本研究以物理學為範疇，篩選具鑑別度之學科能力測驗試題，根據Bloom認知分類為記憶、理解、應用、分析等四層次問題，透過眼動，呈現選擇題型標準化測驗解題歷程，探討其與解題表現之間的關係。本研究將30名大專生以上受測者依四種分組方式比較組間差異，分析各組於各層次問題的眼動資料與解題表現。
研究結果發現(1)透過試題分析與視覺注意力分配了解，物理解題中認知層次越高的題目，難度越高，受測者所花費的時間也越多。(2)依測驗成就分組發現在分析層次中，高分組較低分組在問題區有更多回視，及較多在問題與說明、問題與圖片間之交互閱讀次數，推論兩組之間的差異與分析層次之題目理解及問題與圖片區域間資訊整合的能力有關；(3)在學科背景與測驗成就之交互分組中，理工低分組較非理工低分組有較多的眼動模式差異 。(4)在解題成功與不成功分組中，可以發現在低層次（記憶、理解、應用），成功組較不成功組花費較少閱讀時間，推論這些層次中成功組已達自動化程序，能夠較快搜尋解題關鍵資訊以解題。特別是分析層次題目，成功組較不成功組在正解區之眼動指標數據多達顯著差異，而在解題關鍵區也是成功組高於不成功組，可推測Bloom認知層次分類之分析層次的確與其他三層次題目不同，分析層次之題目多為物理課程中相關但不相似的題目，所以在解題成功組來看，的確有較佳的閱讀理解能力。
研究結果可作為標準化測驗試題設計參考，並提示教師進行學科知能教學時，應著重於基本知識理解與應用。

The main goal of this study was to investigate how students’ visual attention distribution during problem-solving for physics standardized test. A total of 30 undergraduate students voluntarily participated in the study. They were given 12 questions which includes four cognitive levels classify by Bloom taxonomies about kinematics while their eye movements while doing the test were recorded by FaceLAB eye-tracking system.
The result first showed that when solving high-level questions, student tended to spend more time on those questions. Second, when solving analysis level questions, higher-level students of test achievement grouping had more regressions and inter-scanning count. It showed that analysis-level understanding is related to the ability to integrate information between picture areas and questions.
In the interactive grouping of subject background and test achievement, the lower polytechnic group has more differences in eye movement patterns than the non-technical low group. Furthermore, in the group of successful and unsuccessful problem solving, it could be found that at the lower levels (memory, understanding, and application), the successful group spends less time reading than the unsuccessful group. It is inferred that the successful group in these levels has reached an automated program and can search for solutions faster. In particular, in the analysis problems, the eye movement in the correct answer area with the successful group is significantly different from that of the unsuccessful group, and the successful group is higher than the unsuccessful group in the key area. The other three levels of questions are different from this level. Most of the questions at the analysis level are related but not similar topics in the physics curriculum. Therefore, in the successful problem-solving group, they do have better reading comprehension skills.
The research results can be used as a reference for the design of standardized test questions, and it is suggested that teachers should focus on the understanding and application of basic knowledge when teaching subject knowledge.

陳章正、江新合 (2006) 。高中學生物理解題歷程之研究。中華民國第二十二屆科學教育學術研討會。
Anderson, J.R. (2009). Cognitive Psychology and Its Implications, Worth Publishers, New York.
Bransford, J., & Stein, B. S. (1984). The IDEAL problem solver: A guide for improving thinking, learning, and creativity. New York: W. H. Freeman
Chi, M. T., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121-152.
Chi, M. T., Glaser; R., & Farr, M. J. (1988). The nature of expertise. Hillsdale, NJ: Lawrence Erlbaum Associates. 
Dewey, J. (1910). How we think. Chicago: Henry Regnery.
Ebel, R. L. (1979). Essentials of educational measurement. Englewood cliffs, NJ: Prentice-Hall.
Frenck-Mestre, C. (2005). Eye-movement recording as a tool for studying syntactic processing in a second language: a review of methodologies and experimental findings. Second Language Research, 21(2), 175–198. doi: 10.1191/0267658305sr257oa
Gok, T. (2014). An investigation of students’ performance after peer instruction with stepwise problem-solving strateges. International Journal Of Science And Mathematics Education, 13(3), 561-582. doi: 10.1007/s10763-014-9546-9
Groen, M., & Noyes, J. (2010) Solving problems: How can guidance concerning task-relevancy be provided? Computers in Human Behavior, 26(6):1318-1326.
Hegarty, M., Mayer, R., & Monk, C. (1995). Comprehension of arithmetic word problems: A comparison of successful and unsuccessful problem solvers. Journal of Educational Psychology, 87(1), 18-32.

