本研究開發數位遊戲學習結合推薦系統，以強化二元樹遊戲過程中的運算思維。比較不同性別學生在遊戲式學習系統中結合兩種不同推薦機制，分別為實驗組使用「遊戲式學習推薦系統_特徵值凱利方格版」，以及控制組使用「遊戲式學習推薦系統_一般凱利方格版」，對於學生在二元樹的學習成效、自我效能、數位遊戲式學習量表、心流等量表的表現。研究結果顯示使用任何一個遊戲式學習系統的女生和男生都有顯著提升他們的學習成效、自我效能、遊戲式學習接受度和心流。不同推薦機制的遊戲式學習系統和不同性別在學習成效、自我效能和心流的表現有交互作用。控制組男生學習成效顯著高於控制組女生；實驗組女生學習成效顯著高於控制組女生；控制組男生自我效能顯著高於控制組女生；實驗組女生的心流表現顯著高於控制組女生。並進一步分析不同性別的學生使用不同推薦機制的遊戲式學習之行為模式，結果顯示，實驗組學生在遊戲中會循序漸進探索場景的人物，獲得更多資訊才去回答問題。而控制組學生在尋找學習內容的提示後會直接去回答答案。

The study attempted to explore the effects of integrating digital game-based learning (GBL) system with the recommendation system, so as to enhance the computational thinking in the binary tree game. The experimental group used "GBL recommendation system feature-value version", and the control group adopted "GBL recommendation system Kelly grid version". This study further analyzed female and male students’ academic performance of binary tree, self-efficacy, GBL perceptions and flow experience. The results of this study showed that the females and males in two groups have significantly improved their learning effectiveness, self-efficacy, GBL perceptions and flow experience. There was an interactive effect between different GBL systems and gender on the learning effectiveness and self-efficacy and flow experience. The males in the control group also outperformed the females in the control group. The females in the experimental group outperformed the females in the control group. The self-efficacy of the males in the control group was higher than that of the females in the control group. The flow experience of the females in the experimental group was significantly higher than that of the females in the control group. The behavioral patterns of the students with different gender in different groups were further analyzed. The results showed that the students in the experimental group inquired the objects or roles in the scene sequentially, so as to collect more information and knowledge to solve problem, However, the students in the control group looked for the prompt of the learning content and directly went to answer the question.

Acquah, E. O., & Katz, H. T. (2019). Digital game-based L2 learning outcomes for primary through high-school students: A systematic literature review. Computers & Education, 143, 103667.
Aho, A. V. (2012). Computation and computational thinking. The Computer Journal, 55(7), 832-835.
Al-Nakhal, M. A., & Naser, S. S. A. (2017). Adaptive intelligent tutoring system for learning computer theory.
Al‐Zahrani, A. M. (2015). From passive to active: The impact of the flipped classroom through social learning platforms on higher education students' creative thinking. British Journal of Educational Technology, 46(6), 1133-1148.
Allan, V., Barr, V., Brylow, D., & Hambrusch, S. (2010, March). Computational thinking in high school courses. In Proceedings of the 41st ACM technical symposium on Computer science education (pp. 390-391).
Angeli, C., & Valanides, N. (2020). Developing young children's computational thinking with educational robotics: An interaction effect between gender and scaffolding strategy. Computers in Human Behavior, 105, 105954.
Atmatzidou, S., & Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75, 661-670.
Backlund, P., & Hendrix, M. (2013, September). Educational games-are they worth the effort? A literature survey of the effectiveness of serious games. In 2013 5th international conference on games and virtual worlds for serious applications (VS-GAMES) (pp. 1-8). IEEE.
Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis. Cambridge university press.
Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: what is Involved and what is the role of the computer science education community? Inroads, 2(1), 48-54.
Bau, D., Gray, J., Kelleher, C., Sheldon, J., & Turbak, F. (2017). Learnable programming: blocks and beyond. arXiv preprint arXiv:1705.09413.
Bawa, P., Watson, S. L., & Watson, W. (2018). Motivation is a game: Massively multiplayer online games as agents of motivation in higher education. Computers & Education, 123, 174-194.
Boyle, E. A., Hainey, T., Connolly, T. M., Gray, G., Earp, J., Ott, M., Lim, T., Ninaus, M., Ribeiro, C., & Pereira, J. (2016). An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games. Computers & Education, 94, 178-192.
