本研究主要探討在電腦版圖形推理測驗中，圖/文認知風格、自我效能 及工作記憶，對閱讀行為及推理表現之影響。受試者共 53 名，包含大學 生及研究生參與本研究。本研究採用量化研究方法進行統計分析，使用問 卷調查法來了解受試者之認知風格、自我效能，以測驗方式探討受試者之 工作記憶，並且以 OGAMA 軟體記錄並量化閱讀行為及推理表現。 本研究結果顯示，圖形推理測驗中(1)圖/文認知風格者的自我效能及推 理表現並無差異，但在題目區各小圖樣凝視離散程度有顯著差異。(2)自我 效能對橫向閱讀行為有預測力，亦對推理表現有顯著預測力。(3)工作記憶 對自我效能無預測力，而工作記憶之數字順背廣度對比對閱讀行為有顯著 預測力、數字逆背廣度對推理表現有顯著預測力。(4)閱讀行為之題目區各 小圖樣凝視離散程度與橫向閱讀對推理表現有顯著預測力。最後本研究依 據研究結果進行討論，並針對教師的教學及未來後續研究提出相關建議。

This study attempted to investigate the role of visual/verbal cognitive styles, self-efficacy, and working memory in graphic reading behaviors and performance in computer-based Raven Progressive Matrices Test. A total of 53 undergraduate and graduate students participated in this study. Both eye-tracking techniques and statistical analysis were applied. The results indicated that in computer-based Raven Progressive Matrices Test, (1) no difference was found in self-efficacy and performance between visual and verbal groups, but a significant difference was found on the diversity of average fixation time for each cell of a matrix between these two groups. (2) self-efficacy significantly predicted row scanning behavior and performance. (3) working memory did not significantly predict self-efficacy, however, forward digit-span working memory significantly predicted toggle reading behavior, while backward digit-span working memory significantly predicted Raven test performance. (4) average fixation time on each cell of a matrix and row scanning behaviors significantly predicted Raven test performance. Finally, and implications and suggestions for future research were provided.

中文部分
王淑玲、林珊如(2000)。我國大學生學習動機與學習策略應用之研究。89
年國科會成果報告，編號 NSC 89-2413-H-011-001-S。
陳湘淳、李玉琇(2011)。記憶策略訓練對工作記憶容量的影響。教育心理
學報, 37(1)。
簡薇紋(2012)。圖/文認知風格與圖/文搜尋情境之適配性對自我效能、線上搜
尋行為及表現之影響(碩士論文)。國立台灣科技大學，台北市。
李若璿(2012)。在網路合作學習環境中，圖/文認知風格、團體效能與示範
作用對線上合作註記行為及團體表現之影響(碩士論文)。國立台灣科技
大學，台北市。
卓峯志(2009)台灣的帄面廣告應當如何進行圖文配置?-從本質論與建構論
觀點進行探討. 第十七屆中華民國廣告暨公共關係國際學術與實務研
討會論文集: 數位環境下的溝通策略

英文部分
Arthur, W., & Day, D. V. (1994). Development of a short form for the Raven advanced progressive matrices test. Educational and Psychological measurement, 54(2), 394-403.
Bandura, A. (1989). Regulation of cognitive processes through perceived self-efficacy. Developmental psychology, 25(5), 729.
Baker, L., & Brown, A. L. (1984). Metacognitive skills and reading. Handbook of reading research, 353-394.
Beattie, S., Woodman, T., Fakehy, M., & Dempsey, C. (2015). The role of performance feedback on the self-efficacy-performance relationship. Sport, Exercise, and Performance Psychology, 5(1), 1.
Bernacki, M. L., Nokes-Malach, T. J., & Aleven, V. (2015). Examining self-efficacy during learning: variability and relations to behavior, performance, and learning. Metacognition and Learning, 10(1), 99-117.
Berry, J. M., West, R. L., & Dennehey, D. M. (1989). Reliability and validity of the memory self-efficacy questionnaire. Developmental Psychology, 25, 701-713.
Bowles, R. P., & Salthouse, T. A. (2003). Assessing the age-related effects of proactive interference on working memory tasks using the Rasch model. Psychology and aging, 18(3), 608.
Canham, M., & Hegarty, M. (2010). Effects of knowledge and display design on comprehension of complex graphics. Learning and instruction, 20(2), 155-166.
Cao, J., & Nishihara, A. (2013). Viewing behaviors affected by slide features and learning style in slide video from a sequence analysis perspective. The Journal of Information and Systems in Education, 12(1), 1-12.
Carpenter, P. A., Just, M. A., & Shell, P. (1990). What one intelligence test measures: a theoretical account of processing in the Raven progressive matrices test. Psychological Review, 97, 404– 431.
