程式設計已經成為現今社會中的重要技能之一，各國為了培養國家的競爭力，也已經將程式設計的教學納入課綱並且列為必修的課程，而目前對於中小學的學生或其他程式初學者，大部分都是以程式實作的方式進行程式設計基礎能力的培養。為了了解程式設計中，工作記憶與程式設計的策略運用在程式實作上的相關性，本研究以自行開發的遊戲式程式實作平台進行研究，在平台上設計了兩種遊戲視角，並結合視覺化程式設計指令進行程式設計實作，嘗試了解受試者在程式實作的遊戲任務中所運用的策略。任務中，透過程式實作歷程與眼動歷程數據，分析推論在程式設計的實作能力高低成就者，在工作記憶與策略運用的差異。
研究以程式實作任務的平均數分為高低成就兩組，在三個程式實作任務中比較兩組受試者之間在工作記憶與程式策略運用的差異。在工作記憶能力與程式實作能力的關係中，結果顯示視覺空間能力和中央執行功能兩項能力都與程式實作的能力較有關聯；另外，在眼動指標的統計分析與眼動的序列分析中也得知，高成就組使用由上而下的問題解決角度的比例較高，而低成就組在由下而上的問題解決角度的比例較高。高成就的學生不但擁有較優異的視覺空間與中央執行的能力外，在程式設計的策略中傾向於使用由上而下的策略進行問題解決，而低成就者學生則視情況會需要有其他功能的輔助，幫助理解並解決程式設計問題，所以沒有展現一致的策略。

Programming has become an essential skill in the modern society. To improve national competitiveness, many countries have regarded programming as a compulsory course in their curriculum guidelines. Most of the programming courses for primary and secondary school students or beginners emphasize programming implementation to cultivate students’ basic skills. To understand the relationship of working memory and strategies with programming implementation, this research developed a game-based programming implementation system, whose purpose was to investigate how students apply their programming strategies through visual programming blocks with two types of perspective areas. Based on the programming behavior logs and eye tracking process, students’ data about the differences between the high and low achievers could be collected and further discussed.
According to the average scores of programming implementation task, students were divided into two groups: the high achievers and the low achievers. In the relationship of working memory capacity with programming implementation ability, the result indicated that visual space ability and central executive controls have significant difference with implementation ability. In addition, based on the result of sequence analysis of eye tracking, it was found that the high achievers tended to apply top-down strategy while the low achievers preferred to apply bottom-up strategy. The high achievers are not only with better visual space ability and central executive controls, but tend to use top-down strategy in the problem solving process. However, the low achievers seemed to lack of consistent strategies.

一、中文部分
國家教育研究院（2016）。新課綱「程式設計」，學邏輯解問題。取自http://epaper.naer.edu.tw/index.php?edm_no=134&content_no=2672
蔡進雄. (2019). 未來教育新趨勢—各國程式設計教育的動態. 國家教育研究院電子報第 181 期.

二、英文部分
Abrahao, S., Gravino, C., Insfran, E., Scanniello, G., & Tortora, G. (2012). Assessing the effectiveness of sequence diagrams in the comprehension of functional requirements: Results from a family of five experiments. IEEE Transactions on Software Engineering, 39(3), 327-342.
Alloway, T. P., Gathercole, S. E., Willis, C., & Adams, A. M. (2004). A structural analysis of working memory and related cognitive skills in young children. Journal of experimental child psychology, 87(2), 85-106.
Alloway, T. P. (2009). Working memory, but not IQ, predicts subsequent learning in children with learning difficulties. European Journal of Psychological Assessment, 25(2), 92–98. https://doi.org/10.1027/1015-5759.25.2.92
Almstrum, V. L. (1999). The Propositional Logic Test as a Diagnostic Tool for Misconceptions About Logical Operations. Journal of Computers in Mathematics and Science Teaching, 18(3), 205–224. Retrieved from http://www.editlib.org/p/18884
Anderson, M., Kirsner, K., Macleod, C., Maybery, M., O'brien-Malone, A., & Speelman, C. (1998). Implicit and Explicit Mental Processes.
Atkinson, R. C., &Shiffrin, R. M. (1968). Human Memory: A proposed system and its control processes BT - The Psychology of Learning and Motivation. The Psychology of Learning and Motivation, 2(5), 89–195. https://doi.org/10.1111/j.2007.0030-1299.15674.x
Baddeley, A. D., & Hitch, G. (1974). Working memory. In Psychology of learning and motivation (Vol. 8, pp. 47-89). Academic press.
