跳到主要內容

臺灣博碩士論文加值系統

(3.215.79.68) 您好!臺灣時間:2022/07/04 04:48
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:趙之瑄
研究生(外文):Chao, Chih-Hsuan
論文名稱:智慧化回饋對線上學習者之學習自我效能、投入度與線上學習行為之影響:以校園學術研究倫理課程為例
論文名稱(外文):Effects of smart feedback on online learners’ learning self-efficacy, engagement, and behavioral patterns: The case of research ethics education
指導教授:孫之元孫之元引用關係
指導教授(外文):Sun, Chih-Yuan
口試委員:黃芸茵周倩陳昭秀孫之元
口試委員(外文):Huang, Yun-yinChou, ChienChen,Chao-HsiuSun, Chih-Yuan
口試日期:2016-07-29
學位類別:碩士
校院名稱:國立交通大學
系所名稱:教育研究所
學門:教育學門
學類:綜合教育學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:中文
論文頁數:101
中文關鍵詞:序列分析學習分析學習行為學習自我效能投入度
外文關鍵詞:Sequential AnalysisLearning AnalyticsLearning Behavioral PatternLearning Self-EfficacyEngagement
相關次數:
  • 被引用被引用:1
  • 點閱點閱:1087
  • 評分評分:
  • 下載下載:68
  • 收藏至我的研究室書目清單書目收藏:2
本研究旨在探討智慧化回饋功能對線上學習者之學習自我效能、投入度與線上學習行為之影響,以台灣北部地區某兩所國立大學、一所私立大學以及南部地區一所國立大學進行便利取樣223位研究生為樣本,研究者自行架設具有線上回饋功能之學術研究倫理線上課程平台,蒐集學習者教材點擊次數、教材閱讀時間、測驗失敗次數、測驗時間,以學習自我效能問卷、投入度問卷進行相關分析。並撰寫程式將學習者行為追蹤點擊記錄轉換為認真閱讀、隨意閱讀、多工閱讀、暫離四個編碼,於本系統後端資料庫進行行為編碼資料轉換、偵測學習者之學習序列行為路徑、序列行為次數,以訂定好的序列規則範本給予學習者適性的智慧化回饋功能,本研究將學習者以智慧化回饋功能分為警示加鼓勵回饋、鼓勵回饋、警示回饋與無回饋功能四組,進行課程學習。
本研究發現警示加鼓勵回饋功能與鼓勵回饋功能顯著提升兩組學習者之學習自我效能與投入度。此外,鼓勵回饋功能組之學習者偏好以多工方式進行線上學習,而警示加鼓勵型回饋功能與鼓勵型回饋功能組學習者出現至暫離的學習行為,是由於學習者需要一些休息時間以為接下來的課程能認真閱讀進行準備。因此,本研究認為,「綜合型」(警示加鼓勵)的回饋方式能有效的提升學習自我效能與投入度,且在此線上學習課程中,「鼓勵型」的回饋功能對學習者而言是必要的。
本研究建議,未來線上學習課程中,可設計更多元化、更精緻之回饋功能,例如可將回饋功能設計以語音、動畫的方式呈現,且應縮短課程章節長度。此外,為更準確的記錄學習者線上學習行為,未來的研究者應增加更多學習系統頁面上追蹤點擊範圍,並配合平台個人化管理,使更能掌握學習者的學習行為,提高線上學習的有效性。此研究成果希望能作為線上學習系統回饋功能設計與線上學習平台改善之參考。

The purpose of this study was to explore how smart feedback influences online learners’ learning self-efficacy, engagement and their online learning behavioral patterns. Sequential analysis was applied to analyze the learning behaviors in an online academic research ethics course. Participants were 223 graduate students at two national universities and one private university in northern Taiwan and one national university in southern Taiwan. A self-developed smart feedback learning management system was used to track the usage data, including the frequencies of logins, online reading, and failing exams, as well as the duration of reading. The instruments included a learning self-efficacy scale and an engagement scale. The system logs were transformed into four codes for lag sequential analysis: intensive reading, skimmed reading, multi-tasking reading, and offline. The system transformed the logs into student behavior data as well as detected students’ sequential behavior paths and frequencies. The rules based on sequential behaviors for smart feedback function were established for this study. Learners were randomly assigned into four groups: warning with encouragement feedback, encouragement feedback, warning feedback, and no feedback groups.

