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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2008
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/9463/1/SitiHasrinafasyaFSKSM2008.pdf http://eprints.utm.my/id/eprint/9463/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:685?site_name=Restricted Repository |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Teknologi Malaysia |
| Language: | English |
| Summary: | 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. |
|---|