臺灣博碩士論文加值系統

本研究旨在應用學習分析探討學習動機與線上學習行為模式之影響,以臺 灣北部地區某三所國立大學便利取樣 161 位研究生為樣本。研究者自行架設具有 行為追蹤記錄功能之學術研究倫理線上課程,蒐集登入次數、教材閱讀次數、測 驗失敗次數、教材閱讀時間與測驗時間並與學習動機問卷及使用者投入度問卷進 行統計分析。接著將以學習動機與線上教材閱讀時間為學習者進行階層式群集分 析,並將行為追蹤記錄編碼為認真閱讀、隨意閱讀、多工閱讀、測驗成功、測驗 失敗,暫離六個編碼,以序列分析深入探討各群集之線上學習行為模式差異。
本研究發現學習動機與實際閱讀時間並無顯著關聯,且學習者僅帄均閱讀一 半的教材即通過本課程,但經過本課程後顯著提升學習者之學習動機。然而,高 動機學習者在多工情境中表現出相對認真的學習模式,且線上教材閱讀時間為顯 著認真進行線上課程之指標。分析亦顯示學習者偏好以少量多次方式進行線上學 習,使用者投入度與實際行為投入並無顯著關聯,且遭遇測驗失敗等負面回饋與 使用投入度呈負相關。因此,本研究認為線上學習者普遍而言傾向被動學習,但 學習動機仍是一個影響學習行為之因素。
本研究建議,未來線上學習帄臺或教學設計應設法提高學習動機,例如可結 合有趣的多媒體動畫;且應縮短課程單元長度、避免給予負面回饋。此外,線上 教材閱讀時間可作為衡量線上學習者認真學習的標準,未來線上學習帄臺應設計 學習分析功能,供線上課程管理者或教師給予學習者引導與回饋。此研究成果希 望能作為從事線上帄臺設計或課程管理之開發人員、教師與研究者之參考。

The purpose of this study was to explore how online learners’ motivation influences their online learning behavioral patterns. Learning analytics was applied to analyze the learning behaviors in an online academic research course. Participants were 161 graduate students at three national universities in northern Taiwan. A self-developed 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 and exam. The instruments included the learning motivation scale and the user engagement scale. Learners were clustered into three groups based on their learning motivation and online reading duration using the hierarchical cluster analysis. In order to understand the differences in online learning behaviors among these three groups, system logs were transformed into six codes for lag sequential analysis: intensive reading, skimmed reading, multi-tasking reading, passing exam, failing exam, and offline.
The results showed that even though the learners completed the task after reading only half of the course materials, the online course still significantly increased learners’ motivation. Students in the high-motivation group showed intensive reading behaviors in the multi-tasking reading situation. Online reading duration was a significant predictor of the serious learning behavior. In addition, user engagement was not correlated with behavior engagement, online learners preferred to learn
IIfragmentally, and negative feedback, such as failing the exam, was negatively correlated with the user engagement. The findings showed that online learners tended to be passive, while learning motivation was still a key factor which facilitated the learning behavior.
This study suggests that online learning platforms should be designed to increase learning motivation, for examples, incorporating interesting multimedia, decreasing the learning duration for each unit, and avoiding negative feedback. Furthermore, online reading duration could be used as a key indicator to evaluate how seriously the students study online. Future online learning platforms could collect log data and provide visual summary to the instructors. The findings of this study may serve as references for learning management system or online course developers, instructors, and researchers.

