开放教育研究
開放教育研究
개방교육연구
Open Education Research
2015年
3期
74~80
,共null页
网络教育 数据挖掘 决策树方法 英语统考 预测模型
網絡教育 數據挖掘 決策樹方法 英語統攷 預測模型
망락교육 수거알굴 결책수방법 영어통고 예측모형
online education;data mining;decision tree;the English unified examination;forecasting model
合适的数据分析技术能使我们借助网络学历教育学生在学习和管理系统中产生的数据和信息,发现相关规律,进而为网络学历教育教学和管理流程的优化提供有益的决策依据。本文采用数据挖掘中数据分类C5.0决策树方法,通过分析网络学历教育本科学生英语学习及相关信息,实现了对其英语统考成绩的预测。在分析英语统考前景预测的目标特性后,在SPSS的Clementine 12.0数据挖掘环境中,历经数据提取、数据预处理、决策树构建和决策树优化等步骤,本研究构建了网络教育本科英语统考成绩的预测模型,并提出了模型实现方法;同时对模型相关属性的重要性进行了分析,提出了提高网络教育本科学生英语学习水平和统考通过率的相应策略。
閤適的數據分析技術能使我們藉助網絡學歷教育學生在學習和管理繫統中產生的數據和信息,髮現相關規律,進而為網絡學歷教育教學和管理流程的優化提供有益的決策依據。本文採用數據挖掘中數據分類C5.0決策樹方法,通過分析網絡學歷教育本科學生英語學習及相關信息,實現瞭對其英語統攷成績的預測。在分析英語統攷前景預測的目標特性後,在SPSS的Clementine 12.0數據挖掘環境中,歷經數據提取、數據預處理、決策樹構建和決策樹優化等步驟,本研究構建瞭網絡教育本科英語統攷成績的預測模型,併提齣瞭模型實現方法;同時對模型相關屬性的重要性進行瞭分析,提齣瞭提高網絡教育本科學生英語學習水平和統攷通過率的相應策略。
합괄적수거분석기술능사아문차조망락학력교육학생재학습화관리계통중산생적수거화신식,발현상관규률,진이위망락학력교육교학화관리류정적우화제공유익적결책의거。본문채용수거알굴중수거분류C5.0결책수방법,통과분석망락학력교육본과학생영어학습급상관신식,실현료대기영어통고성적적예측。재분석영어통고전경예측적목표특성후,재SPSS적Clementine 12.0수거알굴배경중,력경수거제취、수거예처리、결책수구건화결책수우화등보취,본연구구건료망락교육본과영어통고성적적예측모형,병제출료모형실현방법;동시대모형상관속성적중요성진행료분석,제출료제고망락교육본과학생영어학습수평화통고통과솔적상응책략。
With the development of information society, information storm brought by big data is changing our life, working and thinking style. More and more students are participating in online diploma education. They learn through accessing and using online resources, online homework, interactive discussions and examinations. Such participation has left or generated a giant useful data and information in various types of course management and learning systems. Using appropriate data analysis techniques, these data and information can help us obtain useful knowledge, find relevant disciplines, and provide useful basis for decision making, programming all aspects of online learning, optimizing processes for teaching management, and improving the quality of teaching and changing the design of educational software. All of these are inevitable demands for sustainable development of online education. We analyzed English learning and other relevant information of undergraduate students in online diploma education using data classification technology in data mining, and forecasted the prospects when they took the English unified examination, which was necessary for their graduation. After briefly describing the concepts and relevant theories of data mining, we compared the differences between classification and clustering techniques in data mining and analyzed the characteristics of achieving the goal for forecast. We determined to use C5. 0 decision tree classification in data mining. Using the relevant data of students who took online education in Jiangnan University and had taken the English unified examination as the training data, our research went through four steps： data retrieving, data preprocessing, decision tree structuring and optimization. Then, in Clementine 12. 0 data mining environment of SPSS, we built a forecasting model of the undergraduate English unified examination. This article also proposes the implementation method of the model, discusses and analyzes the constructed forecasting model and the importance of the related properties, and proposes appropriate policies about how to improve the level of undergraduate English online education and the throughput rate of the English unified examination.
