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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 5-Sep-2019 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
dc.contributor.advisor 林怡伶zh_TW
dc.contributor.advisor Lin, Yi-Lingen_US
dc.contributor.author (Authors) 李明緯zh_TW
dc.contributor.author (Authors) Lee, Ming-Weien_US
dc.creator (作者) 李明緯zh_TW
dc.creator (作者) Lee, Ming-Weien_US
dc.date (日期) 2019en_US
dc.date.accessioned 5-Sep-2019 15:44:20 (UTC+8)-
dc.date.available 5-Sep-2019 15:44:20 (UTC+8)-
dc.date.issued (上傳時間) 5-Sep-2019 15:44:20 (UTC+8)-
dc.identifier (Other Identifiers) G0106356011en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/125527-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 106356011zh_TW
dc.description.abstract (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。zh_TW
dc.description.abstract (摘要) 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.en_US
dc.description.tableofcontents Chapter 1 INTRODUCTION 1
1-1 Background and Motivation 1
1-2 Research Questions 3
1-3 Research Method 5
Chapter 2 LITERATURE REVIEW 8
2-1 Orchestration in Learning Analytics 8
2-2 Dashboards and Visualizations in Learning Analytics 10
2-3 Visual Analytics in Learning Environment 11
Chapter 3 RESEARCH MODEL 17
3-1 Learning Goal Orientation, Format and Task 18
3-2 Learning Comprehension 20
3-3 Understanding of Visualization 22
3-4 Perceived Learning 23
Chapter 4 METHODOLOGY 25
4-1 Dataset 25
4-2 System Development and Interface 26
4-3 Search Fact Tasks and Inference Generation Tasks 30
4-4 Apparatus 33
4-5 Subjects and Experiment Procedure 33
4-6 Analysis Method 37
Chapter 5 DATA AND MEASUREMENTS 39
5-1 User Behavior and Perception Data 39
5-2 Eye-tracking Data 45
Chapter 6 MODEL SPECIFICATIONS 51
6-1 Log-based User Behavior and Perception Data Analysis 51
6-2 Eye-tracking Data - Fixation Analysis 57
6-3 Eye-tracking Data - Transition Analysis 64
Chapter 7 DISCUSSIONS 66
7-1 The Influence on User Behavior and Perception 66
7-2 Eye Movement and User Behavior 70
Chapter 8 CONCLUSION 80
REFERENCE 85
Appendix A: Visualizations with different format 94
Appendix B: Learning Goal Orientation Measurement Items 95		zh_TW
dc.format.extent 3074828 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106356011en_US
dc.subject (關鍵詞) 學習分析zh_TW
dc.subject (關鍵詞) 圖表理解zh_TW
dc.subject (關鍵詞) 資訊視覺化zh_TW
dc.subject (關鍵詞) 學習目標導向zh_TW
dc.subject (關鍵詞) 紙本考試zh_TW
dc.subject (關鍵詞) 教育科技協作zh_TW
dc.subject (關鍵詞) 眼動追蹤zh_TW
dc.subject (關鍵詞) learning analyticsen_US
dc.subject (關鍵詞) graph comprehensionen_US
dc.subject (關鍵詞) information visualizationen_US
dc.subject (關鍵詞) learning goal orientationen_US
dc.subject (關鍵詞) paper-based assessmenten_US
dc.subject (關鍵詞) classroom orchestration technologyen_US
dc.subject (關鍵詞) eye trackingen_US
dc.title (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zh_TW
dc.title (題名) The impact of individual differences on visual analytics of an orchestration platform: An empirical study using eye-trackingen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (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.		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU201901137en_US