Baddeley, A. (2000). The episodic buffer in working memory evidence. Applied Mathematical Modelling, 40(13–14), 6267–6279. https://doi.org/10.1016/j.apm.2016.02.027
Baddeley, A. D., & Andrade, J. (2000). Working memory and the vividness of imagery. Journal of experimental psychology: general, 129(1), 126.
Baddeley, A. (2001). The concept of episodic memory. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 356(1413), 1345-1350.
Baddeley, A. D. (1992). Working Memory Components of Working Memory Individual Differences in Working Memory The Slave Systems of Working Memory. Science, 255(ii), 556–559. https://doi.org/10.4249/scholarpedia.3015
Baddeley, A., Chincotta, D., & Adlam, A. (2001). Working memory and the control of action: Evidence from task switching. Journal of experimental psychology: General, 130(4), 641.
Bell, D., Koulouri, T., Lauria, S., Macredie, R. D., &Sutton, J. (2014). Microblogging as a mechanism for human-robot interaction. Knowledge-Based Systems, 69(1), 64–77. https://doi.org/10.1016/j.knosys.2014.05.009
Bednarik, R., & Tukiainen, M. (2006, March). An eye-tracking methodology for characterizing program comprehension processes. In Proceedings of the 2006 symposium on Eye tracking research & applications (pp. 125-132). ACM.
Bergersen, G. R., & Gustafsson, J. E. (2011). Programming skill, knowledge, and working memory among professional software developers from an investment theory perspective. Journal of individual Differences.
Brooks, R. (1983). Towards a theory of the comprehension of computer programs. International Journal of Man-Machine Studies, 18(6), 543–554. https://doi.org/10.1016/S0020-7373(83)80031-5
Brooks, R. E. (1978). Using a behavioral theory of program comprehension in software engineering. ICSE 1978: Proceedings of the 3rd International Conference on Software Engineering, 196–201. Retrieved from http://portal.acm.org/citation.cfm?id=800099.803210
Brown, S. C., & Craik, F. I. M.(2000). Encoding and retrieval of information. The oxford handbook of memory, 93-107.
Busjahn, T., Shchekotova, G., Antropova, M., Schulte, C., Sharif, B., Simon, …Ihantola, P. (2014). Eye tracking in computing education. Proceedings of the Tenth Annual Conference on International Computing Education Research - ICER ’14, 3–10. https://doi.org/10.1145/2632320.2632344
Carlisle, M. C., Wilson, T. A., Humphries, J. W., &Hadfield, S. M. (2005). Raptor. Proceedings of the 36th SIGCSE Technical Symposium on Computer Science Education - SIGCSE ’05, 176. https://doi.org/10.1145/1047344.1047411
Chalmers, K. A., &Freeman, E. E. (2018a). A Comparison of Single and Multi-Test Working Memory Assessments in Predicting Academic Achievement in Children. Journal of Psychology: Interdisciplinary and Applied, 152(8), 613–629. https://doi.org/10.1080/00223980.2018.1491469
Chalmers, K. A., &Freeman, E. E. (2018b). Does accuracy and confidence in working memory performance relate to academic achievement in NAPLAN, the Australian national curriculum assessment? Australian Journal of Psychology, 70(4), 388–395. https://doi.org/10.1111/ajpy.12207
Chao, P. Y. (2016). Exploring students’ computational practice, design and performance of problem-solving through a visual programming environment. Computers and Education, 95, 202–215. https://doi.org/10.1016/j.compedu.2016.01.010
Conway, A. R. A., Cowan, N., BUNTING, M. F., &Therriault, D. J. (2002). A latent variable analysis of working memory capacity, short-term memory capacity, processing speed, …. Intelligence, 30, 163–183. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S0160289601000964%5Cnpapers://fd97edd0-de1d-401e-87f3-202830b99eeb/Paper/p784
Crosby, M. E., Scholtz, J., & Wiedenbeck, S. (2002, June). The Roles Beacons Play in Comprehension for Novice and Expert Programmers. In PPIG (p. 5).
