藉由電子書學習系統探討學生自主學習、學業成績與線上學習行為之間的關係-以微積分課程為例

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题名:	藉由電子書學習系統探討學生自主學習、學業成績與線上學習行為之間的關係-以微積分課程為例
作者:	劉泰佑;LIU, TAI-YU
贡献者:	數學系
关键词:	自主學習能力;線上學習行為;學業成績
日期:	2020-07-28
上传时间:	2020-09-02 18:47:25 (UTC+8)
出版者:	國立中央大學
摘要:	本研究旨在藉由電子書學習系統去探討學生的自主學習能力、線上學習行為、經驗學習與滿意度以及學業成績之間的相互關係，自主學習能力中包含了目標設定、環境結構、學習策略、時間管理、尋求幫助和自我評估。並運用SEM結構方程模型與Amos探討學生的自主學習能力、線上學習行為和學業成績之間的關係。研究對象為中央大學108學年度大學部有修習微積分課程之學生共91名，以該名學生施測之問卷結果為主要資料。本研究編制一份用於測量學生的自主學習能力和經驗學習與滿意度之問卷於會考時間進行施測。藉由問卷資料和學生在線上學習系統的瀏覽紀錄資料與學業修習狀況進行統計數據分析，統整出自主學習能力、線上學習行為、經驗學習與滿意度和學業成績之間的相互關係。結果發現，學生的自主學習能力對於學生的線上學習行為和學業成績，為顯著正相關；學生的線上學習行為對於學生的學業成績，為顯著正相關。故我們可以藉由學生的自主學習能力以及學生在BookRoll系統的線上學習行為去預測學生的學習成效。;The purpose of this study is to explore the interrelationships among students′ self-regulated learning ability, online learning behavior, experience learning and satisfaction, and learning outcomes through the e-book learning system. The self-regulated learning ability includes goal setting, environment structuring, and task strategies, time management, help seeking and self-evaluation. And using SEM structural equation model and Amos to explore the relationship between students′ self-regulated learning ability, online learning behavior and learning outcomes. The research object is a total of 91 students who have taken calculus courses in the 108th academic year of National Central University. This research compiles a questionnaire for measuring students′ self-regulated learning ability and experience learning and satisfaction to be tested during the examination time. Through the questionnaire data, the student′s online learning system′s browsing record data and academic status for statistical data analysis, the correlation between self-regulated learning ability, online learning behavior, experience learning and satisfaction and learning outcomes is sorted out. It was found that students′ self-regulated learning ability was significantly positively correlated with students′ online learning behavior and learning outcomes; students′ online learning behavior was significantly positively correlated with student′s learning outcomes. Therefore, we can predict students′ learning effectiveness through students′ self-regulated learning ability and students′ online learning behavior in BookRoll system.
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