基於自律學習程度及社會網路分析探討學生行為順序模式差異及學習成就-以社交關係程度為調節變項
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2024
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數位學習是當今教育中一種不斷發展的方法,COVID-19疫情加速了數位學習的普及。然而,學習者在數位學習中面臨著輟學率高、評估問題、時間管理問題和不同的學習方式等挑戰。為了提高數位學習者的學習表現,學習分析的研究逐漸增多,它可以用來測量、蒐集、分析和報告學習者,過去的研究中不僅指出自律學習與學習成就的正向影響相關,而社交關係程度對學習成就也具有重要影響。因此,為了探討自律學習是否影響社交關係程度,且社交關係程度是否會調節自律學習及學習成就之間,本研究將分析學習者的行為順序模式,蒐集國小到高中的學生資料,以學習階段作為分類規則,觀察每一群體中社交關係程度良好的學生是否與自律學習程度較高的學生為同一群,並且使用SPSS分析社交關係程度的調節效果,最後研究不同群體之間的學習行為差異。本研究將有助知道哪些行為會發生於自律及高社交關係程度學生身上,能產生好的學習循環;哪些行為僅發生於非自律及低社交關係程度學生身上,讓教師可以藉此找出需要重點關注之學生。研究結果顯示,調節效果僅顯著於高中階段,在其他階段未達顯著,導致其結果原因可能在於國小及國中階段時,學生多在討論區中聊天,而非發表課堂相關內容,因此無法達成知識共享使調節效果無顯著。在行為差異方面可以發現在高自律的學生方面,行為次數較高有明顯的學習行為,且行為較多樣化路徑較為豐富,低自律學習者則是行為較為單一,顯現出了被動的學習模式,擁有較短較小的行為路徑,也因此在知識獲取上沒有高自律者的紮實,但在社交關係上並無發現明顯的行為差異。最後不同學習階段的行為差異,因為每個階段所主要使用的平臺功能不同,也導致行為順序模式上的差異,這可能來自教師使用的教學法之差異,但由於目前平臺沒有蒐集教師的教學法之資料,因此無法直接斷言是否在教學法相同時也會存在著差異,而這部分可以在未來的分析上做改進。
E-learning is a continuously evolving method in today's education, and the COVID-19 pandemic has accelerated its adoption. However, learners face challenges in e-learning, such as high dropout rates, assessment issues, time management problems, and different learning styles. To improve the performance of online learners, research in learning analytics has been growing. It could be used on measure, collect, analysis, and report on learners. Past research not only indicates a positive impact of self-regulated learning on academic achievement but also emphasizes the significant influence of social relationships on learning outcomes. Therefore, in order to investigate whether self-regulated learning is associated with the level of social relationships and whether the level of social relationships may moderates between self-regulated learning and academic achievement, this study will be analyzing the behavioral patterns of learners by collecting the data of the students from elementary school to high school, schooling stage will be used as the classification criterion. The study will be observing whether students with high levels of social relationships belonging to the same group as those with higher levels of self-regulated learning. SPSS will be used to analysis the moderating effect of social relationships, and finally, the study will be investigating differences in learning behaviors among different groups. This research will help identify which behaviors occur in self-regulated and highly social relationships students, leading to positive learning cycles, and which behaviors are unique to non-self-regulated and low social relationships students. This information can assist educators in focusing on those students who needs great attention.The results of the study showed that the moderating effect was only significant at the high school level, but not at the other levels. The reason for this may be that at the elementary and middle school levels, students chatted in discussion forums rather than posting classroom-related content, so there was no way to achieve knowledge sharing, which made the moderating effect not significant. In terms of behavioral differences, it can be found that students with high self-discipline have a higher number of behaviors with obvious learning behaviors and richer behavioral paths, while low self-discipline learners have a shorter and smaller behavioral paths with passive learning modes with more homogeneous behaviors, which makes them less solid in knowledge acquisition than high self-discipline learners, and does not show any obvious behavioral differences in social relationships. Finally, the behavioral differences in different learning stages are due to the different functions of the platform used in each stage, which also lead to differences in behavioral patterns. This may be due to the differences in the pedagogical methods used by the teachers, but since the platform does not collect data on the pedagogical methods of the teachers at the moment, it is not possible to directly conclude whether there are differences even when the pedagogical methods are the same, and this part can be improved in the future analyses.
E-learning is a continuously evolving method in today's education, and the COVID-19 pandemic has accelerated its adoption. However, learners face challenges in e-learning, such as high dropout rates, assessment issues, time management problems, and different learning styles. To improve the performance of online learners, research in learning analytics has been growing. It could be used on measure, collect, analysis, and report on learners. Past research not only indicates a positive impact of self-regulated learning on academic achievement but also emphasizes the significant influence of social relationships on learning outcomes. Therefore, in order to investigate whether self-regulated learning is associated with the level of social relationships and whether the level of social relationships may moderates between self-regulated learning and academic achievement, this study will be analyzing the behavioral patterns of learners by collecting the data of the students from elementary school to high school, schooling stage will be used as the classification criterion. The study will be observing whether students with high levels of social relationships belonging to the same group as those with higher levels of self-regulated learning. SPSS will be used to analysis the moderating effect of social relationships, and finally, the study will be investigating differences in learning behaviors among different groups. This research will help identify which behaviors occur in self-regulated and highly social relationships students, leading to positive learning cycles, and which behaviors are unique to non-self-regulated and low social relationships students. This information can assist educators in focusing on those students who needs great attention.The results of the study showed that the moderating effect was only significant at the high school level, but not at the other levels. The reason for this may be that at the elementary and middle school levels, students chatted in discussion forums rather than posting classroom-related content, so there was no way to achieve knowledge sharing, which made the moderating effect not significant. In terms of behavioral differences, it can be found that students with high self-discipline have a higher number of behaviors with obvious learning behaviors and richer behavioral paths, while low self-discipline learners have a shorter and smaller behavioral paths with passive learning modes with more homogeneous behaviors, which makes them less solid in knowledge acquisition than high self-discipline learners, and does not show any obvious behavioral differences in social relationships. Finally, the behavioral differences in different learning stages are due to the different functions of the platform used in each stage, which also lead to differences in behavioral patterns. This may be due to the differences in the pedagogical methods used by the teachers, but since the platform does not collect data on the pedagogical methods of the teachers at the moment, it is not possible to directly conclude whether there are differences even when the pedagogical methods are the same, and this part can be improved in the future analyses.
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Keywords
社會網路分析, 學習行為分析, 順序模式探勘, 教育大數據, 自律學習, Social Network Analysis, Learning Behavior Analysis, Sequential Pattern Mining, Educational Big Data, Self-regulated Learning