本文選題：計(jì)算機(jī)自適應(yīng)測(cè)驗(yàn) + 項(xiàng)目反應(yīng)理論�。� 參考：《電子科技大學(xué)》2017年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)的快速發(fā)展,越來(lái)越多的學(xué)習(xí)者選擇通過(guò)互聯(lián)網(wǎng)進(jìn)行在線學(xué)習(xí),各種基于智能化和自動(dòng)化的在線學(xué)習(xí)模式、方法方興未艾,在線學(xué)習(xí)的自適應(yīng)測(cè)評(píng)就是其中的一個(gè)重要方面。本文針對(duì)自適應(yīng)在線學(xué)習(xí)測(cè)評(píng)理論與技術(shù)展開了深入研究,將教育學(xué)、心理學(xué)等最新成果應(yīng)用到在線學(xué)習(xí)測(cè)評(píng)的研究當(dāng)中,提出了針對(duì)學(xué)習(xí)者個(gè)體的自適應(yīng)選題策略算法,并在此基礎(chǔ)上實(shí)現(xiàn)了在線學(xué)習(xí)的自適應(yīng)測(cè)評(píng)系統(tǒng),提高了學(xué)習(xí)者的測(cè)評(píng)效率,為學(xué)習(xí)者高效地進(jìn)行個(gè)性化在線學(xué)習(xí)能力測(cè)評(píng)提供了新的途徑。論文主要進(jìn)行了三個(gè)方面的工作:一、研究并設(shè)計(jì)自適應(yīng)在線學(xué)習(xí)測(cè)評(píng)系統(tǒng)的選題策略,通過(guò)研究自適應(yīng)測(cè)驗(yàn)的經(jīng)典選題策略,分析最大信息量法、a分層法以及其改進(jìn)算法的特點(diǎn)與局限,在經(jīng)典選題策略的基礎(chǔ)上提出了新的可靠、可行的改進(jìn)選題策略,同時(shí)與傳統(tǒng)選題策略及其改進(jìn)算法從項(xiàng)目曝光率、題庫(kù)平均曝光率、測(cè)驗(yàn)準(zhǔn)確性、測(cè)驗(yàn)效率和測(cè)驗(yàn)重疊率等多個(gè)維度進(jìn)行了性能比較。二、研究基于蒙特卡洛模擬的自適應(yīng)測(cè)評(píng)選題策略的檢驗(yàn)方法,對(duì)本文提出的算法進(jìn)行了模擬實(shí)現(xiàn),設(shè)計(jì)檢驗(yàn)方法實(shí)驗(yàn)程序結(jié)構(gòu)并編寫檢驗(yàn)方法程序,應(yīng)用檢驗(yàn)方法模擬選題策略測(cè)評(píng)過(guò)程,并對(duì)傳統(tǒng)選題策略與本文提出的改進(jìn)策略進(jìn)行比較。三、設(shè)計(jì)并實(shí)現(xiàn)了自適應(yīng)在線學(xué)習(xí)測(cè)評(píng)系統(tǒng),基于可用性和可靠性的考慮,設(shè)計(jì)了測(cè)評(píng)系統(tǒng)架構(gòu),實(shí)現(xiàn)了測(cè)評(píng)系統(tǒng)各模塊功能,建立了自適應(yīng)在線學(xué)習(xí)測(cè)評(píng)系統(tǒng)的測(cè)評(píng)題庫(kù),為學(xué)習(xí)者進(jìn)行在線測(cè)評(píng)提供了有效途徑。論文提出的新型自適應(yīng)測(cè)評(píng)算法與模式,有效降低了傳統(tǒng)方法的項(xiàng)目曝光率,相對(duì)于其他分層方法提高了測(cè)驗(yàn)精度,在測(cè)驗(yàn)準(zhǔn)確性和測(cè)驗(yàn)效率上都有較大提升,開發(fā)的自適應(yīng)在線學(xué)習(xí)測(cè)評(píng)系統(tǒng)為學(xué)習(xí)者個(gè)性化學(xué)習(xí)能力的區(qū)分提供了可靠的測(cè)評(píng)手段,具有良好的應(yīng)用前景和價(jià)值。
[Abstract]:With the rapid development of the Internet, more and more learners choose to learn online through the Internet. All kinds of online learning models based on intelligence and automation are in the ascendant. Adaptive evaluation of online learning is one of the most important aspects. This paper is a deep study of adaptive online learning evaluation theory and technology. In the study, the latest achievements of education and psychology are applied to the study of online learning evaluation. An adaptive selection strategy algorithm is proposed for the individual of the learners. On this basis, an adaptive evaluation system for online learning is realized, which improves the evaluation efficiency of the learners and performs the personalized online learning for the learners efficiently. Ability evaluation provides a new way. The thesis mainly carries out three aspects: first, research and design the selection strategy of adaptive online learning evaluation system, through the study of the classic selection strategy of adaptive test, the analysis of the maximum information method, the a stratification method and its improved algorithm characteristics and limitations, the basis of the classic topic selection strategy. A new reliable and feasible selection strategy is proposed. At the same time, the performance is compared with the traditional selection strategy and its improved algorithm from the project exposure rate, the average exposure rate of the question bank, the test accuracy, the test efficiency and the test overlap rate. Two, the test of the adaptive selection strategy based on the Mongol Tekalo simulation is studied. Method, the algorithm proposed in this paper is simulated, the test method experiment program structure is designed and the test method program is written. The test method is used to simulate the evaluation process of the selected topic strategy, and the traditional selection strategy is compared with the improved strategy proposed in this paper. Three, the adaptive Online learning evaluation system is designed and implemented, based on the availability of the system. Considering the nature and reliability, the architecture of evaluation system is designed, the functions of each module of the evaluation system are realized, and the evaluation question bank of the self-adaptive online learning evaluation system is set up. It provides an effective way for the learners to evaluate the online evaluation system. The new adaptive evaluation algorithm and model proposed in this paper has effectively reduced the exposure rate of the traditional methods. Compared with other stratification methods, the test accuracy is improved and the test accuracy and test efficiency have been improved greatly. The developed adaptive online learning evaluation system provides a reliable evaluation method for the differentiation of learners' individualized learning ability, and has a good application prospect and value.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：G434

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