結合資料視覺化與自我調節策略之個人化學習對線上學習者之自我調節與序列行為模式之影響：以研究倫理課程為例

Abstract

本研究之目的為瞭解資料視覺化和自我調節策略之系統設計的個人化學習，對於線上學習者自我調節及行為模式的影響，研究對象共182位碩博士生，共分為控制組、自我調節組、資料視覺化組及雙項功能組四個組別。研究工具為自我調節量表、先備知識測驗及Log行為記錄，用以探索學生的序列行為模式。本研究的實驗流程為先進行自我調節前測、先備知識測驗，再進入各組的個人化學習系統，閱讀學術倫理教材並進行單元測驗，最後實施自我調節後測。 本研究結果發現資料視覺化功能有助於促進自我調節的目標設定及尋求協助。此外，自我調節功能結合資料視覺化功能有助於促進學生自我評估，使其在觀看自我的學習表現後，再次閱讀教材，並能促進測驗退步後再回頭複習教材的行為。因此，本研究認為自我調節功能結合資料視覺化能有效地提升自我調節的能力。 本研究建議，在線上學習課程中，除了可將學習的數據分析透過資料視覺化圖形呈現給學生，也需提供自我調節的策略，如目標設定、時間設定等系統功能，輔助資料視覺化的引導，提高學生的自主學習性。最後，本研究成果期望能作為線上學習系統開發與教育研究者之參考。

The purpose of this study was to explore how personalized learning integrated with data visualization and self-regulatory strategies influences online learner’s self-regulation and sequential behavioral patterns. Participants were 182 graduate students, who were randomly assigned into four groups: control group, self-regulation group, data visualization group, and self-regulation with data visualization group. The instruments include the Online Self-regulated Learning Questionnaire and prior-knowledge test; additionally, the behavioral logs were recorded to explore learner’s sequential behavioral patterns. The experimental procedure is as follows. First, the Online Self-regulated Learning Questionnaire and prior-knowledge test were administered. Second, learners engaged in the personalized learning system where they perused the learning materials about research ethics education and completed the tests. Finally, they completed the post-test of the Online Self-regulated Learning Questionnaire. The results demonstrated that the use of data visualization function improves goal setting and help-seeking dimensions of self-regulation. In addition, self-regulation integrated with data visualization improved learner’s self-evaluation, so that after witnessing their performance, learners perused the learning materials again, and reviewed learning materials after receiving their test scores. Therefore, self-regulation integrated with data visualization effectively enhanced students’ self-regulation. This study suggests that course developers should incorporate data visualization functions into the designs of online learning courses. Furthermore, strategies of self-regulation (e.g., goal setting, time management) should be provided so as to guide data visualization and improve students’ self-learning. The findings of the study can serve as the references for online course designers and educators.