The results showed that warning with encouragement feedback as well as the encouragement feedback significantly enhanced learners’ learning self-efficacy and engagement in the online course. Learners in the encouragement feedback group preferred multi-tasking reading. Learners in the warning with encouragement feedback group and encouragement feedback group showed offline behavior in the online course because of learners need to take some rest for continued course. In addition, the integration of warning and encouragement effectively enhanced students’ learning self-efficacy and engagement. In the online learning course, encouragement feedback feature may be necessary to learners.

This study suggests that online learning courses can be designed with diversified and refined feedback features. For example, the feedback can be designed in the format of audio or animation. In addition, in order to maintain learners’ engagement, the length of the course should be shortened. Future researchers may recording a wide range of learners’ clicks course system to understand more learners’ learning behavior and improve their learning effectiveness. The findings of this study may serve as references for online course designer and learning management system researchers.
目錄
摘要 I
Abstract II
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 3
第三節 研究目的 5
第四節 研究問題 6
第五節 名詞釋義 6
第六節 研究範圍與限制 8
第七節 研究章節配置 8
第八節 研究流程 10
第二章 文獻探討 11
第一節 智慧化學習、個人化學習與回饋 11
第二節 學習自我效能之定義與相關研究 21
第三節 投入度之定義 22
第四節 智慧化回饋功能對於學習自我效能、投入度影響之相關研究探討 25
第五節 學習分析與序列分析 27
第六節 小結 32
第三章 研究方法 34
第一節 研究架構 34
第二節 研究對象與設計 35
第三節 施測流程 37
第四節 研究工具 38
第五節 資料處理與分析 54
第六節 預試 61
第四章 研究結果 62
第一節 不同組別學習者之學習自我效能差異 62
第二節 不同組別學習者之投入度差異 67
第三節 行為模式次數分析 75
第四節 行為模式序列分析 77
第五章 討論與建議 84
第一節 不同回饋種類對學習者學習自我效能之影響 84
第二節 不同回饋種類對學習者投入度之影響 86
第三節 不同回饋種類對學習者學習行為之影響 88
第四節 綜合討論 91
第五節 研究限制與建議 93
參考文獻 95
附錄一 學習自我效能量表 100
附錄二 投入度量表 101






表目錄
表2-1-1 傳統數位學習與智慧化學習比較與整理表…………………………………………..13
表2-3-1 投入度相關量表整理……………………………………………………………………………..24
表2-5-1 學習分析類型與相關研究整理表…………………………………………………………..29
表2-5-2線上閱讀序列分析編碼功能與定義表…………………………………………………...31
表3-2-1 受測者學校分佈……………………………………………………………………………………..36
表3-4-1 自我效能探索性因素分析結果摘要表…………………………………………………..39
表3-4-2 投入度量表題項內容………………………………………………………………..…………...41
表3-4-3 原線上學習平台功能表………………………………………………………………..………..43
表3-4-4 研究者自行建智學習平台功能表…………………………………………………………..43
表3-4-5 學術倫理學習教材內容表………………………………………………………………………45
表3-4-6 學習分析記錄定義表……………………………………………………………………………..52
表3-4-7 行為追蹤記錄案鈕是件定義表………………………………………………………………52
表3-5-1 本研究統計方法一覽表………………………………………………………………………….54
表3-5-2 序列分析編碼綱要表……………………………………………………………………………..56
表3-5-3 智慧化回饋提醒分類表(鼓勵類)……………………………………………………………59
表3-5-4 智慧化回饋提醒分類表(警示類)……………………………………………………………60
表3-6-1預試系統架構修改一覽表……………………………………………………………………….61
表4-1-1 四組前測學習自我效能描述性統計摘要表…………………………………………..63
表4-1-2 四組前測學習自我效能變異數同質性檢定摘要表……………………………….63
表4-1-3 四組前測學習自我效能單因子變異數分析摘要表……………………………….63
