以粒子群最佳化演算法建置適性測驗系統

Building Adaptive Testing System Using Particle Swarm Optimization

根據學習者能力而提供適合的試題是性化測驗目的，大部分都是給予測驗者一個能力值，該能力值代表測驗者對學科的了解程度，但每門學科中，往往是由許多區塊知識所構成的，以英文來講，可簡單分成單字、文法、閱讀、聽力等區塊，學習者可能對單字知識為擅長，對文法知識則否。本研究將以粒子群最佳化演算法結合知識構的概念，提出一個能動態選題的適性化測驗系統，根據測驗者上題作答狀況決定下題的難易度，以及多重能力的評估方式，對每個區塊知識給予獨立的能力值，當出題時選出與知識關聯性高的題目，透過這種機制達到適性化學習的目標。

Adaptive testing can generate questions according to learners' competence level. In general, an adaptive testing system gives an ability value to each testee by evaluting the testee's understanding of a subject. A subject is usually comprised of many finds of knowledgd. For example, the subject, English, contains reading, grammar, vocabularies, writing, listening and etc. This research aims to develop an adative testing system based on Particle Swarm Optimization (PSO) and integrating with knowledge structure. The system decides the fiddifulty of the next question according to the correctness of the previoue answer. The experimental results show that the system can dynamically generate queations correlated highly with learners' competence level.