【摘要】：教育數(shù)據(jù)挖掘在我國目前尚處在理論探索階段,大部分研究都是理論描述和可行性分析,應(yīng)用研究很少。本文立足于所研究課題,從教育數(shù)據(jù)挖掘的應(yīng)用研究入手,探索數(shù)據(jù)挖掘技術(shù)在學(xué)生學(xué)習(xí)狀態(tài)分析中的應(yīng)用。在我國高校存在學(xué)生工作者與學(xué)生比例失衡的問題,導(dǎo)致教育管理資源得不到有效利用,本文旨在解決一個(gè)實(shí)際的問題:給哪些學(xué)生分配稀缺的個(gè)性化學(xué)習(xí)指導(dǎo)的教育管理資源,以助其順利升學(xué)畢業(yè),讓有限的教育管理資源發(fā)揮更大的價(jià)值。本文使用基于離群點(diǎn)檢測的學(xué)生學(xué)習(xí)狀態(tài)分析找出學(xué)習(xí)狀態(tài)異常的學(xué)生,為教育管理資源的分配提供依據(jù)。為獲取離群學(xué)生,針對(duì)本問題設(shè)計(jì)了混合兩階段離群點(diǎn)檢測算法,首先使用基于密度的局部離群點(diǎn)檢測算法計(jì)算出每名學(xué)生的局部離群點(diǎn)因子,再使用基于統(tǒng)計(jì)的離群點(diǎn)檢測算法進(jìn)行二元判斷以獲取離群學(xué)生,該算法獲取的離群學(xué)生中絕大部分是學(xué)習(xí)狀態(tài)異常的學(xué)生,但學(xué)習(xí)狀態(tài)異常的學(xué)生的總數(shù)偏少。針對(duì)該算法存在的不足從多方面進(jìn)行改進(jìn),從數(shù)據(jù)角度,添加人工屬性擴(kuò)大知識(shí)庫;從算法角度,使用縮減迭代對(duì)不斷減小的數(shù)據(jù)集進(jìn)行迭代離群點(diǎn)檢測;最后將添加人工屬性和縮減迭代融合形成一種綜合的改進(jìn)。在本問題中,丟失學(xué)習(xí)狀態(tài)異常的學(xué)生的代價(jià)很大,相比混合兩階段離群點(diǎn)檢測算法,改進(jìn)后的算法找出了更多學(xué)習(xí)狀態(tài)異常的學(xué)生,在覆蓋人數(shù)上實(shí)現(xiàn)了對(duì)原始算法的優(yōu)化。實(shí)例分析表明,使用混合兩階段離群點(diǎn)檢測算法或其改進(jìn)算法,基于離群點(diǎn)檢測的學(xué)生學(xué)習(xí)狀態(tài)分析都能有效地找出學(xué)習(xí)狀態(tài)異常的學(xué)生,其結(jié)果可以輔助高校學(xué)生工作者更加科學(xué)、高效地管理。
[Abstract]:Educational data mining in China is still in the stage of theoretical exploration, most of the research is theoretical description and feasibility analysis, the application of research is rare. Based on the research topic, this paper explores the application of data mining technology in the analysis of students' learning state from the perspective of the application of educational data mining. In our country, there exists the problem of imbalance between student worker and student ratio in colleges and universities, which leads to the lack of effective utilization of educational management resources. The purpose of this paper is to solve a practical problem: which students are allocated scarce personalized learning guidance of educational management resources in order to help them graduate smoothly, so that the limited educational management resources play a greater value. In this paper, the students with abnormal learning state are found out by using the analysis of students' learning state based on outlier detection, which provides the basis for the allocation of educational management resources. In order to obtain outliers, a hybrid two-stage outlier detection algorithm is designed. Firstly, the local outlier detection algorithm based on density is used to calculate the local outlier factor of each student. Then the outlier detection algorithm based on statistics is used for binary judgment to obtain outliers. Most of the outliers obtained by the algorithm are students with abnormal learning state, but the total number of students with abnormal learning state is small. In view of the shortcomings of the algorithm, the algorithm is improved from many aspects, from the point of view of data, the artificial attributes are added to expand the knowledge base, and from the point of view of the algorithm, the reduced iteration is used to detect the outliers in the decreasing data set. Finally, a comprehensive improvement will be formed by adding artificial attributes and reducing iterative fusion. In this problem, the cost of missing students with abnormal learning state is very high. Compared with the mixed two-stage outlier detection algorithm, the improved algorithm finds out more students with abnormal learning state, and optimizes the original algorithm in terms of coverage. Examples show that using mixed two-stage outlier detection algorithm or its improved algorithm, the learning state analysis of students based on outlier detection can effectively find out the students with abnormal learning state. The results can help college student workers to manage more scientifically and efficiently.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：G642;TP311.13

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本文編號(hào)：2139907