表4-1-4四組後測學習自我效能描述性統計摘要表……………………………………………64
表4-1-5 四組後測學習自我效能變異數同質性檢定摘要表……………………………….64
表4-1-6 四組後測學習自我效能單因子共變異數分析摘要表…………………………..64
表4-1-7四組後測學習自我效能事後成對比較摘要表……………………………………….65
表4-1-8四組後測學習自我效能事後成對比較整理….……………………………………….66
表4-2-1四組投入度描述性統計摘要表………………………………………………………………67
表4-2-2 四組投入度變異數同質性檢定……………………………………………………………..67
表4-2-3 四組投入度Welch、Brown-Forsythe檢定摘要表………………………………..67
表4-2-4四組投入度事後多重比較摘要表…………………………………………………………..68
表4-2-5四組投入度事後成對比較整理….…………………………………………………………..69
表4-2-6 行為投入度變異數同質性檢定……………………………………………………………..70
表4-2-7 行為投入度單因子變異數分析摘要表………………………………………………….70
表4-2-8 情緒投入度變異數同質性檢定……………………………………………………………..70
表4-2-9 情緒投入度Welch、Brown-Forsythe檢定摘要表……………………….……….71
表4-2-10情緒投入度事後多重比較摘要表……………………………………………….………..71
表4-2-11情緒投入度事後成對比較整理….……………………………………………….………..72
表4-2-12認知投入度變異數同質性檢定……………………………………………………………..72
表4-2-13認知投入度單因子變異數分析摘要表………………..……………………………....73
表4-2-14認知投入度事後多重比較摘要表……………………………………………….………..73
表4-2-15認知投入度事後成對比較整理….……………………………………………….………..74
表4-3-1 各組行為序列編碼觀察值一覽表………………………………………………………….76
表4-3-2 各組行為序列編碼次數比較表………………………………………………………………76
表4-3-3序列行為編碼次數與四組別卡方事後檢定比較表………………………………..77
表4-4-1 序列編碼轉換表 (警示加鼓勵回饋組)………………………………………………….77
表4-4-2 調整後殘差表 (警示加鼓勵回饋組)……………………………………………………..78
表4-4-3 序列編碼轉換表 (警示加鼓勵回饋組)………………………………………………….79
表4-4-4 調整後殘差表 (警示加鼓勵回饋組)……………………………………………………..79
表4-4-5 序列編碼轉換表 (警示回饋組)……………………………………………………………..80
表4-4-6 調整後殘差表 (警示回饋組)…………………………………………………………………80
表4-4-7 序列編碼轉換表(無回饋組)…………………………………………………………………..81
表4-4-8 調整後殘差表 (無回饋組)…………………………………………………………………….82
表5-1-1 學習自我效能統計分析簡表…………………………………………………………………84
表5-2-1 投入度統計分析簡表…………………………………………………………………………….86

圖目錄
圖1-8-1 研究流程圖…………………………………………………………………………….……………….10
圖2-1-1 智慧化數位學習模型……………………………………………………………………………...12
圖2-1-2 智慧整合感控系統組成元素圖…………………………………………………………….…14
圖2-1-3 個人化學習基礎運作過程架構圖……………………………………………………….….17
圖2-1-4 學習者接收回饋概念圖……………………………………………………………………….….18
圖3-1-1 研究架構圖(第一部分預試)……………………………………………………………….……34
圖3-1-2研究架構圖(智慧化回饋功能與學習自我效能、投入度部分)………………35
圖3-3-1 施測流程圖……………………………………………………………………………………………..37
圖3-4-1 學習評台登入前畫面………………………………………………………………………………44
圖3-4-2 學習平台會員註冊報名畫面…………………………………………………………………..44
圖3-4-3 會員登入後畫面……………………………………………………………………………….…….45
圖3-4-4 學習者意見回饋欄………………………………………………………………………………….45
圖3-4-5 單元教材內容畫面(文本內容)………………………………………………………………..44
圖3-4-6 單元教材內容畫面(情境劇部分)……………………………………………………….…...44
圖3-4-7 單元教材內容畫面(想一想部分)…………………………………………………………….48
圖3-4-8 單元測驗內容畫面………………………………………………………………………………….48
圖3-4-9 系統回饋鼓勵示意圖………………………………………………………………………………50
圖3-4-10 系統回饋警示示意圖…………………………………………………………………………….50
圖3-5-1 「高閱讀時間群」行為編碼次數分配比例圓餅圖………………………………..57
圖4-4-1 警示加鼓勵組序列路徑圖………………………………………………………………………78
圖4-4-2 鼓勵組序列路徑圖…………………………………………………………………………….......79
圖4-4-3 警示組序列路徑圖………………………………………………………………………………….81
圖4-4-4 無回饋組序列路徑圖………………………………………………………………………………82
圖4-4-5 四組序列路徑圖………………………………………………………………………………………83

英文部份
Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542-550.