Chen, S. Y., & Liu, X. (2011). Mining students' learning patterns and performance in Web-based instruction: A cognitive style approach. Interactive Learning Environments, 19(2), 179-192.
Chen, C. M., & Wu, C. H. (2015). Effects of different video lecture types on sustained attention, emotion, cognitive load, and learning performance. Computers & Education, 80, 108-121.
Childers, T. L., Houston, M. J., & Heckler, S. E. (1985). Measurement of individual differences in visual versus verbal information processing.
Journal of Consumer Research, 12, 125-134.
Clark, C. R., Veltmeyer, M. D., Hamilton, R. J., Simms, E., Paul, R., Hermens, D., & Gordon, E. (2004). Spontaneous alpha peak frequency predicts working memory performance across the age span. International Journal of Psychophysiology, 53(1), 1-9.
Conklin, H. M., Curtis, C. E., Katsanis, J., & Iacono, W. G. (2000). Verbal working memory impairment in schizophrenia patients and their first-degree relatives: Evidence from the digit span task. American Journal of Psychiatry.
Conway, A. R. A., Kane, M. J., Buntig, M. F., Hambrick, D. Z., Wilhelm, O., & Engle, R. W. (2005). Working memory span tasks: A methodological review and user’s guide. Psychonomic Bulletin and Review, 12, 769–786.
Curie, A., Brun, A., Cheylus, A., Reboul, A., Nazir, T., Bussy, Delange, K., Paulignan, Y., Mercier, S., David, A., Marignier, S., Lydie, M., & Freminville, B. (2016). A novel analog reasoning paradigm: new insights in intellectually disabled patients. Plos one, 11(2).
DeTure, M. (2004). Cognitive style and self-efficacy: Predicting student success in online distance education. American Journal of Distance Education,18(1), 21-38.
Diseth, Å. (2011). Self-efficacy, goal orientations and learning strategies as mediators between preceding and subsequent academic achievement. Learning and Individual Differences, 21(2), 191-195.
Duke, N. K., & Pearson, P. D. (2008). Effective practices for developing reading comprehension. The Journal of Education, 189(1/2), 107-122.
Epelboim, J., & Suppes, P. (2001). A model of eye movements and visual working memory during problem solving in geometry. Vision Research, 41(12), 1561-1574.
Ford, N., Miller, D., & Moss, N. (2001). The role of individual differences in Internet searching: an empirical study. Journal of the American Society for Information Science and technology, 52(12), 1049-1066.
Gerhardt, M. W., & Brown, K. G. (2006). Individual differences in self-efficacy development: The effects of goal orientation and affectivity. Learning and Individual Differences, 16(1), 43-59.
Gog, T., & Scheiter, K. (2010). Eye tracking as a tool to study and enhance multimedia learning. Learning and Instruction, 20(2), 95-99.
Graff, M. (2003). Learning from web‐based instructional systems and cognitive style. British Journal of Educational Technology, 34(4), 407-418.
Graham, A. (2015). Cognitive Styles Impact on Student Self-Efficacy. Scholar Works.
Greiff, S., & Neubert, J. C. (2014). On the relation of complex problem solving, personality, fluid intelligence, and academic achievement. Learning and Individual Differences, 36, 37-48.
GrÉGoire, J., & Van der Linden, M. (1997). Effect of age on forward and backward digit spans. Aging, Neuropsychology, and Cognition, 4(2), 140-149.
Hayes, T. R., Petrov, A. A., & Sederberg, P. B. (2011). A novel method for analyzing sequential eye movements reveals strategic influence on Raven's Advanced Progressive Matrices. Journal of Vision, 11(10), 10-10.
Hoffman, B., & Schraw, G. (2009). The influence of self-efficacy and working memory capacity on problem-solving efficiency. Learning and Individual Differences, 19(1), 91-100.
Hoffman, B. (2010). “I think I can, but I'm afraid to try”: The role of self-efficacy beliefs and mathematics anxiety in mathematics problem-solving efficiency.Learning and Individual Differences, 20(3), 276-283.
Höffler, T. N., Prechtl, H., & Nerdel, C. (2010). The influence of visual cognitive style when learning from instructional animations and static pictures. Learning and Individual Differences, 20, 479-483.
Horton, W. (1994). The Icon Book: Visual Symbols for Computer Systems and Documentation. John Wiley & Sons, Inc., NY
Hoshi, Y., Oda, I., Wada, Y., Ito, Y., Yamashita, Y., Oda, M., Ohta, K., Yamada, Y., & Tamura, M. (2000). Visuospatial imagery is a fruitful strategy for the digit span backward task: a study with near-infrared optical tomography. Cognitive Brain Research,9(3), 339-342.
Huettig, F., & Janse, E. (2016). Individual differences in working memory and processing speed predict anticipatory spoken language processing in the visual world. Language, Cognition and Neuroscience, 31(1), 80-93.
