Dissertation/ Thesis
Development and Performance Verification of Cognitive Diagnosis Model based on Deep Learning
| العنوان: | Development and Performance Verification of Cognitive Diagnosis Model based on Deep Learning |
|---|---|
| Alternate Title: | 深度學習認知診斷模式開發與成效驗證 |
| المؤلفون: | LIU, ZHI-YONG, 劉志勇 |
| Thesis Advisors: | KUO, BOR-CHEN, LI, CHENG-HSUAN, 郭伯臣, 李政軒 |
| سنة النشر: | 2019 |
| المجموعة: | National Digital Library of Theses and Dissertations in Taiwan |
| الوصف: | 107 Deep learning has brought breakthrough development in many fields such as a convolutional neural network for image recognition, long short-term memory networks for speech and natural language processing, word2vec model for produces word vectors, generative adversarial network, and deep reinforcement learning. In this study, Autoencoder algorithm is applied to develop a deep learning cognitive diagnosis model (DLCD). Traditional cognitive diagnosis models, such as DINA and G-DINA, require parameter estimation through the expectation-maximization algorithm or Markov chain Monte Carlo method. DLCD is used to improve the problem that traditional cognitive diagnosis models require large samples for estimation. The research methods are divided into three parts, Q matrix research, simulation research, and real data research. DLCD not only work well both on a complete Q matrix and a non-complete Q matrix but also demonstrates the most favorable generalization ability. The proposed method outperforms DINA and G-DINA in simulated research when the sample size is small. Moreover, DLCD has the highest classification agreement on real data research. Based on the results of simulated and real data sets, DLCD is suitable for small-class teaching and even for only one examinee. |
| Original Identifier: | 107NTCT0629007 |
| نوع الوثيقة: | 學位論文 ; thesis |
| وصف الملف: | 82 |
| الاتاحة: | http://ndltd.ncl.edu.tw/handle/s6t3jr |
| رقم الانضمام: | edsndl.TW.107NTCT0629007 |
| قاعدة البيانات: | Networked Digital Library of Theses & Dissertations |
| الوصف غير متاح. |