Heller, P., R. Keith, and S. Anderson, Teaching problem solving through cooperative grouping. Part
 1: Group versus individual problem solving. American Journal of Physics, 1992b. 60(7): p. 627- 636.
Hestenes, D. (1987). Toward a modeling theory of physics instruction. American Journal of Physics, 55 (5), 440-454.
James W. (1890). The principles of psychology. New York: Henry Holt.
Just, M. A., & Carpenter, P. (1980). A theory of reading: From eye fixation to comprehension. Psychological Review, 87, 329-354.
Kohl, P., & Finkelstein, N. (2008). Patterns of multiple representation use by experts and novices during physics problem solving. Physical Review Special Topics - Physics Education Research, 4(1). 010111-1~13 doi: 10.1103/physrevstper.4.010111
Kozhevnikov, M., Motes, M., & Hegarty, M. (2007). Spatial visualization in physics problem solving. Cognitive Science, 31(4), 549-579. doi: 10.1080/15326900701399897
Krathwohl, D. R. (2002). A revision of bloom's taxonomy: An overview. Theory into Practice, 41 (4), 212-218.
Lai, M. L., Tsai M. J., Yang F. Y., Hsu, C. Y., Liu, T. C., Lee, S. W. Y., Lee, M. H., Chiou, G. L., Liang, J. C., & Tsai, C. C. (2013). A review of using eye-tracking techonology in exploring learning from 2000 to 2012. Educational Research Review, 10, 90-115.
Larkin, J., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335-1342.
Liversedge, S. P., & Findlay, J. M. (2000). Saccadic eye movements and cognition. Trends in Cognitive Science, 4(1), 90-115.
Mayer, R. E. (1992). Thinking, problem solving, cognition. New York: W. H. Freeman and Company.
Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.
Rayner, K. (1998). Eyemovements and in reading and information processing: 20 years of research. Psychological Bulletins, 124(3), 372-422.
Rayner, K. (2009). Eyemovements and attention in reading, scene perception, and visual search. The Quarterly Journal of Experimental Psychology. 62(8), 1457-1506. Doi:10.1080/17470210902816461
Reif, F., Larkin, J., & Brackett, G. (1976). Teaching general learning and problem‐solving skills. American Journal of Physics, 44(3), 212-217. doi: 10.1119/1.10458
Tai, R. H., Loehr, J. F., & Brigham, F. J. (2006). An exploration of the use of eye-gaze tracking to study problem-solving on standardized science assessment. International Journal of Research & Method in Education, 29(2), 185-208.

Tsai, M. J., Hou, H. T., Lai, M. L., Liu, W. Y., & Yang, F. Y. (2012). Visual attention for solving multiple-choice science problem: An eye-tracking analysis. Computers & Education, 58, 375-385. 
Teodorescu, R. E., Bennhold, C., Feldman, G., & Medsker, L. (2013). New approach to analyzing physics problems: a taxonomy of instroductory physics problems. Physical Review Special Topics- Physics Education Research, 9. 010103.

van Gompel, R. P. G., Fischer, M. H., Murray, W. S., & Hill, R. L. (2007). Eye-movement research: An overview of current and past developments. In R. P. G. van Gompel, M. H. Fischer, W. S.
Murray, & R. L. Hill (Eds.), Eye movements: A window on mind and brain (pp. 1−28). Oxford: Elsevier.
Yarbus, A. (1967). Eye movements and vision. New York: Plenum Press.