摘要
Log数据不仅包括学习时间、学习进程、鼠标和键盘敲击等静态数据,还详细呈现了从学习开始到结束的动态数据。文章截取PISA(2012)新加坡、日本、中国上海的Log数据,运用相关、滞后序列、聚类等教育数据挖掘方法分析三个国家学生在"车票"一题的"探索和理解"问题解决过程。结果发现:相比新加坡和日本,中国上海学生仍缺乏深入试题情境进行比较、探索,反映出问题解决策略不足;中国上海学生在"错误倾向组"比例过大,反映出高、低水平问题解决能力的学生呈两极分化,亟待提高低水平学生的问题解决能力。最后,文章依据研究结果在课堂教学、教育决策等方面提出了相关建议。
Log file data is not only static data that includes learning time,learning process,mouse and keyboard clicks,but also dynamic data that reflects the process of learning.Based on PISA(2012),methods of correlation,lag sequence and clustering were employed to analyze the log file on the item of TICKETS in Singapore,Japan and China.The results indicated that a)The behavioral sequences of students in Shanghai China were different from that of Singapore and Japan,reflecting a lack of strategy and method,b)Chinese students in Shanghai owned large proportion in‘error prone groups’,indicating the polarized distribution of the high and low problem-solving ability students.Finally,some suggestions were proposed at end to improve Chinese students’problem-solving performance.
作者
首新
何鹏
陈明艳
胡卫平
SHOU Xin;HE Peng;CHEN Ming-yan;HU Wei-ping(Department of Primary Education,Chongqing Normal University,Chongqing,China 400700;Department of Chemistry,Northeast Normal University,Changchun,Jilin,China 130024;Chongqing Yubei District Tianyi Primary School,Chongqing,China 401147;Key Laboratory of Modern Teaching Technology,Shaanxi Normal University,Xi’an,Shaanxi,China 710062)
出处
《现代教育技术》
CSSCI
北大核心
2018年第12期41-47,共7页
Modern Educational Technology
关键词
问题解决能力
PISA
教育数据挖掘
序列分析
problem solving performance
PISA
educational data mining
behavioral sequence
