本研究主要透過微積分課程與電子書學習系統探討學生的「學習環境」、「線上學習行為」、「學業成績」之間的相互關係，「學習環境」中分為實體課程與線上課程兩個面向並包含了課程掌握度、自主行動、學習者控制、工具使用、實境學習、課程內容設計等六個項目。
本研究根據相關文獻資料，擬制了一份測量學生學習環境之問卷，挑選了中央大學110學年度選修微積分課程的資電學院與理學院共480人作為研究對象，並於本學期第二次會考時施測。
將問卷結果結合BookRoll線上學習系統的後台數據，透過統計軟體SPSS統整出學習環境、線上學習行為與學業成績的相互關係。並運用結構方程模型（Structural Equation Modeling, SEM）分析學生的學習環境、線上學習行為與學業成績三者之間的架構與關聯性。
研究結果顯示，學習環境中的實體課程學習環境和線上課程學習環境對於對於學業成績的影響力較不顯著，而線上學習行為對於學業成績的影響力顯著。因此，實體課程學習環境和線上課程學習環境不可作為預測學生學業成績之參考指標，而線上學習行為可作為預測學生學業成績之參考指標，所以實體課程學習環境和線上課程仍需要透過修正與調整才有機會作為參考指標。

This study mainly explores the interrelationship between students'"learning environment","online learning behavior"and"academic performance" through the course in calculus and e-book learning system, learning environment is divided into two aspects: face to face course and online course, and includes six items, including course degree of mastery, Spontaneity, learner control, tool usage, authentic learning, and course design.
Based on the relevant literature, this study prepared a questionnaire to measure the learning environment of students, and selected 480 students from the College of Electrical Engineering & Computer Science and the College of Science who took calculus course in the 110th academic year of National Central University as research objects, and tested them at the second examination of this semester.
The questionnaire results are combined with the background data of the BookRoll online learning system, and the learning environment, online learning behavior and academic performance were summarized through the statistical software SPSS. Structural Equation Modeling (SEM) is used to analyze the structure and correlation between students' learning environment, online learning behavior and academic performance.
The results of the study show that the face to face course learning environment and the online course learning environment have non-significant on academic performance, while the influence of online learning behavior on academic performance is significant. Therefore, the face to face course learning environment and online courses learning environment cannot be used as a reference index for predicting students' academic performance, and online learning behavior can be used as a reference index for predicting students' academic performance, so the face to face course learning environment and online courses learning environment still need to be corrected and adjusted to have the opportunity to be used as reference.

1. Iyer, R. (2011). Investigating the effectiveness of an online course: Development of the comparative learning environment questionnaire. (Unpublished doctoral dissertation), Curtin University, Sydney
2. Dillenbourg, P., Schneider, D.K., & Synteta, P. (2002). Virtual Learning Environments. In A. Dimitracopoulou (Ed). In the 3rd Hellenic Conference on Information & Communication Technologies in Education, 3-18, Kastaniotis Editions, Greece.
3. Perkins, D., Jay, E., & Tishman, S. (1993). Beyond abilities: A dispositional theory of thinking. Merrill-Palmer Quarterly, 39(1), 1–21.
4. Akpinar, Y., & Hartley, J. R. (1996). Designing interactive learning environments. Journal of Computer-Assisted Learning, 12(1), 33–46.
5. Perkins,D., & Salomon,G.(1989). Are cognitive skills context-bound? Educational Researcher, 18(1), 16–25.
6. Dewey, John. (1916). Democracy and Education. New York: McMillan.
7. Perkins, D., & Unger, C. (1999). Teaching and learning for understanding. In Instructional Design (Ed.), Theories and models (pp. 91–114). Mahwah, NJ: Lawrence Erlbaum Associates.
8. Anderson, G. J., & Walberg, H. J. (1974). Learning environments. In H. J. Walberg (Ed.), Evaluating educational performance: A sourcebook of methods, instruments and examples (pp. 81–98). Berkeley, CA: McCutchan.
9. Diaz, D. P., & Cartnal, R. B. (1999). Students' learning styles in two classes: Online distance learning and equivalent on-campus. College Teaching, 47, 130–135.
10. Fraser, B. J. (2002b). Preface. In S. C. Goh & M. S. Khine (Eds.), Studies in educational learning environments: An international perspective (pp. vii–viii). River Edge, NJ: World Scientific.
11. Khine, M. S. (2002). Study of learning environment for improving science. In S. C. Goh & M. S. Khine (Eds.), Studies in educational learning environments: An international perspective (pp. 131–151). River Edge, NJ: World Scientific.
12. 吳明隆（2003）。SPSS統計應用學習實務：問卷分析與應用統計。臺北市：知城數位科技。
13. 吳清山（2020），實境式學習，教育研究月刊，309期，頁134-135。
14. 陳寬裕、王正華 ( 2017 )，論文統計分析實務：SPSS 與 AMOS 的運用，第三版，五南圖書，2017 年 9 月。
15. 馬政豪. (2010). 線上學習行為分數與學習成就預測模型相關度之探討. 中原大學資訊工程研究所學位論文, 1-53.
16. 戴源甫（2008）。家庭背景、學習態度、與學習成就之相關性─結構方程模式之分析途徑。國立成功大學政治經濟學研究所碩士論文。