【摘要】：隨著互聯(lián)網(wǎng)技術(shù)和信息技術(shù)的高速發(fā)展,教育領(lǐng)域的學(xué)習(xí)數(shù)據(jù)與日俱增。借助于計(jì)算機(jī)等工具,可以收集到大量的相關(guān)數(shù)據(jù)。如何將這些海量數(shù)據(jù)轉(zhuǎn)變?yōu)橹R(shí)和信息,并為學(xué)習(xí)者評(píng)價(jià)、教學(xué)決策及學(xué)習(xí)優(yōu)化等方面提供服務(wù),成為教育數(shù)據(jù)挖掘研究領(lǐng)域的熱點(diǎn)�，F(xiàn)有的研究主要基于開放和智能的在線學(xué)習(xí)系統(tǒng),通過使用學(xué)生建模等技術(shù)來揭示學(xué)生的學(xué)習(xí)特征和學(xué)習(xí)狀況等。但是,基于作答數(shù)據(jù)來對(duì)學(xué)生知識(shí)和能力狀態(tài)的研究較少且更多從心理學(xué)的角度進(jìn)行分析。在心理學(xué)領(lǐng)域,認(rèn)知診斷理論能夠了解個(gè)體內(nèi)部微觀心理加工過程,實(shí)現(xiàn)個(gè)體認(rèn)知情況的診斷。然而,認(rèn)知診斷在進(jìn)行診斷分析前一般以Q矩陣?yán)碚撝笇?dǎo)編制測(cè)試,這與實(shí)際的教育考試存在較大差異。另外,認(rèn)知診斷模型復(fù)雜度較高,且多數(shù)模型僅適于0-1評(píng)分?jǐn)?shù)據(jù),這使得現(xiàn)有的診斷模型在實(shí)際應(yīng)用中存在一定的局限。針對(duì)上述問題,本文基于現(xiàn)有研究提出一種適用于多值評(píng)分?jǐn)?shù)據(jù)的高考考生能力數(shù)據(jù)分析和評(píng)價(jià)方法:首先,基于考生得分信息,通過項(xiàng)目反應(yīng)理論(IRT)來分析考生在所測(cè)項(xiàng)目上的反應(yīng)模式與其潛在特質(zhì)(也稱能力)之間的關(guān)系,對(duì)考生個(gè)體的能力水平進(jìn)行評(píng)估。具體利用Rasch模型,以極大似然估計(jì)方法進(jìn)行參數(shù)估計(jì),計(jì)算每個(gè)考生個(gè)體的總能力值θ。該能力值反映了考生在特定測(cè)試上一致性的內(nèi)在特征,對(duì)考生更深層次的能力挖掘有重要作用。其次,由于項(xiàng)目反應(yīng)理論采用單維性假設(shè),即只適于分析單一維度的能力屬性。因此,采用Q矩陣?yán)碚搶⒖忌豢芍苯佑^察的能力狀態(tài)與在項(xiàng)目上可觀察的作答反應(yīng)相連接,從而把考生能力細(xì)化到各個(gè)能力屬性層面,為考生更深層次的能力分析和評(píng)價(jià)提供理論依據(jù)。具體過程為:(1)詳細(xì)分析測(cè)試項(xiàng)目所測(cè)的不可觀察的能力屬性及其層級(jí)結(jié)構(gòu);(2)利用Q矩陣?yán)碚搶⒛芰顟B(tài)轉(zhuǎn)化為可觀察的項(xiàng)目反應(yīng)模式,并改進(jìn)Q矩陣?yán)碚撘赃m于實(shí)際考試中的多值評(píng)分情況,得到多值評(píng)分下的期望反應(yīng)模式。最后,將考生能力分析和評(píng)價(jià)看作一個(gè)多元分類問題,結(jié)合基于IRT和Q矩陣?yán)碚摰目忌芰χ岛晚?xiàng)目反應(yīng)模式構(gòu)造考生的特征向量,采用概率神經(jīng)網(wǎng)絡(luò)算法進(jìn)行分類判別,挖掘考生更深層次的能力掌握情況。模擬實(shí)驗(yàn)表明,本文提出的結(jié)合IRT和Q矩陣的概率神經(jīng)網(wǎng)絡(luò)分類方法能夠一定程度提高考生能力掌握模式判別的準(zhǔn)確率。另外,基于高考數(shù)據(jù)的實(shí)證研究表明,本文方法能夠有效實(shí)現(xiàn)高考考生能力的分析和評(píng)價(jià),并通過不同地區(qū)、學(xué)校及類別的考生之間的比較分析,能夠發(fā)現(xiàn)不同群體考生的能力差異,具有一定的實(shí)際應(yīng)用價(jià)值。
[Abstract]:With the rapid development of Internet technology and information technology, the learning data in the field of education are increasing day by day. With the help of computers and other tools, a large number of relevant data can be collected. How to transform these massive data into knowledge and information, and to provide services for learners' evaluation, teaching decision making and learning optimization, has become a hot topic in the field of educational data mining. The existing research is mainly based on open and intelligent online learning system, through the use of student modeling technology to reveal students' learning characteristics and learning conditions. However, there are few researches on students' knowledge and ability state based on answer data and more from the perspective of psychology. In the field of psychology, the theory of cognitive diagnosis can understand the process of internal micropsychological processing and realize the diagnosis of individual cognition. However, before the diagnosis and analysis of cognitive diagnosis, the Q-matrix theory is generally used to compile the test, which is quite different from the actual educational examination. In addition, the complexity of cognitive diagnosis model is