本文選題：數據挖掘 + 灰色預測算法�。� 參考：《東北師范大學》2017年碩士論文

【摘要】：隨著國家對高等教育投入的不斷加大,各地高校的招生規(guī)模也隨之擴大,在校學生的人數越來越多,大多數普通高校都面臨著學生就業(yè)難的問題。誠然,畢業(yè)生的職業(yè)發(fā)展受到包括個體素質、家庭背景、就業(yè)政策等多種因素影響,但在高校中教育引導的影響仍處主要地位。在《教育部關于做好2016屆全國普通高等學校畢業(yè)生就業(yè)創(chuàng)業(yè)工作的通知》中第三條指出:大力提高就業(yè)指導服務能力,建立精準推送就業(yè)服務機制,各地高校要充分利用“互聯網+”技術,實現智能化供需匹配,實現就業(yè)服務信息化、個性化。在這種大形勢下,如何提升大學生的綜合素質,提高高校人才培養(yǎng)質量,成為各高校亟待解決的重中之重。因此,在高校教育中,教育工作者如何為學生合理、有效、及時地開展職業(yè)發(fā)展方向指導工作是一項重要的工作任務。但是在這種教育信息化快速發(fā)展的大環(huán)境下,在高校教育工作者的工作有很大訴求的情況下,教學過程中積累的海量數據卻只是以各種不同形式的表格存儲在不同的計算機上,并沒有被更深層次的挖掘使用,這造成了這些數據被大量的閑置。面對以上現狀,筆者在研究中發(fā)現在進行高校學生職業(yè)發(fā)展方向指導工作中,對大學生的未來職業(yè)發(fā)展方向進行預測是必要的,有效的預測方法可以為教育工作者提供客觀、簡便的數據支撐及理論依據,助其順利實施教育指導工作。因此,本文就此開展了一系列的研究工作。筆者首先分析了高校職業(yè)生涯教育工作過程中存在的問題,進而進行了教育數據挖掘相關理論與常用方法分析和大學生職業(yè)發(fā)展方向影響因子維度分析,從而確定了本文的研究維度,然后進行了預測模型的建模、算法實現及預測方法的實例檢驗等相關研究。根據以上的研究,筆者得出了以下結論:灰色預測算法在中小規(guī)模數據預測上性能更優(yōu)越;基于綜合素質測評數據進行預測,得到的預測結論(預測學生的未來職業(yè)發(fā)展方向)更加客觀、精準。但本研究中的算法設計仍存在不足,可以在未來的研究中嘗試通過更大量的數據和更多的實驗驗證去調整相關參數,從而進一步優(yōu)化算法。
[Abstract]:With the increasing of the national investment in higher education, the enrollment scale of colleges and universities has also expanded, and the number of students is increasing. Most ordinary colleges and universities are facing the problem of difficult employment of students. It is true that the professional development of graduates is influenced by many factors, such as individual quality, family background, employment policy and so on, but the influence of educational guidance is still in the main position in colleges and universities. The third article of the notice of the Ministry of Education on doing a good Job in the Employment and Entrepreneurship of the 2016 National ordinary College graduates points out: vigorously improve the ability of employment guidance services, and establish a mechanism for precise employment promotion service. Colleges and universities all over the world should make full use of "Internet" technology, realize intelligent supply and demand matching, and realize the informationization and individuation of employment service. In this situation, how to improve the comprehensive quality of college students and improve the quality of talent training has become the most important task to be solved. Therefore, in college education, it is an important task for educators to guide their career development in a reasonable, effective and timely manner. However, under the circumstances of the rapid development of educational informatization and the great demands of the educators in colleges and universities, the massive data accumulated in the teaching process is only stored on different computers in various forms. It is not used in deeper mining, which results in a large amount of idle data. In the face of the above situation, the author finds that it is necessary to predict the future career development direction of college students in the process of guiding the career development direction of college students, and the effective prediction method can provide objective for the educators. Simple data support and theoretical basis for its smooth implementation of education guidance. Therefore, this paper carried out a series of research work. The author first analyzes the problems existing in the process of career education in colleges and universities, and then analyzes the relevant theories and methods of educational data mining and the dimension analysis of the influencing factors of college students' career development direction. The dimension of this paper is determined, and then the modeling of the prediction model, the algorithm realization and the example test of the prediction method are studied. Based on the above research, the author draws the following conclusions: the grey prediction algorithm has better performance in the prediction of small and medium scale data, and based on the comprehensive quality evaluation data to predict, The predicted conclusions are more objective and accurate. However, the algorithm design in this study is still insufficient, we can try to adjust the relevant parameters through more data and more experimental verification in the future research, so as to further optimize the algorithm.
【學位授予單位】：東北師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：G647.38;TP311.13

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