Hyönä, J. (2010). The use of eye movements in the study of multimedia learning. Learning and Instruction, 20(2), 172-176.
Ivanova, M. V., & Hallowell, B. (2014). A new modified listening span task to enhance validity of working memory assessment for people with and without aphasia. Journal of communication disorders, 52, 78-98.
Jarodzka, H., Scheiter, K., Gerjets, P., & Van Gog, T. (2010). In the eyes of the beholder: How experts and novices interpret dynamic stimuli. Learning and Instruction, 20(2), 146-154.
Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences, 105(19), 6829-6833.
Jbara, A., & Feitelson, D. G. (2015). How programmers read regular code: a controlled experiment using eye tracking. In Proceedings of the 2015 IEEE 23rd International Conference on Program Comprehension (pp. 244-254). IEEE Press.
Jian, Y. C., & Wu, C. J. (2015). Using eye tracking to investigate semantic and spatial representations of scientific diagrams during text-diagram integration. Journal of Science Education and Technology, 24(1), 43-55.
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, 62, 149-158.
Just, M. A., & Carpenter, P. A. (1980). A theory of reading: From eye fixations to comprehension. Psychological review, 87(4), 329.
Kabugo, D., Muyinda, P. B., Masagazi, F. M., Muwagga, A., & Mugagga, M. B. M. (2015). Tracking students’ eye movements when reading educational information on mobile phones: A case of a Luganda literary text. Rethinking Teaching and Learning in the 21st Century.
Khodadady, E., & Tafaghodi, A. (2013). Cognitive styles and fluid intelligence: Are they related? Journal of Studies in Social Sciences, 3(2).
Kim, S., Lombardino, L. J., Cowles, W., & Altmann, L. J. (2014). Investigating graph comprehension in students with dyslexia: An eye tracking study.Research in developmental disabilities, 35(7), 1609-1622.
Kollöffel, B. (2012). Exploring the relation between visualizer–verbalizer cognitive styles and performance with visual or verbal learning material. Computers & Education, 58(2), 697-706.
Komarraju, M., & Nadler, D. (2013). Self-efficacy and academic achievement: Why do implicit beliefs, goals, and effort regulation matter?. Learning and Individual Differences, 25, 67-72.
Körner, C. (2011). Eye movements reveal distinct search and reasoning processes in comprehension of complex graphs. Applied Cognitive Psychology, 25(6), 893-905.
Kunda, M., McGreggor, K., & Goel, A. K. (2013). A computational model for solving problems from the Raven’s Progressive Matrices intelligence test using iconic visual representations. Cognitive Systems Research, 22, 47-66.
Licero, J. (2012). Verbalizer vs. visualizer viewing text and image: An eye tracking study.
Leech, N. L., & Onwuegbuzie, A. J. (2002). A call for greater use of nonparametric statistics.
Loesche, P., Wiley, J., & Hasselhorn, M. (2015). How knowing the rules affects solving the Raven advanced progressive matrices test. Intelligence, 48, 58-75.
Massa, L. J., & Mayer, R. E. (2006). Testing the ATI hypothesis: Should multimedia instruction accommodate verbalizer-visualizer cognitive style?. Learning and Individual Differences, 16(4), 321-335.
Mason, L., Tornatora, M. C., & Pluchino, P. (2013). Do fourth graders integrate text and picture in processing and learning from an illustrated science text? Evidence from eye-movement patterns. Computers & Education, 60(1), 95-109.
Majooni, A., Masood, M., & Akhavan, A. (2015). Scientific visualizations based on integrated model of text and picture comprehension via eye-tracking. Procedia-Social and Behavioral Sciences, 176, 52-59.
Mayer, R. E., & Massa, L. J. (2003). Three facets of visual and verbal learners: cognitive ability, cognitive style, and learning preference. Journal of educational psychology, 95(4), 833.
McCabe, D. P. (2008). The role of covert retrieval in working memory span tasks: Evidence from delayed recall tests. Journal of Memory and Language,58(2), 480-494.
Mampadi, F., Chen, S. Y., Ghinea, G., & Chen, M. P. (2011). Design of adaptive hypermedia learning systems: A cognitive style approach. Computers & Education, 56(4), 1003-1011.
Parker, P. D., Marsh, H. W., Ciarrochi, J., Marshall, S., & Abduljabbar, A. S. (2014). Juxtaposing math self-efficacy and self-concept as predictors of long-term achievement outcomes. Educational Psychology, 34(1), 29-48.
Peebles, D., & Cheng, P. C. H. (2003). Modeling the effect of task and graphical representation on response latency in a graph reading task. Human Factors: The Journal of the Human Factors and Ergonomics Society, 45(1), 28-46.
Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (Technical Report 91-B-004). The Regents of the University of Michigan.