Dalmaijer, E. S., Mathôt, S., &Van derStigchel, S. (2014). PyGaze: an open-source, cross-platform toolbox for minimal-effort programming of eyetracking experiments. Behavior Research Methods, 46(4), 913–921. https://doi.org/10.3758/s13428-013-0422-2DeLine, R., Khella, A., Czerwinski, M., &Robertson, G. (2005). Towards understanding programs through wear-based filtering, 1(212), 183. https://doi.org/10.1145/1056018.1056044
Ebrahimi, A. (1994). Novice programmer errors: Language constructs and plan composition. International Journal of Human - Computer Studies. https://doi.org/10.1006/ijhc.1994.1069
Ericsson, K. A., &Lehmann, A. C. (1996). EXPERT AND EXCEPTIONAL PERFORMANCE : Evidence of Maximal Adaptation to Task Constraints, 273–305.
Fitzgerald, S., Lewandowski, G., McCauley, R., Murphy, L., Simon, B., Thomas, L., &Zander, C. (2008). Debugging: finding, fixing and flailing, a multi-institutional study of novice debuggers. Computer Science Education, 18(2), 93–116. https://doi.org/10.1080/08993400802114508
Gilhooly, K., Logie, R. H., Gilhooly, K. J., &Wynn, V. (1994). Counting on working memory in arithmetic problem solving Counting on working memory in arithmetic problem solving, 22(AUGUST 1994), 395–410. https://doi.org/10.3758/BF03200866
Gul, S., Asif, M., Ahmad, W., &Ahmad, U. (2018). Teaching programming: A mind map based methodology to improve learning outcomes. In 2017 International Conference on Information and Communication Technologies, ICICT 2017 (Vol. 2017-December, pp. 209–213). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICICT.2017.8320192
Hansen, M., &Lumsdaine, A. (2013). What Makes Code Hard to Understand ? arXiv : 1304 . 5257v2 [ cs . SE ] 26 Apr 2013, (Cc), 1–19.
Heaton, R. K., &Staff, P. (1993). Wisconsin Card Sorting Test TM : Computer Version 4 Client Information Caucasian (not of Hispanic Origin) Caveats, 04, 1–4. Retrieved from www.parinc.com
Herman, G. L., Loui, M. C., Kaczmarczyk, L., &Zilles, C. (2012). Describing the What and Why of Students’ Difficulties in Boolean Logic. ACM Transactions on Computing Education, 12(1), 1–28. https://doi.org/10.1145/2133797.2133800
Hitch, G. J., &McLean, J. F. (1999). Working memory impairments in children with specific arithmetic learning difficulties. Journal of Experimental Child Psychology, 74(3), 240–260. https://doi.org/10.1006/jecp.1999.2516
Holding, D. H. (1992). Theories of chess skill. Psychological Research, 54(1), 10-16.
Kane, M. J., Brown, L. H., McVay, J. C., Silvia, P. J., Myin-Germeys, I., &Kwapil, T. R. (2007). For Whom the Mind Wanders, and When. Psychological Science, 18(7), 614–621. https://doi.org/10.1111/j.1467-9280.2007.01948.x
Kersten, M., &Murphy, G. C. (2007). Using task context to improve programmer productivity, 1. https://doi.org/10.1145/1181775.1181777
Ko, A. J., Myers, B. A., Coblenz, M. J., & Aung, H. H. (2006). An exploratory study of how developers seek, relate, and collect relevant information during software maintenance tasks. IEEE Transactions on software engineering, (12), 971-987.
Lee, T., Nam, J., Han, D., Kim, S., &In, H. P. (2011). Micro interaction metrics for defect prediction, 311. https://doi.org/10.1145/2025113.2025156
Mayer, R. E. (1989). Models for Understanding. Review of Educational Research, 59(1), 43. https://doi.org/10.2307/1170446
Milton, J., Solodkin, A., Hluštík, P., & Small, S. L. (2007). The mind of expert motor performance is cool and focused. Neuroimage, 35(2), 804-813.
Mohamad Gobil, A. R., Shukor, Z., &Mohtar, I. A. (2009). Novice difficulties in selection structure. Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009, 2(August), 351–356. https://doi.org/10.1109/ICEEI.2009.5254715
Müller, S. C., &Fritz, T. (2013). Stakeholders’ information needs for artifacts and their dependencies in a real world context. IEEE International Conference on Software Maintenance, ICSM, 290–299. https://doi.org/10.1109/ICSM.2013.40
Obaidellah, U., AlHaek, M., &Cheng, P. C.-H. (2018). A Survey on the Usage of Eye-Tracking in Computer Programming. ACM Computing Surveys, 51(1), 1–58. https://doi.org/10.1145/3145904
Pirolli, P., & Card, S. (1999). Information foraging. Psychological review, 106(4), 643.