Bradac, V., & Walek, B. (2017). A comprehensive adaptive system for e-learning of foreign languages. Expert Systems with Applications, 90, 414-426.
Cetin, I., & Dubinsky, E. (2017). Reflective abstraction in computational thinking. The Journal of Mathematical Behavior, 47, 70-80.
Chiazzese, G., Fulantelli, G., Pipitone, V., & Taibi, D. (2017, October). Promoting computational thinking and creativeness in primary school children. In Proceedings of the 5th International Conference on Technological Ecosystems for Enhancing Multiculturality (pp. 1-7).
Chu, H.-C., Hwang, G.-J., & Tsai, C.-C. (2010). A knowledge engineering approach to developing mindtools for context-aware ubiquitous learning. Computers & Education, 54(1), 289-297.
Chu, H. C., Hwang, G. J., Huang, S. X., & Wu, T. T. (2008). A knowledge engineering approach to developing e‐libraries for mobile learning. The Electronic Library.
Città, G., Gentile, M., Allegra, M., Arrigo, M., Conti, D., Ottaviano, S., Reale, F., & Sciortino, M. (2019). The effects of mental rotation on computational thinking. Computers & Education, 141, 103613.
Council, N. R. (2011). Report of a workshop on the pedagogical aspects of computational thinking. National Academies Press.
de Paula, B. H., Burn, A., Noss, R., & Valente, J. A. (2018). Playing Beowulf: Bridging computational thinking, arts and literature through game-making. International journal of child-computer interaction, 16, 39-46.
del Olmo-Muñoz, J., Cózar-Gutiérrez, R., & González-Calero, J. A. (2020). Computational thinking through unplugged activities in early years of Primary Education. Computers & Education, 150, 103832.
Dorji, U., Panjaburee, P., & Srisawasdi, N. (2015). Gender differences in students’ learning achievements and awareness through residence energy saving game-based inquiry playing. Journal of Computers in Education, 2(2), 227-243.
Duggan, M., & Brenner, J. (2013). The demographics of social media users, 2012 (Vol. 14). Pew Research Center's Internet & American Life Project Washington, DC.
Erdt, M., Fernandez, A., & Rensing, C. (2015). Evaluating recommender systems for technology enhanced learning: a quantitative survey. IEEE Transactions on Learning Technologies, 8(4), 326-344.
Ford, N., & Chen, S. Y. (2000). Individual differences, hypermedia navigation, and learning: an empirical study. Journal of Educational Multimedia and Hypermedia, 9(4), 281-311.
García-Peñalvo, F. J., & Mendes, A. J. (2018). Exploring the computational thinking effects in pre-university education.
Gee, E. R., & Tran, K. M. (2016). Video game making and modding. In Handbook of research on the societal impact of digital media (pp. 238-267). IGI Global.
Gretter, S., & Yadav, A. (2016). Computational thinking and media & information literacy: An integrated approach to teaching twenty-first century skills. TechTrends, 60(5), 510-516.
Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38-43.
Hamari, J., Shernoff, D. J., Rowe, E., Coller, B., Asbell-Clarke, J., & Edwards, T. (2016). Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning. Computers in Human Behavior, 54, 170-179.
Homer, B. D., Hayward, E. O., Frye, J., & Plass, J. L. (2012). Gender and player characteristics in video game play of preadolescents. Computers in Human Behavior, 28(5), 1782-1789.
Hsieh, Y.-H., Lin, Y.-C., & Hou, H.-T. (2016). Exploring the role of flow experience, learning performance and potential behavior clusters in elementary students' game-based learning. Interactive Learning Environments, 24(1), 178-193.
Huang, C. (2013). Gender differences in academic self-efficacy: a meta-analysis. European Journal of Psychology of Education, 28(1), 1-35.
Hubwieser, P., Hubwieser, E., & Graswald, D. (2016, October). How to attract the girls: Gender-specific performance and motivation in the Bebras challenge. In International Conference on Informatics in Schools: Situation, Evolution, and Perspectives (pp. 40-52). Springer, Cham.
Hwang, G. J., Sung, H. Y., Hung, C. M., Yang, L. H., & Huang, I. (2013). A knowledge engineering approach to developing educational computer games for improving students' differentiating knowledge. British Journal of Educational Technology, 44(2), 183-196.
