政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/125527

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/125527

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文筆數/總筆數 : 113451/144438 (79%)
造訪人次 : 51278417 線上人數 : 806

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜尋範圍

查詢小技巧：

您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果

若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料

進階搜尋

主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於政大典藏 ‧ 管理

到手機版

政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 > Item 140.119/125527

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/125527

題名:	以眼動實證研究探討個人差異於教育輔助平台視覺分析上之影響 The impact of individual differences on visual analytics of an orchestration platform: An empirical study using eye-tracking
作者:	李明緯 Lee, Ming-Wei
貢獻者:	林怡伶 Lin, Yi-Ling 李明緯 Lee, Ming-Wei
關鍵詞:	學習分析圖表理解資訊視覺化學習目標導向紙本考試教育科技協作眼動追蹤 learning analytics graph comprehension information visualization learning goal orientation paper-based assessment classroom orchestration technology eye tracking
日期:	2019
上傳時間:	2019-09-05 15:44:20 (UTC+8)
摘要:	本研究著重於探討學習目標導向、視覺化圖表格式(折線圖、柱狀圖、雷達圖)與學習類型(程序性學習及推論學習)對學生在線上複習平台中複習紙本程式考試表現的影響。我們透過使用者研究及眼動儀，探討自行開發的視覺化系統之可行性。此研究總共募集了34 位曾經至少修習過一堂 Java 程式設計課的受測者，並收集了問卷資料、系統紀錄、眼動追蹤數據等相關資料進行後續分析。我們的實驗透過使用迴歸模型驗證學習目標導向、視覺化圖表格式以及學習類型對於使用者在視覺化分析上認知的影響，進而提出以實證研究分析視覺化學習的可行性。我們的實驗結果顯示具有較高學習目標導向的使用者在視覺化分析的輔助下，相對應會有較高的學習表現與學習認知。然而實驗結果也顯示，雷達圖因為組成較為複雜，會對使用者複習時的效率有負面影響。在學習類型方面，實驗結果顯示在視覺化分析的輔助下，使用者在資訊檢索類型的複習表現較推理發想類型更為優越。 We examined the impact of learning goal orientation, visualization format (line, bar and radar chart) and type of learning task (search fact vs. inference generation) upon a viewer’s perception of reviewing paper-based exams in an online virtual assessment environment. A lab experiment was conducted with an eye-tracker. System log, eye-tracking data and questionnaires were collected from 34 students who have taken at least one Java programming course. Our experiments demonstrate the empirical research practicality by using a regression model to validate the effect of learning goal orientation, format and task on user perceptions of visualization analytics. Our results show that the viewers with a high degree of learning goal orientation would have better learning perception of visualization material. Radar graph, however, would have a negative influence on the review performance due to its complicated composition. We also found that with the assistance of visualization analytics, users perform more efficiently on search fact tasks rather than inference generation tasks when reviewing programming exams.
參考文獻:	Albert, M. A., & Dahling, J. J. (2016). Learning goal orientation and locus of control interact to predict academic self-concept and academic performance in college students. Personality and Individual Differences, 97, 245–248. https://doi.org/10.1016/j.paid.2016.03.074 Ali, L., Hatala, M., Gašević, D., & Jovanović, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers and Education, 58(1), 470–489. Ali, N., & Peebles, D. (2013). The effect of Gestalt laws of perceptual organization on the comprehension of three-variable bar and line graphs. Human Factors, 55(1), 183–203. Bandura, A. (1993). Perceived self-efficacy in cognitive development and functioning. Educational Psychologist, 28(2), 117–148. Barzilai, S., & Blau, I. (2014). Scaffolding game-based learning: Impact on learning achievements, perceived learning, and game experiences. Computers & Education, 70, 65–79. Brusilovsky, P., Hsiao, I. H., & Folajimi, Y. (2011). QuizMap: Open social student modeling and adaptive navigation support with TreeMaps. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6964 LNCS, 71–82. Cascio, M. I., Botta, V. C., & Anzaldi, V. E. (2013). The role of self efficacy and internal locus of control in online learning. Journal of E-Learning and Knowledge Society, 9(3), 95–106. Caspi, A., & Blau, I. (2008). Social presence in online discussion groups: Testing three conceptions and their relations to perceived learning. Social Psychology of Education, 11(3), 323–346. Chang, M. M. (2005). Applying self-regulated learning strategies in a web-based instruction - An investigation of motivation perception. Computer Assisted Language Learning, 18(3), 217–230. Chen, I. S. (2017). Computer