Pea, R. D., &Kurland, D. M. (1984). ON THE COGNITIVE EFFECTS OF LEARNING There are revolutionary changes afoot in education , in its contents as well as its methods . Widespread computer access by schools is at the heart of these changes . Throughout the world , but particularly in the U . New Ideas in Psychology, 2(2), 137–168. Retrieved from https://ac.els-cdn.com/0732118X84900187/1-s2.0-0732118X84900187-main.pdf?_tid=8e3374a8-3a0a-40ac-8345-24c550955ea7&acdnat=1539751022_52d9990953445344d8930e0e2de5f9e8
Pennington, N. (1987). Stimulus structures and mental representations in expert comprehension of computer programs. Cognitive Psychology, 19(3), 295–341. https://doi.org/10.1016/0010-0285(87)90007-7
Qian, Y., Lehman, J., Qian, Y., &Lehman, J. (2017). Students’ Misconceptions and Other Difficulties in Introductory Pro-gramming: A Literature Review. ACM Transactions on Computing Education, 18(1), 1–24. https://doi.org/10.1145/3077618
Reitan, R. M., &Wolfson, D. (1995). Category Test and Trail Making Test as Measures of Frontal Lobe Functions. The Clinical Neuropsychologist, 9(1), 50–56. https://doi.org/10.1080/13854049508402057
Rich C (1987) Inspection methods in programming: Cliches and plans. A.I. Memo 1005, MIT Artificial ´ Intelligence Laboratory
Robins, A., Rountree, J., &Rountree, N. (2003). Learning and Teaching Programming: A Review and Discussion. Computer Science Education, 13(2), 137–172. https://doi.org/10.1076/csed.13.2.137.14200
Shneiderman, B. (1976). Exploratory experiments in programmer behavior. International Journal of Computer & Information Sciences, 5(2), 123–143. https://doi.org/10.1007/BF00975629
Shneiderman, B., &Mayer, R. (1979). Syntactic/semantic interactions in programmer behavior: A model and experimental results. International Journal of Computer & Information Sciences, 8(3), 219–238. https://doi.org/10.1007/BF00977789
Shute, V. J. (1991). Who is Likely to Acquire Programming Skills? Journal of Educational Computing Research, 7(1), 1–24. https://doi.org/10.2190/vqjd-t1yd-5wvb-rypj
Shaft, T. M., & Vessey, I. (1995). The relevance of application domain knowledge: The case of computer program comprehension. Information systems research, 6(3), 286-299.
Shanteau, J., & Stewart, T. R. (1992). Why study expert decision making? Some historical perspectives and comments. Organizational Behavior and Human Decision Processes, 53, 95-95.
Sharif, B., &Maletic, J. I. (2010). An eye tracking study on camelcase and under-score identifier styles. IEEE International Conference on Program Comprehension, 196–205. https://doi.org/10.1109/ICPC.2010.41
Soloway, E., &Ehrlich, K. (1984). Empirical Studies of Programming Knowledge. IEEE Transactions on Software Engineering, SE-10(5), 595–609. https://doi.org/10.1109/TSE.1984.5010283
Sorva, J., Karavirta, V., &Korhonen, A. (2007). Roles of Variables in Teaching. Proceedings of the 2007 InSITE Conference, 6. https://doi.org/10.28945/3100
Spohrer, J. C., &Soloway, E. (1986). Novice mistakes: are the folk wisdoms correct? Communications of the ACM, 29(7), 624–632. https://doi.org/10.1145/6138.6145
Stone, J. M., &Towse, J. N. (2015). Cog task_working memory, (1989). https://doi.org/10.5334/jors.br
Storey, M. A. (2006). Theories, tools and research methods in program comprehension: Past, present and future. Software Quality Journal, 14(3), 187–208. https://doi.org/10.1007/s11219-006-9216-4
Turner, M. L., &Engle, R. W. (1989). Is working memory capacity task dependent? Journal of Memory and Language, 28(2), 127–154. https://doi.org/10.1016/0749-596X(89)90040-5
Shaft, T. M., & Vessey, I. (1995). The relevance of application domain knowledge: The case of computer program comprehension. Information systems research, 6(3), 286-299.
Waugh, N. C., & Norman, D. A. (1965). Primary memory. Psychological review, 72(2), 89.
Winslow, L. E. (1996). Programming pedagogy---a psychological overview. ACM SIGCSE Bulletin, 28(3), 17–22. https://doi.org/10.1145/234867.234872