Rasmussen, D., & Eliasmith, C. (2014). A spiking neural model applied to the study of human performance and cognitive decline on Raven's Advanced Progressive Matrices. Intelligence, 42, 53-82.
Raven, J. (2000). The Raven's progressive matrices: change and stability over culture and time. Cognitive psychology, 41(1), 1-48.
Riding, R., & Cheema, I. (1991). Cognitive styles—an overview and integration. Educational Psychology, 11(3&4), 193-215.
Riding, R. J., & Sadler‐Smith, E. (1997). Cognitive style and learning strategies: Some implications for training design. International Journal of Training and Development, 1(3), 199-208.
Rushton, J. P., Skuy, M., & Fridjhon, P. (2003). Performance on Raven's advanced progressive matrices by African, East Indian, and White engineering students in South Africa. Intelligence, 31(2), 123-137.
Schunk, D. H. (2003). Self-efficacy for reading and writing: influence of modeling, goal setting, and self-evaluation. Reading & Writing Quarterly, 19(2), 159-172.
Schunk, D. H., & Zimmerman, B. J. (2007). Influencing children's self-efficacy and self-regulation of reading and writing through modeling. Reading & Writing Quarterly, 23(1), 7-25.
Shah, P., & Miyake, A. (1999). Models of working memory: An introduction. In A. Miyake & P. Shah (Eds.), Model of working memory: Mechanisms of active maintenance and executive control (pp. 1-27). New York, NY: Cambridge University Press.
Solheim, O. J. (2011). The impact of reading self-efficacy and task value on reading comprehension scores in different item formats. Reading Psychology,32(1), 1-27.
Speece, M. (2012). Learning Style, Culture and Delivery Mode in Online Distance Education. Online Submission.
Sternberg, R. J., & Zhang, L. F. (2001). Perspectives on Thinking, Learning and Cognitive Styles. Mahwah, NJ: Lawrence Erlbaum Associates.
Storbeck, J., Davidson, N. A., Dahl, C. F., Blass, S., & Yung, E. (2015). Emotion, working memory task demands and individual differences predict behavior, cognitive effort and negative affect. Cognition and Emotion, 29(1), 95-117.
Sunday, M. (2014). Determining a correlation between individual differences in eye movements and working memory.
Sun, F., Morita, M., & Stark, L. W. (1985). Comparative patterns of reading eye movement in Chinese and English. Perception & Psychophysics, 37(6), 502-506.
Swanson, H. L.(2015). Intelligence, working memory, and learning disabilities.
Thunholm, P. (2004). Decision-making style: habit, style or both?. Personality and individual differences, 36(4), 931-944.
Unsworth, N., & Engle, R. W. (2007). The nature of individual differences in working memory capacity: Active maintenance in primary memory and controlled search from secondary memory. Psychological Review, 114, 104–132.
Uruchrutu, E., MacKinnon, L., & Rist, R. (2005). User cognitive style and interface design for personal, adaptive learning. what to model? User Modeling, 154-163.
Vakil, E., & Lifshitz-Zehavi, H. (2012). Solving the Raven Progressive Matrices by adults with intellectual disability with/without Down syndrome: Different cognitive patterns as indicated by eye-movements. Research in developmental disabilities, 33(2), 645-654.
Verguts, T., & Boeck, P. D. (2002). The induction of solution rules in Raven's Progressive Matrices Test. European Journal of Cognitive Psychology, 14(4), 521-547.
Vigneau, F., Caissie, A. F., & Bors, D. A. (2006). Eye-movement analysis demonstrates strategic influences on intelligence. Intelligence, 34(3), 261-272.
Vogt, S., & Magnussen, S. (2007). Expertise in pictorial perception: eye-movement patterns and visual memory in artists and laymen. Perception London, 36(1), 91.
Vobkuhler, A., Nordmeier, V., Kuchinke, L., & Jacobs, A. M. (2008). OGAMA (Open Gaze and Mouse Analyzer): open-source software designed to analyze eye and mouse movements in slideshow study designs. Behavior research methods, 40(4), 1150-1162.
Vrugt, A. J., Langereis, M. P., & Hoogstraten, J. (1997). Academic self-efficacy and malleability of relevant capabilities as predictors of exam performance. The Journal of Experimental Education, 66(1), 61-72.
Wieber, F., Odenthal, G., & Gollwitzer, P. (2010). Self-efficacy feelings moderate implementation intention effects. Self and Identity, 9(2), 177-194.
Yang, H. C. (2012). Modeling the relationships between test-taking strategies and test performance on a graph-writing task: Implications for EAP. English for Specific Purposes, 31(3), 174-187.
Yoğurtçu, K. (2013). The impact of self-efficacy perception on reading comprehension on academic achievement. Procedia-Social and Behavioral Sciences, 70, 375-386.
Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational psychology, 25, 82-91.