摘要 ...........................................................................................................................................I Abstract.................................................................................................................................. II
致謝 ........................................................................................................................................ IV 目錄 ..........................................................................................................................................V 表目錄 ...................................................................................................................................VII 圖目錄 ................................................................................................................................. VIII
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...........................................................................................................................5 第五節 名詞釋義...........................................................................................................................6 第六節 研究章節配置...................................................................................................................7 第七節 研究流程...........................................................................................................................8
第二章 文獻探討..................................................................................................................9 第一節 學習分析...........................................................................................................................9 第二節 學習動機對線上學習的影響......................................................................................23 第三節 總結..................................................................................................................................30
第三章 研究方法...............................................................................................................32 第一節 研究架構.........................................................................................................................32
V第二節 研究對象與設計............................................................................................................34 第三節 研究量表.........................................................................................................................36 第四節 研究工具:自建線上學習帄臺..................................................................................38 第五節 資料處理與分析............................................................................................................47
第四章 研究結果...............................................................................................................51 第一節 行為次數分析.................................................................................................................51 第二節 群集分析.........................................................................................................................54 第三節 行為模式次數分析........................................................................................................61 第四節 行為模式序列分析........................................................................................................63
第五章 討論與建議...........................................................................................................69 第一節 線上學習行為模式........................................................................................................69 第二節 投入度與線上學習行為...............................................................................................72 第三節 數位學習動機與線上學習行為模式.........................................................................74 第四節 研究限制與建議............................................................................................................77
參考文獻............................................................................................................................... 79 附錄一 數位學習動機量表因素負荷表(預詴).......................................................86 附錄二 投入度量表因素負荷表(正式施測)...........................................................88

中文部分 教育部(2014)。校園學術倫理教育與機制發展計畫。取自:http://ethics.nctu.edu.tw/
英文部分
Agudo-Peregrina, #westeur002# . F., Iglesias-Pradas, S., Conde-Gonz#westeur034#lez, M. #westeur002# ., &; Hern#westeur034#ndez-Garc#westeur046#a, #westeur002# . (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(0), 542-550. doi: 10.1016/j.chb.2013.05.031
Ali, L., Asadi, M., Gašević, D., Jovanović, J., &; Hatala, M. (2013). Factors influencing beliefs for adoption of a learning analytics tool: An empirical study. Computers &; Education, 62(0), 130-148. doi: 10.1016/j.compedu.2012.10.023
Austin, K. A., Gorsuch, G. J., Lawson, W. D., &; Newberry, B. P. (2011). Developing and designing online engineering ethics instruction for international graduate students. Instructional Science, 39(6), 975-997.
Bakeman, R. (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.
Black, E. W., Dawson, K., &; Priem, J. (2008). Data for free: Using LMS activity logs to measure community in online courses. The Internet and Higher Education, 11(2), 65-70. doi: 10.1016/j.iheduc.2008.03.002
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.
Chen, K. C., &; Jang, S. J. (2010). Motivation in online learning: Testing a model of self-determination theory. Computers in Human Behavior, 26(4), 741-752. doi: 10.1016/j.chb.2010.01.011
Choi, K., &; Kim, D. Y. (2013). A cross cultural study of antecedents on career preparation behavior: Learning motivation, academic achievement, and career decision self-efficacy. Journal of Hospitality, Leisure, Sport &; Tourism Education, 13(0), 19-32. doi: 10.1016/j.jhlste.2013.04.001
79Davis, F. D., Bagozzi, R. P., &; Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace1. Journal of applied social psychology, 22(14), 1111-1132.
Davis, F. D., Bagozzi, R. P. &; Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
Davis Jr., F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology.
Deci, E. L., &; Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior, : Plenum, New York.
Eryilmaz, E., Chiu, M. M., Thoms, B., Mary, J., &; Kim, R. (2014). Design and evaluation of instructor-based and peer-oriented attention guidance functionalities in an open source anchored discussion system. Computers &; Education, 71, 303-321. doi: 10.1016/j.compedu.2013.08.009
Fredricks, J. A., Blumenfeld, P., Friedel, J., &; Paris, A. (2005). School engagement What do children need to flourish? (pp. 305-321): Springer.
Fredricks, J. A., Blumenfeld, P. C., &; Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of educational research, 74(1), 59-109.
Gardner, J. S. (2008). Simultaneous media usage: Effects on attention. Virginia Polytechnic Institute and State University.
Gil-Flores, J., Torres-Gordillo, J.-J., &; Perera-Rodr#westeur046#guez, V.-H. (2012). The role of online reader experience in explaining students’ performance in digital reading. Computers &; Education, 59(2), 653-660. doi: 10.1016/j.compedu.2012.03.014
Guerbas, A., Addam, O., Zaarour, O., Nagi, M., Elhajj, A., Ridley, M., &; Alhajj, R. (2013). Effective web log mining and online navigational pattern prediction. Knowledge-Based Systems, 49(0), 50-62. doi: 10.1016/j.knosys.2013.04.014
Haythornthwaite, C., de Laat, M., &; Dawson, S. (2013). Introduction to the Special Issue on Learning Analytics Introduction. American Behavioral Scientist, 57(10), 1371-1379. doi: 10.1177/0002764213498850
He, W. (2013). Examining students’ online interaction in a live video streaming environment using data mining and text mining. Computers in Human Behavior, 29(1), 90-102. doi: 10.1016/j.chb.2012.07.020
80
Horzum, M. B., #westeur023#nder, İ., &; Beşoluk, Ş. (2014). Chronotype and academic achievement among online learning students. Learning and Individual Differences, 30(0), 106-111. doi: 10.1016/j.lindif.2013.10.017
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. (2012a). Analyzing the Learning Process of an Online Role-Playing Discussion Activity. Educational Technology &; Society, 15(1), 211-222.
