透過序列探勘分析學習影片瀏覽操作對於學習成效的影響;Exploring the influence of video viewing action on learning performance by using sequence mining.

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题名:	透過序列探勘分析學習影片瀏覽操作對於學習成效的影響;Exploring the influence of video viewing action on learning performance by using sequence mining.
作者:	施博文;Shih, Po-Wen
贡献者:	資訊工程學系在職專班
关键词:	磨課師;影片瀏覽操作分析;相關係數分析;單因子共變異分析;探索性因素分析;滯後序列分析;MOOCs;Video learning behavior;Correlation Coefficient analysis;ANCOVA;Lag Sequential Analysis;Exploratory Factor Analysis
日期:	2019-07-19
上传时间:	2019-09-03 15:35:45 (UTC+8)
出版者:	國立中央大學
摘要:	透過分析高成就以及低成就在學習模式上的差異，找出何種學習模式對於成績的提升有幫助。摘要隨著技術的進步，互聯網越來越受歡迎。近年來，在線教育機會不斷增長。與傳統研究不同，在線學習提供了極大的靈活性和便利性。首先，您不必浪費時間和金錢去校園。您還可以選擇一個完全符合您興趣和需求的計劃，因為您不僅限於您所在地區提供的課程。由於學生在操作磨課師平台上，有著多種操作方式，如觀看影片、觀看文件、測驗考試…等。這些行為都會被系統紀錄下來，若是僅紀錄而未分析，則對於這些資訊過於浪費。而最常被學生使用的功能是觀看影片的行為，因此透過觀察學生在操作磨課師教學平台上觀看影片的操作，試著解析學生的操作學習模式，是否會影響到學生考試成績，並希望藉著此分析系統，達到可於期中便可預測到學生未來成績並提供學生更良好的觀看影片的方法，以達到督促增進學生成績的功能。本研究透過相關係數分析、多因子分析、探索性因素分析與滯後序列分析四種學習分析法，來找出對學生學習成績具有影響力的關鍵觀看學習模式。其中，透過單因子共變異分析分析影片參與程度對於成績是否有影響，利用滯後序列分析找出高低成就學生的觀看學習模式的差異，透過相關係數分析找出不同學習行為對於學生成就的影響。利用探索性因素分析將找出的萃取出不同面向，找出學習模式，利用多因子分析找出對整體學生成績最具影響性的觀看模式；而透過本研究所提供之分析結果，教師可參考而對學習狀況較差之學生進行輔導，避免學生因遇到學習困難而放棄學習。關鍵字：學習分析、磨課師、影片瀏覽操作分析、相關係數分析、單因子共變異分析、探索性因素分析、滯後序列分析、多因子分析 ;From Lag-sequential Analysis, by analyzing the differences in learning behavior between high achievement and low achievement, it is helpful to find out which learning behavior is helpful for improvement. ABSTRACT With advances in technology, Internet has gained become more popular. Online education opportunities have been growing in recent years. Unlike traditional studies, online learning offers great flexibilities and convenience. To begin with, you don′t have to waste time and money traveling to the campus. You can also select a program that entirely fits with your interests and needs because you are not restricted to the classes that are offered in your area. Because students can use many functions in Massive Open Online Courses (MOOCs), such as watching movies, watching documents, quizzing exams, fill in questionnaire, etc. These behaviors are recorded by the MOOCs system. If they are only recorded but not analyzed, they are too wasteful. The most commonly used behavior is watching a learning video. Therefore, this method observes the operation of the students watching the learning video on the MOOCs and try to analyze whether the student′s operational learning model will affect the student′s test scores. This research uses Correlation coefficient, Multiple Factor analysis, Exploratory factor analysis s and Lag-sequential Analysis to find out the key viewing learning modes that have an impact on students′ learning performance. Among them, Lag Sequential Analysis is used to find out the difference between the learning behavior between high and low achievement students and the influence of difference learning performance is found through by Correlation coefficient. Use Exploratory factor analysis to reduce the number of patterns found and find out the learning mode. Use Multiple correspondence analysis to find the most influential viewing mode patterns for overall student performance. Use the analysis results provided by the research, teachers can find out the students who have poor learning patterns and help them to catch up with the learning schedule. Keywords: learning analysis, MOOCs, Video learning behavior, Correlation Coefficient analysis, ANCOVA, Lag Sequential Analysis, Exploratory Factor Analysis, Multi Factor Analysis
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