high, and most of the models are only suitable for 0-1 score data, which makes the existing diagnosis models have some limitations in practical application. In order to solve the above problems, this paper proposes a method to analyze and evaluate the ability of college entrance examination candidates based on the existing research. Firstly, based on the information of candidates' scores, this paper proposes a new method to analyze and evaluate the ability of college entrance examination candidates. Through item response theory (IRT), this paper analyzes the relationship between the reaction mode of the examinee on the test item and its potential trait (also called ability), and evaluates the ability level of the examinee individual. Using the Rasch model, the maximum likelihood estimation method is used to estimate the parameters, and the total ability value 胃 of each examinee is calculated. This ability value reflects the inherent characteristics of the consistency of the examinee in the specific test, and plays an important role in the deeper ability mining of the examinee. Secondly, because the project response theory adopts one dimensional hypothesis, it is only suitable for analyzing the single dimension capability attribute. Therefore, the Q matrix theory is used to connect the unobservable ability state of the examinee with the observable response on the project, so that the examinee's ability can be refined to each ability attribute level. To provide theoretical basis for deeper competence analysis and evaluation of candidates. The specific processes are as follows: (1) the unobservable ability attributes and their hierarchical structure measured by the test items are analyzed in detail; (2) the ability state is transformed into observable item response mode by using the Q matrix theory. The Q-matrix theory is improved to suit the multi-value score in practical examination and the expected response mode under multi-value scoring is obtained. Finally, considering the ability analysis and evaluation of examinee as a multivariate classification problem, combining the ability value of examinee based on IRT and Q matrix theory and item response mode to construct the characteristic vector of examinee, the probabilistic neural network algorithm is used to classify and discriminate. Excavate examinee deeper ability grasps the situation. The simulation results show that the probabilistic neural network classification method combined with IRT and Q matrix can improve the accuracy of judging the ability of examinee to a certain extent. In addition, the empirical research based on college entrance examination data shows that this method can effectively realize the analysis and evaluation of the ability of college entrance examination candidates, and through the comparative analysis of different regions, schools and categories of candidates, Ability to find different groups of candidates ability difference, has certain practical application value.
【學(xué)位授予單位】：西南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP311.13;G632.0

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本文編號(hào)：2260889