【摘要】：高校課堂教學(xué)目前是各大高校教學(xué)的主要形式,它是高校教學(xué)的基礎(chǔ)并且在教學(xué)過程中具有非常重要的作用。而課堂教學(xué)質(zhì)量評價體系的建立和實施不僅對高校的教學(xué)發(fā)展理論有很大的幫助作用,更能保障高校課堂教學(xué)質(zhì)量評價的順利進(jìn)行以及課堂教學(xué)活動發(fā)揮有效的作用。目前,傳統(tǒng)的教學(xué)質(zhì)量評價方式在只有學(xué)生參與的情況下,雖然已經(jīng)取得了一定的成果,但是還有一些問題沒有很好的解決,比如學(xué)生的主觀因素對教師存在一定的偏見,使得評價結(jié)果出現(xiàn)誤差,或者只注重評價結(jié)果而不能體現(xiàn)教師教學(xué)的過程,也會導(dǎo)致評價結(jié)果出現(xiàn)誤差等等。而支持向量機(jī)(Support Vector Machines,簡稱SVMs)被引入教學(xué)質(zhì)量評價之后,學(xué)生、同行和領(lǐng)導(dǎo)都參與評價,不僅能夠避免人為因素對結(jié)果造成的誤差還能充分體現(xiàn)教師的教學(xué)過程。另外教學(xué)質(zhì)量評價是一種多類分類問題,最終選擇支持向量機(jī)多類分類算法對本文的課堂教學(xué)質(zhì)量評價結(jié)果進(jìn)行預(yù)測。概括起來,本文的主要工作如下:(1)分析和總結(jié)了課堂教學(xué)質(zhì)量評價的意義和傳統(tǒng)的評價方法存在的缺陷。根據(jù)具體的需求和評價指標(biāo)體系的構(gòu)建原則,制定了課堂教學(xué)質(zhì)量的評價指標(biāo)體系。由于各指標(biāo)之間存在非線性關(guān)系,因此,決定將支持向量機(jī)算法應(yīng)用于課堂教學(xué)質(zhì)量評價中,用來解決教學(xué)質(zhì)量評價中可能遇到的問題。(2)介紹了目前常用的幾種支持向量機(jī)多類分類算法,重點研究了二叉樹支持向量機(jī)多類分類算法,并且針對已經(jīng)存在算法生成的是偏二叉樹的缺陷,提出了一種新的改進(jìn)思想。改進(jìn)算法利用完全二叉樹的生成策略以及聚類中的類距離的相關(guān)定義,使得生成的二叉樹結(jié)構(gòu)達(dá)到完全或者近似完全的狀態(tài),從而提高分類精度和效率。最后通過在UCI數(shù)據(jù)集上做仿真實驗,驗證了改進(jìn)算法的有效性。(3)利用改進(jìn)的二叉樹支持向量機(jī)多類分類算法,構(gòu)建基于二叉樹支持向量機(jī)的高校課堂教學(xué)質(zhì)量評價模型,針對山東省某高校的教學(xué)質(zhì)量進(jìn)行評價,填寫評價量表,并且統(tǒng)計、收集多組數(shù)據(jù)。在MATLAB環(huán)境下,對收集到的數(shù)據(jù)集進(jìn)行實驗,并分析其結(jié)果。將改進(jìn)算法的預(yù)測精度和效率與支持向量機(jī)算法、二叉樹支持向量機(jī)算法相比較,改進(jìn)算法優(yōu)勢明顯,能夠更好的預(yù)測未標(biāo)記樣本。
[Abstract]:At present, classroom teaching in colleges and universities is the main form of teaching in colleges and universities. It is the foundation of teaching in colleges and universities and plays a very important role in the teaching process. The establishment and implementation of the evaluation system of classroom teaching quality can not only help the theory of teaching development in colleges and universities, but also ensure the smooth progress of evaluation of classroom teaching quality and the effective role of classroom teaching activities. At present, although the traditional teaching quality evaluation method has only the participation of students, although it has achieved certain results, there are still some problems that have not been solved very well. For example, the subjective factors of students have certain prejudice against teachers. It makes the evaluation result error, or only pays attention to the evaluation result but not the teacher teaching process, also will cause the evaluation result to appear the error and so on. After the introduction of support vector machine (SVMs) to the evaluation of teaching quality, students, peers and leaders all participate in the evaluation, which can not only avoid the errors caused by human factors, but also fully reflect the teaching process of teachers. In addition, the evaluation of teaching quality is a kind of multi-class classification problem. Finally, support vector machine multi-class classification algorithm is chosen to predict the evaluation results of classroom teaching quality in this paper. To sum up, the main work of this paper is as follows: (1) the significance of classroom teaching quality evaluation and the shortcomings of traditional evaluation methods are analyzed and summarized. The evaluation index system of classroom teaching quality is established according to the concrete needs and the construction principle of evaluation index system. Because of the nonlinear relationship among the indicators, it is decided to apply the support vector machine (SVM) algorithm to the evaluation of classroom teaching quality. It is used to solve the problems that may be encountered in the evaluation of teaching quality. (2) several commonly used SVM classification algorithms are introduced, and the binary tree support vector machine multi-class classification algorithm is studied emphatically. In order to solve the problem of partial binary tree generation, a new improved idea is proposed. The improved algorithm makes use of the generation strategy of complete binary tree and the relative definition of class distance in clustering to make the structure of the generated binary tree complete or nearly complete so as to improve the classification accuracy and efficiency. Finally, the effectiveness of the improved algorithm is verified by the simulation experiment on the UCI dataset. (3) using the improved binary tree support vector machine multi-class classification algorithm, the evaluation model of college classroom teaching quality based on binary tree support vector machine is constructed. This paper evaluates the teaching quality of a university in Shandong province, fills out the evaluation scale, and collects many groups of data. In MATLAB environment, the collected data sets are tested and the results are analyzed. Comparing the prediction accuracy and efficiency of the improved algorithm with the support vector machine algorithm and binary tree support vector machine algorithm, the improved algorithm has obvious advantages and can better predict unlabeled samples.
【學(xué)位授予單位】：重慶師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：G642.4

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