Feature reduction for neural network in determining the Bloom’s cognitive level of question items
The concept of Bloom’s taxonomy has broadly implemented as a guideline in designing a reasonable examination question paper that consist of question items belonging to various cognitive levels which are tolerate to the different capability of students. Currently, academician will identify the Bloom’...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | أطروحة |
| اللغة: | English |
| منشور في: |
2009
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | http://eprints.utm.my/id/eprint/11449/6/ChaiJingHuiMFSKSM2009.pdf |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| id |
my-utm-ep.11449 |
|---|---|
| record_format |
uketd_dc |
| spelling |
my-utm-ep.114492017-09-20T09:51:15Z Feature reduction for neural network in determining the Bloom’s cognitive level of question items 2009-10 Chai, Jing Hui QA75 Electronic computers. Computer science The concept of Bloom’s taxonomy has broadly implemented as a guideline in designing a reasonable examination question paper that consist of question items belonging to various cognitive levels which are tolerate to the different capability of students. Currently, academician will identify the Bloom’s cognitive level of question items manually. However, most of them are not knowledgeable in identify the cognitive level and this situation will result to miss categorized of question items. To overcome this problem, this study has proposed a question classification model using artificial neural network trained by the scaled conjugate gradient backpropagation learning algorithm as question classifier to classify cognitive level of question items. 2009-10 Thesis http://eprints.utm.my/id/eprint/11449/ http://eprints.utm.my/id/eprint/11449/6/ChaiJingHuiMFSKSM2009.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information System |
| institution |
Universiti Teknologi Malaysia |
| collection |
UTM Institutional Repository |
| language |
English |
| topic |
QA75 Electronic computers Computer science |
| spellingShingle |
QA75 Electronic computers Computer science Chai, Jing Hui Feature reduction for neural network in determining the Bloom’s cognitive level of question items |
| description |
The concept of Bloom’s taxonomy has broadly implemented as a guideline in designing a reasonable examination question paper that consist of question items belonging to various cognitive levels which are tolerate to the different capability of students. Currently, academician will identify the Bloom’s cognitive level of question items manually. However, most of them are not knowledgeable in identify the cognitive level and this situation will result to miss categorized of question items. To overcome this problem, this study has proposed a question classification model using artificial neural network trained by the scaled conjugate gradient backpropagation learning algorithm as question classifier to classify cognitive level of question items. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Chai, Jing Hui |
| author_facet |
Chai, Jing Hui |
| author_sort |
Chai, Jing Hui |
| title |
Feature reduction for neural network in determining the Bloom’s cognitive level of question items |
| title_short |
Feature reduction for neural network in determining the Bloom’s cognitive level of question items |
| title_full |
Feature reduction for neural network in determining the Bloom’s cognitive level of question items |
| title_fullStr |
Feature reduction for neural network in determining the Bloom’s cognitive level of question items |
| title_full_unstemmed |
Feature reduction for neural network in determining the Bloom’s cognitive level of question items |
| title_sort |
feature reduction for neural network in determining the bloom’s cognitive level of question items |
| granting_institution |
Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems |
| granting_department |
Faculty of Computer Science and Information System |
| publishDate |
2009 |
| url |
http://eprints.utm.my/id/eprint/11449/6/ChaiJingHuiMFSKSM2009.pdf |
| _version_ |
1747814856657993728 |