self-efficacy, learning performance, and the mediating role of learning engagement. Computers in Human Behavior, 72, 362–370. Conati, C., & Maclaren, H. (2010). Exploring the role of individual differences in information visualization. Proceedings of the Working Conference on Advanced Visual Interfaces, 199–206. Crawford, E. R., LePine, J. A., & Rich, B. L. (2010). Linking job demands and resources to employee engagement and burnout: A theoretical extension and meta-analytic test. Journal of Applied Psychology, 95(5), 834–848. Cui, W., Zhou, H., Qu, H., Wong, P. C., & Li, X. (2008). Geometry-based edge clustering for graph visualization. IEEE Transactions on Visualization and Computer Graphics, 14(6), 1277–1284. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. Debicki, B. J., Kellermanns, F. W., Barnett, T., Pearson, A. W., & Pearson, R. A. (2016). Beyond the Big Five: The mediating role of goal orientation in the relationship between core self-evaluations and academic performance. International Journal of Management Education, 14(3), 273–285. Denny, P., Luxton-Reilly, A., & Hamer, J. (2008). The PeerWise system of student contributed assessment questions. Conferences in Research and Practice in Information Technology Series, 78, 69–74. Dillenbourg, P. (2013). Design for classroom orchestration. Computers and Education, 69, 485–492. https://doi.org/10.1016/j.compedu.2013.04.013 Dillenbourg, P., & Jermann, P. (2010). Technology for classroom orchestration. New Science of Learning: Cognition, Computers and Collaboration in Education, 525–552. Eison, J. A. (1979). The development and validation of a scale to assess different student orientations towards grades and learning. Ekstrom, R. B., French, J. W., & Harman, H. H. (1976). Manual for Kit of factor-referenced cognitive tests. Educational testing service Princeton, NJ. Few, S. (2005). Keep Radar Graphs Below the Radar–Far Below. Perceptual Edge, (May), 1–5. Freedman, E. G., Shah, P., & Vekiri, I. (2005). The comprehension of quantitative information in graphical displays. The Cambridge Handbook of Visuospatial Thinking, 426–476. Gansner, E. R., & North, S. C. (1999). An Open Graph Visualization System and Its Applications. Software - Practice and Experience, 30(May 1999), 1203–1233. Gašević, D., Dawson, S., & Siemens, G. (2015). Let ’ s not forget : Learning analytics are about learning. TechTrends, 59(1), 64–71. Gibson, D., & de Freitas, S. (2016). Exploratory Analysis in Learning Analytics. Technology, Knowledge and Learning, 21(1), 5–19. Goldberg, J. H., & Kotval, X. P. (1999). Computer interface evaluation using eye movements: methods and constructs Joseph. International Journal of Industrial Ergonomics, 24(6), 631–645. Govaerts, S., Verbert, K., Duval, E., & Pardo, A. (2012). The student activity meter for awareness and self-reflection. Proceedings of the 2012 ACM Annual Conference Extended Abstracts on Human Factors in Computing Systems Extended Abstracts - CHI EA ’12, 869–884. Green, Tear Marie, & Fisher, B. (2010). Towards the personal equation of interaction: The impact of personality factors on visual analytics interface interaction. VAST 10 - IEEE Conference on Visual Analytics Science and Technology 2010, Proceedings, (November), 203–210. Green, Tera Marie, Jeong, D. H., & Fisher, B. (2010). Using personality factors to predict interface learning performance. Proceedings of the Annual Hawaii International Conference on System Sciences, 1–10. Helfman, J. I., & Goldberg, J. H. (2007). Selecting the Best Graph Based on Data, Tasks, and User Roles. Usability Professionals’ Association, UPA Conference on Patterns: Blueprints for Usability, Austin, TX, (July). Hsiao, I-Han. (2016). Mobile Grading Paper-Based Programming Exams: Automatic Semantic Partial Credit Assignment Approach. In K. Verbert, M. Sharples, & T. Klobučar (Eds.), Adaptive and Adaptable Learning (pp. 110–123). Cham: Springer International Publishing. Hsiao, I-Han, Pandhalkudi Govindarajan, S. K., & Lin, Y.-L. (2016). Semantic visual analytics for today’s programming courses. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK ’16, 48–53. Hsiao, I. -Han, & Brusilovsky, P. (2012). Motivational Social Visualizations for Personalized E-Learning (pp. 153–165). https://doi.org/10.1007/978-3-642-33263-0_13 Hsiao, I. H., Huang, P. K., & Murphy, H. (2017). Uncovering reviewing and reflecting behaviors from paper-based formal assessment. Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 319–328. Hsiao, I. Han, Bakalov, F., Brusilovsky, P., & König-Ries, B. (2013). Progressor: Social navigation support through open social student modeling. New Review of Hypermedia and Multimedia, 19(2), 112–131. Hsiao, I. Han, & Lin, Y. L. (2017). Enriching programming content semantics: An evaluation of visual analytics approach. Computers in Human Behavior, 72, 771–782. Hsiao, I., Sosnovsky, S., & Brusilovsky, P. (2008). Adaptive Navigation Support in an E-Learning System for Java Programming. Journal of Computer Assisted Learning, 26(4), 270–283. Hu, P. J. H., & Hui, W. (2012). Examining the role of learning engagement in technology-mediated learning and its effects on learning effectiveness and satisfaction. Decision Support Systems, 53(4), 782–792. Joo, Y. J., Lim, K. Y., & Kim, J. (2013). Locus of control, self-efficacy, and task value as predictors of learning outcome in an online university context. Computers & Education, 5(4), 149–158. Just, M. A., & Carpenter, P. A. (1976). Eye fixations and cognitive processes. Cognitive Psychology, 8(4), 441–480. Klein, H. J., Noe, R. A., & Wang, C. (2006). Motivation To Learn and Course Outcomes: the Impact of Delivery Mode, Learning Goal Orientation, and Perceived Barriers and Enablers. Personnel Psychology, 59(3), 665–702. Koffka, K. (2013). Principles of Gestalt psychology. Routledge. Li, R., Pelz, J., Shi, P., & Haake, A. R. (2012). Learning image-derived eye movement patterns to characterize perceptual expertise. Proceedings of the Annual Meeting of the Cognitive Science Society, 34(34), 1900–1905. Lu, Y., & Hsiao, I.-H. (2016). Seeking Programming-related Information from Large Scaled Discussion Forums, Help or Harm? Edm ’16, 442–447. Malcolm, G. L., & Henderson, J. M. (2009). The effects of target template specificity on visual search in real-world scenes: Evidence from eye movements. Journal of Vision, 9(11), 8–8. https://doi.org/10.1167/9.11.8 Mazur, D. J., & Hickam, D. H. (1993). Patients’ and Physicians’ Interpretations of Graphic Data Displays. Medical Decision Making, 13(1), 59–63. Michalski, R. S. (1993). Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning. Machine Learning, 11(2), 111–151. Nesbitt, K. V., & Friedrich, C. (2002). Applying Gestalt principles to animated visualizations of network data. Proceedings of the International Conference on Information Visualisation, 2002-Janua(January), 737–743. Okan, Yashmina, Garcia-Retamero, R., Cokely, E. T., & Maldonado, A. (2011). Individual Differences in Graph Literacy: Overcoming Denominator Neglect in Risk Comprehension. Journal of Behavioral Decision Making, 21, 453–456. Okan, Yasmina, Garcia-Retamero, R., Galesic, M., & Cokely, E. T. (2012). When Higher Bars Are Not Larger Quantities: On Individual Differences in the Use of Spatial Information in Graph Comprehension. Spatial Cognition & Computation, 12(2–3), 195–218. Papamitsiou, Z., & Economides, A. A. (2015). Temporal learning analytics visualizations for increasing awareness during assessment. RUSC. Universities and Knowledge Society Journal, 12(3), 129. Paredes, Y. V., Huang, P. K., Murphy, H., & Hsiao, I. H. (2017). A Subjective Evaluation of Web-based Programming Grading Assistant: Harnessing digital footprints from paper-based assessments. CEUR Workshop Proceedings, 1828, 23–30. Patel, K., Bancroft, N., Drucker, S. M., Fogarty, J., Ko, A. J., & Landay, J. A. (2010). Gestalt: Integrated Support for Implementation and Analysis in Machine Learning. Uist, 37–46. Payne, S. C., Youngcourt, S. S., & Beaubien, J. M. (2007). A meta-analytic examination of the goal orientation nomological net. Journal of Applied Psychology, 92(1), 128–150. https://doi.org/10.1037/0021-9010.92.1.128 Pinker, S. (1990). A theory of graph comprehension. Artificial Intelligence and the Future of Testing, 73–126. Ratwani, R. M., & Boehm-davis, D. A. (2008). Thinking graphically: Connecting vision and cognition during graph comprehension. Journal of Experimental Psychology, (703). Ratwani, R. M., & Trafton, J. G. (2008). Shedding light on the graph schema: Perceptual features versus invariant structure. Psychonomic Bulletin and Review, 