透過分析學習行為模式提供干預措施以提升學習成效;Interventions to enhance learning outcomes by analyzing learning patterns

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Title:	透過分析學習行為模式提供干預措施以提升學習成效;Interventions to enhance learning outcomes by analyzing learning patterns
Authors:	侯家臻;Hou, Chia-Chen
Contributors:	資訊工程學系
Keywords:	學習行為模式;學習干預;學習成效;自我調節學習;Learning Pattern;Intervention;Learning Outcome;Self-Regulated Learninf
Date:	2022-07-12
Issue Date:	2022-10-04 11:58:24 (UTC+8)
Publisher:	國立中央大學
Abstract:	仰賴資訊科技的蓬勃發展，在課程引入線上學習系統輔助課程學習的教學模式越來越普及。過去研究顯示學生在不同的學習環境下會採用不同的學習行為模式，而學習行為模式與自我調節學習相互影響著。如何從這些學習系統收集數據並進行分析以更了解學習行為模式與背後自我調節學習能力相關的議題日漸被重視。本研究引入多種線上學習系統於實驗場域以輔助課程進行並收集學習數據，生成學習序列後使用序列分群方法，萃取出9種學生在課中、課外的learning modes。接著針對萃取出的模式使用k-means進一步分群出不積極(G1) 、課中不積極課外積極(G2)、課中積極課外不積極(G3)、積極(G4)，4種的學生。透過相關性分析這些模式與學習成效及自我調節學習能力的關聯性，並使用ANOVA分析4群學生在學習成效與自我調節學習能力的差異，針對learning patterns設計一套的學習干預內容於實驗組進行。兩組在課程第一週與期中考後實施MSLQ問卷取得學生的自我調節學習能力前、後測資料，並以第一次小考、期中考成績作為學習成效的前、後測分數。最後使用ANCOVA比較兩組在學習成效、自我調節學習能力前、後測的差異，並討論學習干預活動對兩者的影響。研究結果顯示不同learning modes的4群學生在學習成效與自我調節學習能力上有一定程度的差異性，在課中行為較積極者在學習成效上的表現優於較不積極者，另外，課中與課外皆較積極的學生(G4)在自我調節學習各面向的能力(如：組織、時間和學習環境、努力規範…等)亦比其他群學生來得高。在干預成效上，實驗組的學生在學習干預活動後在成績進步的幅度上與控制組相比有顯著差異，其進步幅度更大。在自我調節學習各面向的能力上可維持學生在複誦面向上的能力，其餘面向則無維持或提升的影響。 ;Relying on the vigorous development of technology, the teaching mode of apply online learning system into the curriculum to assist learning is becoming more and more popular. Previous studies have shown that students will adopt different learning patterns in different learning environments, and that learning patterns and self-regulated learning affect each other. Thus, how to collect and analyze data from these learning systems to better understand the issues on learning patterns and self-regulated learning ability behind them has been paid more and more attention. In this study, a variety of online learning systems are introduced into the experimental field to assist the course and collect learning data. After generating the learning sequence, the sequence clustering method is used to extract 9 learning modes of students in- and out-of -class. Then, according to the extraction mode, K-means is used to further cluster the students who are not active (G1), not active in class but active out of class (G2), not active in class but active out of class (G3), and active (G4). Through the correlation analysis of these models with learning outcome and self-regulated learning ability, ANOVA was used to analyze the differences on learning outcome and self-regulated learning ability among the four groups of students. A interventions based on learning patterns were designed for the experimental group. Both groups implemented MSLQ questionnaire to obtain the pre-test and post test data of students′ self-regulated learning ability on first week and after midterm, and took the results of the first quiz and the midterm exam as the pre-test and post test scores of learning outcome. Finally, ANCOVA was used to compare the pre-test and post test differences on learning outcome and self-regulated learning ability between the two groups, and to discuss the impact of intervention activities on them. The results show that the four groups of students with different learning patterns have differences in learning outcome and self-regulated learning ability. Students who are more active in class perform better in learning outcome than the less active ones. In addition, the students who are more active both in class and out of class (G4) also have higher self-regulated learning ability in all factors (such as Organization, Time and Study Environment, Effort Regulation, etc.) than other groups of students. In terms of the effectiveness of the intervention, the students in the experimental group have a significant difference in the progress compared to the control group, and the progress is greater. The ability of self-regulated learning in all factors can maintain students′ ability in Rehearsal, while the other factors have no influence on maintenance or improvement.
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