隨著大學多元入學方案的實施，普通高中畢業生可以透過學測成績申請進入技職大學就讀。技職大學也逐年增加對普通高中畢業生的招生名額。在這樣的環境下，普通高中畢業生沒有專業科目的先備知識，進入技職大學後容易對專業科目感到恐懼。技職大學的教師更面臨學生先備知識不一致，準備課程的困難度增加，尤其以專業科目課程更為明顯。而程式設計課程相較於其他專業科目，其學習門檻更高，容易使沒有先備知識的學生感到困難並降低其學習意願。本研究的目的在於針對技職大學內的程式設計課程，透過遊戲式學習(Game-based Learning)進行差異化教學(Differentiated Instruction)，探討不同先備知識學生使用程式教學遊戲的行為意圖，並運用科技接受模型(Technology Acceptance Model, TAM)探究其差異因素。

With universities' implementation of multi-entrance program, ordinary high school graduates can apply for admission to technical and vocational colleges through general scholastic ability test. The University of Science and Technology has also increased the enrollment quota for ordinary high school graduates every year. In such an environment, these school graduates do not have the professional knowledge of the subjects. After entering these vocational and technical colleges do they start feeling frightened of these professional subjects.Teachers in technical and vocational colleges also face increasing difficulties in preparing courses for students with different prior knowledge, especially in professional subjects. Compared to other professional subjects, the programming course has a higher learning threshold, which makes it difficult for students who do not have prior knowledge to move on, thus reducing their willingness to learn.The purpose of this study is to research the different programming courses available at the University of Science and Technology and explore the behavioral intention of students, who use different types of game-based learning for differentiated instructions.And use technology acceptance model to inquire into the differentiation factors.

Alavi, M., & Leidner, D. E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS quarterly, 107-136.

Anderson, G. J. (1970). Effects of classroom social climate on individual learning. American educational research journal, 135-152.

Arbaugh, J. B. (2000). Virtual classroom characteristics and student satisfaction with internet-based MBA courses. Journal of management education, 24(1), 32-54.

Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS quarterly, 351-370.

Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS quarterly, 351-370.

Brusilovsky, P., & Spring, M. (2004). Adaptive, engaging, and explanatory visualization in a C programming course. In Proceedings of World Conference on Educational Multimedia,
Hypermedia and Telecommunications (pp. 1264-1271).

Chen, H. R., Hwang, J. P., Wu, T. T., Huang, Y. M., & Hsueh, H. T. (2011, July). Assessment of implementing a digital game-based learning system over Facebook. In Advanced Learning Technologies (ICALT), 2011 11th IEEE International Conference on (pp. 621-622). IEEE.

Chen, S. Y., & Macredie, R. D. (2004). Cognitive modeling of student learning in web-based instructional programs. International Journal of Human-Computer Interaction, 17(3), 375-402.

Chicharro, M., García, J., & Ramírez, R. (2008). Jupiter: A web environment for learning based on competitive games. Retrieved Dec. 12, 2016, from http://www.mundointernet.es/IMG/pdf/ponencia154.pdf

Cunningham, H. (1994). Gender and computer games. Media Education Journal, 17(Winter), 13-15.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003.

Du Boulay, B. (1986). Some difficulties of learning to program. Journal of Educational Computing Research, 2(1), 57-73.
Facer, K. (2003). Screenplay: Children and computing in the home. Psychology Press.

Fagin, B. S., Merkle, L. D., & Eggers, T. W. (2001, September). Teaching computer science with robotics using Ada/Mindstorms 2.0. In ACM SIGAda Ada Letters (Vol. 21, No. 4, pp. 73-78). ACM.

Felleisen, M., Findler, R. B., Flatt, M., & Krishnamurthi, S. (2004). The TeachScheme! project: Computing and programming for every student. Computer Science Education, 14(1), 55-77.

Fletcher, G. H., & Lu, J. J. (2009). Education human computing skills: rethinking the K-12 experience. Communications of the ACM, 52(2), 23-25.

Guadagnoli, M. A., & Lee, T. D. (2004). Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning. Journal of motor behavior, 36(2), 212-224.

Guzdial, M. (2008). Education paving the way for computational thinking. Communications of the ACM, 51(8), 25-27.

Hall, T. (2002). Differentiated instruction. Wakefield, MA: National
Center on Accessing the General Curriculum. Retrieved Dec. 12, 2016, from http://www.cast.org/publications/ncac/ncac_diffinstruc.html

Hogle, J. G. (1996). Considering games as cognitive tools: In search of effective “edutainment”. University of Georgia: Department of Instructional Technology, ERIC Document Reproduction Service No. ED 425737.

Huang, Y. M. (2016). The factors that predispose students to continuously use cloud services: Social and technological perspectives. Computers & Education, 97, 86-96.

