapted similarity measure approach can be applied to ensure the degree of similarity values. In this case, Bloom was transformed into normalized rank data before Euclidean distance similarity measure was applied. Meanwhile, question type was converted into binary, 0 and 1 before Hamming distance was applied to calculate its similarity value. Both difficulty index and discrimination index used the concept of fuzzy similarity measure, where by their index ranges were adjusted and expressed in trapezoidal fuzzy numbers, respectively. Lastly, these approaches were aggregated together to produce one single similarity value of question item. 2008-10 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/9463/1/SitiHasrinafasyaFSKSM2008.pdf Che Hassan, Siti Hasrinafasya (2008) Similarity measure for retrieval of question items with multi-variable data sets. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information System. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:685?site_name=Restricted Repository |
| spellingShingle | QA75 Electronic computers. Computer science Che Hassan, Siti Hasrinafasya Similarity measure for retrieval of question items with multi-variable data sets |
| title | Similarity measure for retrieval of question items with multi-variable data sets |
| title_full | Similarity measure for retrieval of question items with multi-variable data sets |
| title_fullStr | Similarity measure for retrieval of question items with multi-variable data sets |
| title_full_unstemmed | Similarity measure for retrieval of question items with multi-variable data sets |
| title_short | Similarity measure for retrieval of question items with multi-variable data sets |
| title_sort | similarity measure for retrieval of question items with multi-variable data sets |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/9463/ http://eprints.utm.my/9463/ http://eprints.utm.my/9463/1/SitiHasrinafasyaFSKSM2008.pdf |