Hou, H. T. (2012b). Exploring the behavioral patterns of learners in an educational massively multiple online role-playing game (MMORPG). Computers &; Education, 58(4), 1225-1233. doi: 10.1016/j.compedu.2011.11.015
Hou, H. T., &; Li, M. C. (2014). Evaluating multiple aspects of a digital educational problem-solving-based adventure game. Computers in Human Behavior, 30(0), 29-38. doi: 10.1016/j.chb.2013.07.052
Hou, H. T., &; Wu, S. Y. (2011). Analyzing the social knowledge construction behavioral patterns of an online synchronous collaborative discussion instructional activity using an instant messaging tool: A case study. Computers &; Education, 57(2), 1459-1468. doi: 10.1016/j.compedu.2011.02.012
Hu, Y. H., Lo, C. L., &; Shih, S. P. (2014). Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior, 36(0), 469-478. doi: 10.1016/j.chb.2014.04.002
Hung, J. L., &; Zhang, K. (2008). Revealing online learning behaviors and activity patterns and making predictions with data mining techniques in online teaching. MERLOT Journal of Online Learning and Teaching.
Johnson, L., Adams, S., Cummins, M., Estrada, V., Freeman, A., &; Ludgate, H. (2013). The NMC Horizon Report: 2013 Higher Education Edition. Austin, Texas: The New Media Consortium.
Johnson, L., Adams, S., and Cummins, M. . (2012). The NMC Horizon Report: 2012 Higher Education Edition. Austin, Texas: The New Media Consortium.
Johnson, L., Becker, S., Estrada, V., &; Freeman, A. (2014). The NMC Horizon Report: 2014 Higher Education Edition. Austin, Texas: The New Media Consortium.
Johnson, L., Smith, R., Willis, H., Levine, A., &; Haywood, K. (2011). The NMC Horizon Report: 2011 Higher Education Edition. Austin, Texas: The New
81
Media Consortium.
Kl#westeur055#sgen, W., &; Zytkow, J. M. (2002). Handbook of data mining and knowledge
discovery: Oxford University Press, Inc.
Kong, J. S. L., Kwok, R. C. W., &; Fang, Y. (2012). The effects of peer intrinsic and
extrinsic motivation on MMOG game-based collaborative learning.
Information &; Management, 49(1), 1-9. doi: 10.1016/j.im.2011.10.004 Kudryavtseva, M. G. (2014). Possibilities of distance learning as a means of foreign
language learning motivation among students of economics. Procedia - Social and Behavioral Sciences, 152(0), 1214-1218. doi: 10.1016/j.sbspro.2014.09.301
Lee, M. K. O., Cheung, C. M. K., &; Chen, Z. (2005). Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation. Information &; Management, 42(8), 1095-1104. doi: 10.1016/j.im.2003.10.007
Lepper, M. R., &; Hodell, M. (1989). Intrinsic motivation in the classroom. Research on motivation in education, 3, 73-105.
Liu, C. C., Cheng, Y. B., &; Huang, C. W. (2011). The effect of simulation games on the learning of computational problem solving. Computers &; Education, 57(3), 1907-1918. doi: 10.1016/j.compedu.2011.04.002
Liu, Z. (2005). Reading behavior in the digital environment: Changes in reading behavior over the past ten years. Journal of documentation, 61(6), 700-712.
Lohr, S. (2012). The age of big data. New York Times, 11.
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(0), 26-34. doi: 10.1016/j.iheduc.2014.09.005
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. doi: 10.1016/j.compedu.2009.09.008
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.
McAfee, A., &; Brynjolfsson, E. (2012). Big data: the management revolution. Harvard business review, 90(10), 60-68.
Moore, M. G. (1989). Editorial: Three types of interaction. 82
Mustapaşa, O., Karahoca, A., Karahoca, D., &; Uzunboylu, H. (2011). “Hello world”, web mining for e-learning. Procedia Computer Science, 3(0), 1381-1387. doi: 10.1016/j.procs.2011.01.019
Nunnally, J. C., &; Bernstein, I. (1994). Psychological theory(3rd ed.): New York: McGraw-Hill.
O'Brien, H. L., &; Toms, E. G. (2008). What is user engagement? A conceptual framework for defining user engagement with technology. Journal of the American Society for Information Science and Technology, 59(6), 938-955.
O'Brien, H. L., &; Toms, E. G. (2010). The development and evaluation of a survey to measure user engagement. Journal of the American Society for Information Science and Technology, 61(1), 50-69.
O’Brien, H. L., &; Toms, E. G. (2012). Examining the generalizability of the User Engagement Scale (UES) in exploratory search. Information Processing &; Management, 49(5), 1092-1107. doi: 10.1016/j.ipm.2012.08.005
Ophir, E., Nass, C., &; Wagner, A. D. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences, 106(37), 15583-15587.
