應用潛在語意分析於試題相似度比對
-以中華民國物流協會認證題庫為例

黃巧媛

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應用潛在語意分析於試題相似度比對 -以中華民國物流協會認證題庫為例

Apply the Latent Semantic Analysis for Similarity Comparison of Examination Questions －Case Study of Certification Exam by Taiwan Association of Logistics Management

黃巧媛 , Masters　　Advisor：陳志騰　　

繁體中文

數位學習；文字探勘；潛在語意分析；向量空間模型；試題相似性； e-Learning ； Text Mining ； Latent Semantic Analysis ； Vector Space Model ； Exam Questions Similarity

Abstract │ Reference (68) │ Altmetrics

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In recent years, the booming Internet has brought the new style of learning. Unlike the traditional learning, e-learning gets popular increasingly. Nowadays, more and more people are using e-learning approach. When evaluating the effect of learning, the traditional classroom uses paper and pencil tests to measure the learning effect. However, e-learning would use online exam tests to evaluate. Nevertheless, the current online exam system can not directly determine and compare the similarity between exam questions in the item pool. Therefore, this study is going to take advantage of Latent Semantic Analysis (LSA) to evaluate the degree of similarity between exam questions. To achieve the goal of avoiding the appearance of similar exam questions or concepts, I hope this analysis approach can be applied to the advance evaluation of setting online exam in the future.

In this study, the results show that:
(i)	In the experimental design regarding removing verbiages or not, removing verbiages is a better way to judge the similarity.
(ii)	In general word segmentation system, logistics terminology is not able to conduct an efficient result yet. Therefore, the result is still without the significant outcomes.
(iii)	In the process of calculating the similarity with LSA, the magnitude of dimension reduction will affect the efficiency when judging the exam question. How the dimension reduction is depends on critical parameter for the practical application. Compared to the result of Vector Space Model (VSM), LSA can determine the similarity of exam questions more accurately.
To sum up, with regards to the similarity analyze for exam questions by LSA and VSM, LSA could precisely determine the similarity. Consequently, LSA is a technique which is important for comparing the similarity of exam questions.

Reference ( 68 ) 〈TOP〉

Contreras-Piña, C., & Ríos, S. A. (2015). An empirical comparison of latent sematic models for applications in industry. Neurocomputing.
連結：
Feldman, R., & Dagan, I. (1995). Knowledge Discovery in Textual Databases (KDT). Paper presented at the KDD.
連結：
Feldman, R., & Sanger, J. (2007). The text mining handbook: advanced approaches in analyzing unstructured data: Cambridge University Press.
連結：
Huffaker, D. A., & Calvert, S. L. (2003). The new science of learning: Active learning, metacognition, and transfer of knowledge in e-Learning applications. Journal of Educational Computing Research, 29(3), 325-334.
連結：
Martin, D. I., & Berry, M. W. (2007). Mathematical foundations behind latent semantic analysis. Handbook of latent semantic analysis, 35-56.
連結：

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