Ioannidou, A., Bennett, V., Repenning, A., Koh, K. H., & Basawapatna, A. (2011). Computational Thinking Patterns. Online Submission.
ISTE, I., & CSTA, C. (2011). Operational Definition of Computational Thinking for K–12 Education. National Science Foundation.
Ke, F. (2008). Alternative goal structures for computer game-based learning. International Journal of Computer-Supported Collaborative Learning, 3(4), 429.
Kelly, G. A. (1955). The Psychology of Personal Constructs Vol. 1. New York: W.W Norton.
Khan, A., Ahmad, F. H., & Malik, M. M. (2017). Use of digital game based learning and gamification in secondary school science: The effect on student engagement, learning and gender difference. Education and Information Technologies, 22(6), 2767-2804.
Liao, C.-W., Chen, C.-H., & Shih, S.-J. (2019). The interactivity of video and collaboration for learning achievement, intrinsic motivation, cognitive load, and behavior patterns in a digital game-based learning environment. Computers & Education, 133, 43-55.
Lin, H., & Sun, C. T. (2015). Massively Multiplayer Online Role Playing Games (MMORPG s). The International Encyclopedia of Digital Communication and Society, 1-7.
Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51-61.
Manovich, L. (2013). Software takes command (Vol. 5). A&C Black.
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.
Pearce, J. M., Ainley, M., & Howard, S. (2005). The ebb and flow of online learning. Computers in Human Behavior, 21(5), 745-771.
Peterson, M. (2016). The use of massively multiplayer online role-playing games in CALL: An analysis of research. Computer Assisted Language Learning, 29(7), 1181-1194.
Pintrich, P. R. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ).
Premlatha, K., Dharani, B., & Geetha, T. (2016). Dynamic learner profiling and automatic learner classification for adaptive e-learning environment. Interactive Learning Environments, 24(6), 1054-1075.
Prensky, M. (2003). Digital game-based learning. Computers in Entertainment (CIE), 1(1), 21-21.
Qian, M., & Clark, K. R. (2016). Game-based Learning and 21st century skills: A review of recent research. Computers in Human Behavior, 63, 50-58.
Reinders, H., & Wattana, S. (2015). Affect and willingness to communicate in digital game-based learning. ReCALL, 27(1), 38-57.
Román-González, M., Pérez-González, J.-C., & Jiménez-Fernández, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test. Computers in Human Behavior, 72, 678-691.
Shute, V., & Towle, B. (2003). Adaptive e-learning. Educational psychologist, 38(2), 105-114.
Shute, V. J., & Zapata‐Rivera, D. (2007). Adaptive technologies. ETS Research Report Series, 2007(1), i-34.
Soflano, M., Connolly, T. M., & Hainey, T. (2015). An application of adaptive games-based learning based on learning style to teach SQL. Computers & Education, 86, 192-211.
Tedre, M. (2017). Many paths to computational thinking. In Paper presented at the TACCLE 3 final conference, Brussels, Belgium.
Truong, H. M. (2016). Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities. Computers in Human Behavior, 55, 1185-1193.
Tsai, F.-H. (2017). An investigation of gender differences in a game-based learning environment with different game modes. Eurasia Journal of Mathematics, Science and Technology Education, 13(7), 3209-3226.
Wing, J. (2011). Research notebook: Computational thinking-What and why. The Link Magazine, 6.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717-3725.
Wouters, P., Van Nimwegen, C., Van Oostendorp, H., & Van Der Spek, E. D. (2013). A meta-analysis of the cognitive and motivational effects of serious games. Journal of Educational Psychology, 105(2), 249.
Xie, H., Chu, H.-C., Hwang, G.-J., & Wang, C.-C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, 103599.
Yang, J. C., & Quadir, B. (2018). Individual differences in an English learning achievement system: Gaming flow experience, gender differences and learning motivation. Technology, Pedagogy and Education, 27(3), 351-366.
Zuffianò, A., Alessandri, G., Gerbino, M., Kanacri, B. P. L., Di Giunta, L., Milioni, M., & Caprara, G. V. (2013). Academic achievement: The unique contribution of self-efficacy beliefs in self-regulated learning beyond intelligence, personality traits, and self-esteem. Learning and Individual Differences, 23, 158-162.