Huang, S. H., Wu, T. T., & Huang, Y. M. (2013, August). Learning diagnosis instruction system based on game-based learning for mathematical course. In Advanced Applied Informatics (IIAIAAI), 2013 IIAI International Conference on (pp. 161-165). IEEE.

Huang, Y. M., & Huang, Y. M. (2015). A scaffolding strategy to develop handheld sensor-based vocabulary games for improving students’ learning motivation and performance. Educational Technology Research and Development, 63(5), 691-708.

Huang, Y. M., Huang, S. H., & Wu, T. T. (2014). Embedding diagnostic mechanisms in a digital game for learning mathematics. Educational Technology Research and Development, 62(2), 187-207.

Huang, Y. P., & Huang, Y. M. (2013, July). Programming language learning supported by an accredited course strategy. In Advanced Learning Technologies (ICALT), 2013 IEEE 13th International Conference on (pp. 327-329). IEEE.

Hwang, G. J., Yang, L. H., & Wang, S. Y. (2013). A concept map-embedded educational computer game for improving students' learning performance in natural science courses. Computers & Education, 69, 121-130.

Johnson, D. W., Johnson, R., & Anderson, D. (1983). Social interdependence and classroom climate. The Journal of Psychology, 114(1), 135-142.

Kazimoglu, C., Kiernan, M., Bacon, L., & Mackinnon, L. (2012). A serious game for developing computational thinking and learning introductory computer programming. Procedia-Social and Behavioral Sciences, 47, 1991-1999.

Ke, F., & Grabowski, B. (2007). Gameplaying for maths learning: cooperative or not?. British Journal of Educational Technology, 38(2), 249-259.

Li, S. (2002). Rock 'em, sock 'em Robocode! Retrieved Dec. 12, 2016, from http://www.ibm.com/developerworks/java/library/jrobocode/

Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864-873.

Lin, Y. C., & Huang, Y. M. (2013). A Fuzzy-based Prior Knowledge Diagnostic Model with Multiple Attribute Evaluation. Educational Technology & Society, 16(2), 119-136.

Lin, Y. C., Lin, Y. T., & Huang, Y. M. (2011). Development of a diagnostic system using a testing-based approach for strengthening student prior knowledge. Computers & Education, 57(2), 1557-1570.

Malone, T. W. (1980, September). What makes things fun to learn? Heuristics for designing instructional computer games. In Proceedings of the 3rd ACM SIGSMALL symposium and the first SIGPC symposium on Small systems(pp. 162-169). ACM.

Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research, 2(3), 173-191.

Moreno, J. (2012). Digital competition game to improve programming skills. Educational Technology & Society, 15(3), 288-297.

Nussbaum, M. (2007). Games, learning, collaboration and cognitive divide. Retrieved Dec, 12, 2016, from http://www.oecd.org/edu/ceri/39414787.pdf

Papastergiou, M. (2009). Digital game-based learning in high school computer science education: Impact on educational effectiveness and student motivation. Computers & Education, 52(1), 1-12.

Prensky, M. (2003). Digital game-based learning. Computers in Entertainment (CIE), 1(1), 21-21.

Radford, A. (2000). Games and learning about form in architecture. Automation in Construction, 9(4), 379-385.

Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A., Rosenbaum, E., Silver, J., Silverman, B., & Kafai, Y. (2009). Scratch: programming for all. Communications of the ACM, 52(11), 60-67.

Saulter, J. (2007). Introduction to video game design and development. New York: McGraw-Hill.

Schunk, D. H. (2000).Learning theories: An educational perspective (3rd ed.). Englewood Cliffs, NJ: Merrill.

Smith, D. C., Cypher, A., & Spohrer, J. (1994). KidSim: programming agents without a programming language. Communications of the ACM, 37(7), 54-67.

Somersalo, H., Solantaus, T., & Almqvist, F. (2002). Classroom climate and the mental health of primary school children. Nordic journal of psychiatry, 56(4), 285-290.
Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS quarterly, 561-570.

Tomlinson, C. A. (2001). How to differentiate instruction in mixed-ability classrooms. ASCD.

Tomlinson, C. A., & Strickland, C. A. (2005). Differentiation in practice: A resource guide for differentiating curriculum, grades 9-12. ASCD.

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research, 11(4), 342-365.

Yang, B. H., & Asada, H. (1995, May). Progressive learning for robotic assembly: Learning impedance with an excitation scheduling method. In Robotics and Automation, 1995. Proceedings., 1995 IEEE International Conference on (Vol. 3, pp. 2538-2544). IEEE.

Zheng, M., Spires, H. A., & Meluso, A. (2011). Examining upper elementary students' gameplay experience: A flow theory perspective. In A. Mendez-Vilas (Ed.), Education in a technological world: Communicating current and emerging research and technological efforts (pp. 190-198).

Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of educational psychology, 81(3), 329.