本研究旨在探究雲端行動學習對大學生學習表現與自我調整學習的影響。本研究採用不等組前後測準實驗設計，實驗對象為臺北市某國立大學選修運輸科技課程之大學生，透過隨機分組分為實驗組與對照組，進行教學和實作活動。本研究以水陸兩用車當作教學實驗之單元。實驗組採用雲端行動學習平台進行討論，而對照組採用實體討論。本研究蒐集課程活動前自我調整學習問卷結果和太陽能車的成績作為前測分數，在課程活動後自我調整學習問卷結果和水陸兩用車的成績作為後測分數。量化資料分析部分，採用SPSS 22.0 for Windows統計軟體進行標準差、平均數、和獨立樣本單因子共變數分析，在質化資料分析部分，採用檢視資料、資料編碼和資料分析，依據研究結果，闡述研究發現。
本研究主要結論如下所述：1. 雲端行動學習對學習表現無顯著的影響。2. 雲端行動學習對自我調整學習無顯著的影響。3. 比較高與低學習表現的學生在自我調整策略有不同的選擇，發現在後設認知策略、動機信念管理策略和環境與資源管理策略有顯著不同。本研究針對研究結果，對於未來教學加入雲端行動學習和後續研究提供建議。

The purpose of the research was to investigate the impact of cloud-based m-learning on learning performance and self-regulated learning among college students. A nonequivalent pretest-posttest quasi-experimental design was applied in the research. The experimental objects were students in a transportation technology course of a national university in Taipei. They were assigned randomly in two groups, an experimental group and a control group to conduct teaching and practical activities. The research was conducted with Amphibious Vehicle as a teaching content. The experimental group used cloud-based m-learning for discussion, while the control group used the traditional way. This study used self-regulated learning questionnaire results and Solar Car test scores as pretest before the class, and the study used self-regulated learning questionnaire results and Amphibious vehicle test scores as posttest after the class. The SPSS 22.0 for Windows was used to proceed standard deviation, average and the one-way analysis of covariance (ANCOVA) in the quantitative data analysis. We used viewing data, data coding and data analysis in the qualitative data analysis.
The main conclusions of this study were：1. Cloud-based m-learning has no significant effects on students' learning performances. 2. Cloud-based m-learning has no significant effects on self-regulated learning. 3. Students with higher and lower learning performances had different fom choosing self-regulated strategies. There were significant differences in using metacognition strategies, motivation and belief management strategies and environment and resource management strategies. According to the results, several suggestions were given to the implementation of the cloud-based m-learning and to the further research.

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