Pellas, N. (2014). The influence of computer self-efficacy, metacognitive self-regulation and self-esteem on student engagement in online learning programs: Evidence from the virtual world of Second Life. Computers in Human Behavior, 35(0), 157-170. doi: 10.1016/j.chb.2014.02.048
Pe#westeur050#a-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications, 41(4, Part 1), 1432-1462. doi: 10.1016/j.eswa.2013.08.042
Perera, D., Kay, J., Koprinska, I., Yacef, K., &; Za#westeur048#ane, O. R. (2009). Clustering and sequential pattern mining of online collaborative learning data. Knowledge and Data Engineering, IEEE Transactions on, 21(6), 759-772.
Pintrich, P. R. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ).
Pintrich, P. R., &; Schunk, D. H. (1996). Motivation in education: Theory, research, and applications: Merrill Englewood Cliffs, NJ.
Raab, R. T., Ellis, W. W., &; Abdon, B. R. (2001). Multisectoral partnerships in e-learning: a potential force for improved human capital development in the Asia Pacific. The Internet and Higher Education, 4(3), 217-229.
Rideout, V. J., Foehr, U. G., &; Roberts, D. F. (2010). Generation M [superscript 2]: 83
Media in the Lives of 8-to 18-Year-Olds. Henry J. Kaiser Family Foundation. Ruip#westeur042#rez-Valiente, J. A., Mu#westeur050#oz-Merino, P. J., Leony, D., &; Delgado Kloos, C. (2014).
ALAS-KA: A learning analytics extension for better understanding the learning process in the Khan Academy platform. Computers in Human Behavior(0). doi: 10.1016/j.chb.2014.07.002
Ryan, R. M., &; Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary educational psychology, 25(1), 54-67.
Saad#westeur042#, R. G., He, X., &; Kira, D. (2007). Exploring dimensions to online learning. Computers in Human Behavior, 23(4), 1721-1739. doi: 10.1016/j.chb.2005.10.002
Shukor, N. A., Tasir, Z., Van der Meijden, H., &; Harun, J. (2014). A Predictive Model to Evaluate Students’ Cognitive Engagement in Online Learning. Procedia - Social and Behavioral Sciences, 116(0), 4844-4853. doi: 10.1016/j.sbspro.2014.01.1036
Siemens, G. (2013). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 57(10), 1380-1400. doi: 10.1177/0002764213498851 Skinner, E., Furrer, C., Marchand, G., &; Kindermann, T. (2008). Engagement and
disaffection in the classroom: Part of a larger motivational dynamic? Journal
of educational psychology, 100(4), 765.
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. doi: 10.1111/j.1467-8535.2010.01157.x
Sung, Y. T., Hou, H. T., Liu, C. K., &; Chang, K. E. (2010). Mobile guide system using problem‐solving strategy for museum learning: a sequential learning behavioural pattern analysis. Journal of Computer Assisted learning, 26(2), 106-115.
Thompson, K., Ashe, D., Carvalho, L., Goodyear, P., Kelly, N., &; Parisio, M. (2013). Processing and Visualizing Data in Complex Learning Environments. American Behavioral Scientist, 57(10), 1401-1420. doi: 10.1177/0002764213479368
Tseng, S.-C., &; Tsai, C.-C. (2010). Taiwan college students' self-efficacy and motivation of learning in online peer assessment environments. Internet and
84
Higher Education, 13(3), 164-169. doi: 10.1016/j.iheduc.2010.01.001 Venkatesh, V., Morris, M. G., Davis, G. B., &; Davis, F. D. (2003). User acceptance of
information technology: Toward a unified view. MIS Quarterly, 27(3).
Wang, S. L., &; Lin, S. S. J. (2007). The effects of group composition of self-efficacy and collective efficacy on computer-supported collaborative learning.
Computers in Human Behavior, 23(5), 2256-2268. doi:
10.1016/j.chb.2006.03.005
Wulder, M. A. (2005). A practical guide to the use of selected multivariate statistics:
Canadian Forest Service Pacific Forestry Centre.
Yoo, S. J., Han, S.-h., &; Huang, W. (2012). The roles of intrinsic motivators and
extrinsic motivators in promoting e-learning in the workplace: A case from South Korea. Computers in Human Behavior, 28(3), 942-950. doi: 10.1016/j.chb.2011.12.015
Z#westeur034#mečn#westeur046#k, R. (2014). The Measurement of Employee Motivation by Using Multi-factor Statistical Analysis. Procedia - Social and Behavioral Sciences, 109(0), 851-857.