Ainscow, M. (2006). Responding to the Challenge of Learner Diversity: A briefing paper for the Teaching and Learning in 2020 Review. University of Manchester Faculty of Education.
Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis: Cambridge university press.
Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.
Bangert-Drowns, R. L., Kulik, C.-L. C., Kulik, J. A., & Morgan, M. (1991). The instructional effect of feedback in test-like events. Review of educational research, 61(2), 213-238.
Carini, R. M., Kuh, G. D., & Klein, S. P. (2006). Student engagement and student learning: Testing the linkages*. Research in higher education, 47(1), 1-32.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188.
Cheng, K.-H., & Tsai, C.-C. (2011). An investigation of Taiwan University students' perceptions of online academic help seeking, and their web-based learning self-efficacy. The Internet and Higher Education, 14(3), 150-157.
Cho, M.-H., & Cho, Y. (2014). Instructor scaffolding for interaction and students' academic engagement in online learning: Mediating role of perceived online class goal structures. The Internet and Higher Education, 21, 25-30.
Chookaew, S., Panjaburee, P., Wanichsan, D., & Laosinchai, P. (2014). A Personalized E-Learning Environment to Promote Student's Conceptual Learning on Basic Computer Programming. Procedia-Social and Behavioral Sciences, 116, 815-819.
Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in knowledge discovery and data mining.
Feng, Z. (2012). Personalized Learning Network Teaching Model. Physics Procedia, 24, 2026-2031.
Fournier, H., Kop, R., & Sitlia, H. (2011). The value of learning analytics to networked learning on a personal learning environment.
Frank, M., Reich, N., & Humphreys, K. (2003). Respecting the human needs of students in the development of e-learning. Computers & Education, 40(1), 57-70.
Gamalel-Din, S. A. (2010). Smart e-Learning: A greater perspective; from the fourth to the fifth generation e-learning. Egyptian Informatics Journal, 11(1), 39-48.
Garrison, D. R. (2011). E-learning in the 21st century: A framework for research and practice: Taylor & Francis.
Hamilton, N. W. (2002). Academic ethics: Problems and materials on professional conduct and shared governance: Greenwood Publishing Group.
Harandi, S. R. (2015). Effects of e-learning on Students’ Motivation. Procedia-Social and Behavioral Sciences, 181, 423-430.
Hou, H.-T. (2010). Applying Lag Sequential Calculation and Social Network Analysis to Detect Learners' Behavioral Patterns and Generate Automatic Learning Feedback-Scenarios for Educational MMORPG Games. Paper presented at the Digital Game and Intelligent Toy Enhanced Learning (DIGITEL), 2010 Third IEEE International Conference on.
Hou, H.-T. (2011). A case study of online instructional collaborative discussion activities for problem-solving using situated scenarios: An examination of content and behavior cluster analysis. Computers & Education, 56(3), 712-719.
Hou, H.-T. (2012). Exploring the behavioral patterns of learners in an educational massively multiple online role-playing game (MMORPG). Computers & Education, 58(4), 1225-1233.
Hou, H.-T. (2015). Integrating cluster and sequential analysis to explore learners’ flow and behavioral patterns in a simulation game with situated-learning context for science courses: A video-based process exploration. Computers in Human Behavior, 48, 424-435.
Jin, X., Wah, B. W., Cheng, X., & Wang, Y. (2015). Significance and challenges of big data research. Big Data Research, 2(2), 59-64.
Johnson, L., Becker, S., Estrada, V., & Freeman, A. (2014). Horizon Report: 2014 Higher Education.
Kay, R. H., & Knaack, L. (2009). Assessing learning, quality and engagement in learning objects: the Learning Object Evaluation Scale for Students (LOES-S). Educational Technology Research and Development, 57(2), 147-168.