15(4), 757–762. Rovai, A. P., & Baker, J. D. (2005). Gender Differences in Online Learning: Sense of Community, Perceived Learning, and Interpersonal Interactions. Quarterly Review of Distance Education, 6(1), 31–44. Shah, P., & Freedman, E. G. (2011). Bar and line graph comprehension: An interaction of top-down and bottom-up processes. Topics in Cognitive Science, 3(3), 560–578. Shah, P., Mayer, R. E., & Hegarty, M. (1999). Graphs as Aids to Knowledge Construction: Signaling Techniques for Guiding the Process of Graph Comprehension. Journal of Educational Psychology, 91(4), 690–702. Siemens, G., & Baker, R. S. J. de. (2012). Learning analytics and educational data mining: towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 252–254. Siemens, George, & Gasevic, D. (2012). Guest editorial-learning and knowledge analytics. Journal of Educational Technology and Society, 15(3). Steichen, B., Carenini, G., & Conati, C. (2013). User-adaptive information visualization: using eye gaze data to infer visualization tasks and user cognitive abilities. Iui, 317–328. Sun, R., Merrill, E., & Peterson, T. (2001). From implicit skills to explicit knowledge_ a bottom-up model of skill learning. Cognitive Science, 25, 203–244. Swan, K. (2001). Virtual interaction : Design factors affecting student satisfaction and perce ... Education, 22(2), 306–331. Retrieved from Toker, D., Conati, C., Carenini, G., & Haraty, M. (2012). Towards adaptive information visualization: On the influence of user characteristics. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7379 LNCS, 274–285. Toker, D., Conati, C., Steichen, B., & Carenini, G. (2013). Individual user characteristics and information visualization: connecting the dots through eye tracking. In proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 295–304). VandeWalle, D. M. (1997). Development and validation of a work domain achievement goals instrument. Educational and Psychological Measurement, 8(6), 995–1015. Venkatesh, V. (2000). Determinants of perceived ease of use: integrating perceived behavioral control, computer anxiety and enjoyment into the Technology Acceptance Mode. Information Systems Research, 11(4), 342–365. Wang, C., Shannon, D. M., & Ross, M. E. (2010). Students ’ characteristics , self-regulated learning , technology self-ef fi cacy , and course outcomes in online learning. Dissertation, 34(3), 302–323. Wang, M., Wu, B., Kinshuk, Chen, N. S., & Spector, J. M. (2013). Connecting problem-solving and knowledge-construction processes in a visualization-based learning environment. Computers and Education, 68, 293–306. Wu, D., Hiltz, Roxanne, S., & Bieber, M. (2010). Acceptance of educational technology: field studies of asynchronous participatory examinations. Communications of the Association for Information Systems, 26(1), 21. Xanthopoulou, D., Bakker, A. B., Demerouti, E., & Schaufeli, W. B. (2009). Reciprocal relationships between job resources, personal resources, and work engagement. Journal of Vocational Behavior, 74(3), 235–244. Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: Self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human Computer Studies, 59(4), 431–449. Yigitbasioglu, O. M., & Velcu, O. (2012). A review of dashboards in performance management: Implications for design and research. International Journal of Accounting Information Systems, 13(1), 41–59. Zajac, D. M., Button, S. B., & Mathieu, J. E. (1996). Goal Orientation in Organizational Research : A Conceptual and Empirical Foundation. Organizational Behavior and Human Decision Processes, 67(1), 26–48. Ziemkiewicz, C., Crouser, R. J., Yauilla, A. R., Su, S. L., Ribarsky, W., & Chang, R. (2011). How locus of control influences compatibility with visualization style. VAST 2011 - IEEE Conference on Visual Analytics Science and Technology 2011, Proceedings, 81–90. Ziemkiewicz, C., & Kosara, R. (2009). Preconceptions and individual differences in understanding visual metaphors. Computer Graphics Forum, 28(3), 911–918.
描述:	碩士國立政治大學資訊管理學系 106356011
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0106356011
資料類型:	thesis
DOI:	10.6814/NCCU201901137
顯示於類別:	[資訊管理學系] 學位論文

文件中的檔案:

檔案	大小	格式	瀏覽次數
601101.pdf	3002Kb	Adobe PDF2	58	檢視/開啟

在政大典藏中所有的資料項目都受到原著作權保護.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回饋