Kim, S., Song, S.-M., & Yoon, Y.-I. (2011). Smart learning services based on smart cloud computing. Sensors, 11(8), 7835-7850.
Krause, U.-M., Stark, R., & Mandl, H. (2009). The effects of cooperative learning and feedback on e-learning in statistics. Learning and Instruction, 19(2), 158-170.
Kumar, V. S., Clemens, C., & Harris, S. (2015). Causal Models and Big Data Learning Analytics Ubiquitous Learning Environments and Technologies (pp. 31-53): Springer.
Lei, C.-U., Wan, K., & Man, K. L. (2013). Developing a Smart Learning Environment in Universities Via Cyber-Physical Systems. Procedia Computer Science, 17, 583-585.
Liem, G. A. D., & Martin, A. J. (2012). The Motivation and Engagement Scale: Theoretical framework, psychometric properties, and applied yields. Australian Psychologist, 47(1), 3-13.
Lin, P.-C., Hou, H.-T., Wu, S.-Y., & Chang, K.-E. (2014). Exploring college students' cognitive processing patterns during a collaborative problem-solving teaching activity integrating Facebook discussion and simulation tools. The Internet and Higher Education, 22, 51-56.
Luan, J. (2002). Data mining and its applications in higher education. New directions for institutional research, 2002(113), 17-36.
Ma, J., Han, X., Yang, J., & Cheng, J. (2015). Examining the necessary condition for engagement in an online learning environment based on learning analytics approach: The role of the instructor. The Internet and Higher Education, 24, 26-34.
Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588-599.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.
Marz, N., & Warren, J. (2015). Big Data: Principles and best practices of scalable realtime data systems: Manning Publications Co.
Mayer-Schönberger, V., & Cukier, K. (2014). Learning with Big Data: The Future of Education: Houghton Mifflin Harcourt.
Miliband, D. (2004). Personalised learning: building a new relationship with schools. Paper presented at the Speech by the Minister of State for School Standards to the North of England Education Conference.
Muis, K. R., Ranellucci, J., Trevors, G., & Duffy, M. C. (2015). The effects of technology-mediated immediate feedback on kindergarten students' attitudes, emotions, engagement and learning outcomes during literacy skills development. Learning and Instruction, 38, 1-13.
Narciss, S., Sosnovsky, S., Schnaubert, L., Andrès, E., Eichelmann, A., Goguadze, G., & Melis, E. (2014). Exploring feedback and student characteristics relevant for personalizing feedback strategies. Computers & Education, 71, 56-76.
Noh, K.-S., Ju, S.-H., & Jung, J.-T. (2011). An exploratory study on concept and realization conditions of smart learning. Journal of Digital Convergence, 9(2), 79-88.
Nunnally, J. C., & Bernstein, I. (1994). Psychological theory(3rd ed.): New York: McGraw-Hill.
O’Brien, H. L., & Toms, E. G. (2013). Examining the generalizability of the User Engagement Scale (UES) in exploratory search. Information Processing & Management, 49(5), 1092-1107.
Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert systems with applications, 41(4), 1432-1462.
Pintrich, P. R. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ).
Ritzhaupt, A. D., & Kealy, W. A. (2015). On the utility of pictorial feedback in computer-based learning environments. Computers in Human Behavior, 48, 525-534.
Russell, G. (2005). Behaviour and Sequential Analyses: Principles and Practice. Journal of Advanced Nursing, 52(2), 224-224.
Shehab, A., & Gamalel, D. (2010). Smart E-learning: A greater perspective; from the fourth to the fifth generation E-learning. Egyptian Informatics Journal Elsevier (accepted May 25, 2010).
Shen, D., Cho, M.-H., Tsai, C.-L., & Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. The Internet and Higher Education, 19, 10-17.
Sun, J. C. Y., & Rueda, R. (2012). Situational interest, computer self‐efficacy and self‐regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191-204.
Sun, J. C.-Y., Lin, C.-T., & Chou, C. (2016, July). Applying learning analytics to explore the influence of online learners' motivation on their online learning behavioral patterns. Paper presented at the International Congress on Advanced Applied Informatics / 5th International Conference on Learning Technologies and Learning Environments (LTLE 2016), Kumamoto, Japan
Sun, Y., Yuan, Y., & Wang, G. (2015). An on-line sequential learning method in social networks for node classification. Neurocomputing, 149, 207-214.
Sung, M. (2015). A Study of Adults’ Perception and Needs for Smart Learning. Procedia-Social and Behavioral Sciences, 191, 115-120.
Thompson, K., Ashe, D., Carvalho, L., Goodyear, P., Kelly, N., & Parisio, M. (2013). Processing and visualizing data in complex learning environments. American Behavioral Scientist, 0002764213479368.
Timmers, C., & Veldkamp, B. (2011). Attention paid to feedback provided by a computer-based assessment for learning on information literacy. Computers & Education, 56(3), 923-930.
Ulrich, C., & Nedelcu, A. (2015). MOOCs in Our University: Hopes and Worries. Procedia-Social and Behavioral Sciences, 180, 1541-1547.
Wang, S.-L., & Wu, P.-Y. (2008). The role of feedback and self-efficacy on web-based learning: The social cognitive perspective. Computers & Education, 51(4), 1589-1598.
Xing, W., Guo, R., Petakovic, E., & Goggins, S. (2015). Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory. Computers in Human Behavior, 47, 168-181.
Yang, T.-C., Chen, S. Y., & Hwang, G.-J. (2015). The influences of a two-tier test strategy on student learning: A lag sequential analysis approach. Computers & Education, 82, 366-377.
Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82-91.
宋鴻燕. (2004). 大學生 “網路自我效能感” 與學習動機特質: 以 “文化與心理健康” 通識課程為例. 通識教育, 11(4), 23-43.
李宜玫, & 孫頌賢. (2010). 大學生選課自主性動機與學習投入之關係. 教育科學研究期刊, 55(1), 155-182.
阮嘉玲. (2008). 以多維度判斷理論探討預警系統之圖像設計. 臺北科技大學工業工程與管理研究所學位論文, 1-55.
邱成欽, & 林弘昌. (2010). 影響企業員工使用數位學習意願之相關因素探討. 生活科技教育, 43(5), 9-26.
張振亨, & 陳思亮. (2010). 數位學習 (E-Learning) 的認識與應用. 2014 年, 5.
郭明堂. (2009). 影響危害標示警告文字及顏色之風險知覺因素: 情境, 性別與年齡, 危機管理學術研討會, 台灣台南.
郭真秀. (2008). 大學生電腦自我效能與學習策略對資訊能力影響之探討.
陳一平. (2011). 視覺心理學: 雙葉書廊.
陳鏗任. (2014). 大學院校應用學習分析之概況. 教育資料與圖書館學, 51(4), 1-36.
游師柔, & 孫之元. (2014). 大學生的網路自我效能對反釣魚行為與表現之影響. 國立臺灣科技大學人文社會學報, 10(2), 113-139.
蔣葆琳. (2001). 大學生在非同步網路教學環境中自我效能研究. 國立東華大學教育研究所碩士論文 (未出版).


連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
1. 以學習分析初探線上學習者之學習動機對其線上學習行為模式之影響:以校園學術研究倫理課程為例
2. 大學生線上學習感知、學習行為、學習成就與課程滿意度於線上學習模式與混成學習模式之關係研究─以自我決定理論觀點探討線上學習感知
3. 線上學習推廣之研究─以學習者觀點探討線上學習與傳統學習之比較
4. 建構線上學習社群以提升企業線上學習成效之研究
5. 線上學習者的成就目標以及其對遊戲化線上學習平台中的遊戲因子的態度
6. 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
7. 國中小教師線上學習滿意度之研究—以K12線上學習系統為例
8. 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
9. 結合回饋與信號設計之平板即時回饋系統對大學生情境興趣與課堂注意力之影響
10. 線上自我調節學習、投入度與認知負荷關係之中介效果研究:以結合線上自我調節學習策略之即時回饋線上學習環境為例
11. 探勘線上學習模式與探討學習模式對學習成效的影響
12. 學術自由下研究倫理的建構與應用:以生物資料庫對原住民基因採集為例
13. 藉由電子書學習系統探討學生線上學習準備度、線上學習行為與學習成果之間的關係-以微積分課程為例
14. 台灣地區社會與行為科學研究人員研究倫理概念之探討
15. 國中教師使用線上學習平台輔助教學意願之研究