박사

정보 영재의 컴퓨팅 사고력 향상을 위한 퍼즐 기반 알고리즘 학습 모형 = Puzzle-Based Algorithm Learning Model for Improving Computational Thinking for Informatics Gifted Students

최정원 2015년
논문상세정보
' 정보 영재의 컴퓨팅 사고력 향상을 위한 퍼즐 기반 알고리즘 학습 모형 = Puzzle-Based Algorithm Learning Model for Improving Computational Thinking for Informatics Gifted Students' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 알고리즘 학습
  • 정보영재
  • 컴퓨팅 사고력
  • 퍼즐 기반 학습
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
454 0

0.0%

' 정보 영재의 컴퓨팅 사고력 향상을 위한 퍼즐 기반 알고리즘 학습 모형 = Puzzle-Based Algorithm Learning Model for Improving Computational Thinking for Informatics Gifted Students' 의 참고문헌

  • 핵심인재관리와 조직성과 : 기업규모 및 종업원 참여제도의 조절효과
    박오원 대한경영학회지, 26(2), 389-409 [2013]
  • 초중등 단계 Computational thinking 도입을 위한 기초연구, BD14060010
  • 초등학교 정보영재를 위한 컴퓨터 알고리즘 교육과정의 개발에 대한 연구
    문교식 대구교육대학교 초등교육연구논총, 23(1), 329-350 [2007]
  • 초등학교 정보영재를 위한 창의성 개발연구
    이길복 전우천 한국정보교육학 회 학술발표논문집, 8(1), 404-412 [2003]
  • 초등 정보영재의 창의성 개발을 위한 컴퓨터 알고리즘 교육에대한 연구
    문교식 대구교육대학교 초등교육연구논총, 24(1), 187-202 [2008]
  • 중학교 선택 교과 교육과정
    교육과학기술부 교육과학기술부 고시 제 2011-361호[별책 18] [2011]
  • 정보교육에서 교수-학습 도구로써의 퍼즐 활용 및 효과성에 관한 연 구. 석사학위논문
    장정아 고려대학교 [2009]
  • 정보과학영재를 위한 교육 분야 정의
    나동섭 이재호 한국정보교육학회 하계 학술대회 발표논문집, 378-379 [2001]
  • 정보과학 영재교육과정 및 교수학습 자료개발
    이재호 한국교육개발원 제9기 영재교육 담당교원 직무 연수[공통정보과학]. 201-222 [2009]
  • 정보 영재를 위한 온라인 학습 도구 평가 준거 개발. 박사학위논문
    김용 고 려대학교 [2008]
  • 쉽게 배우는 알고리즘 - 관계 중심의 사고법
    문병로 서울: 한빛미디어 [2010]
  • 놀이를 통한 알고리즘 개념 학습이 학습 동기 및 학업 성취도에 미치 는 영향. 석사학위논문
    권은정 한국교원대학교 [2008]
  • 교육용 프로그래밍 언어와 정보영재아동의 프로그래밍능력의 상관관계분석 연구
    전우천 한국정보교육학회, 16(3), 353-361 [2012]
  • Wing, J. M. (2006). Computational thinking, Communications of the ACM, 49(3), 33-35.
  • Wenger, E. (1998). Communities of practice: Learning, meaning, and identity.New York: Cambridge University Press.
  • Renzulli,J.S.(2012).ReexaminingtheRoleofGiftedEducationand Talent Development for the 21st Century A Four-Part Theoretical Approach.GiftedChildQuarterly,56(3),150-159.
  • Peter Winkler (2010). Mathematical Puzzles: A Connoisseur's Collection. Massachusetts: A.K. Peters.
  • Perrone, K. M., Tschopp, M. K., Snyder, E. R., Boo, J. N., & Hyatt, C. (2010). A longitudinal examination of career expectations and outcomes of academically talented students 10 and 20 years post-high school graduation. Journal of Career Development, 36(4), 291-309.
  • Perlis, A. (1962). The computer in the university. In M. Greenberger, Ed., Computers and the World of the Future, MIT Press, Cambridge, MA, 180? 219.
  • Partnership for the 21st century skills (2009). P21 Framework Definitions. 24 May 2014, http://www.p21.org/storage/documents/P21_Framework_Definition s.pdf.
  • Parhami, B. (2012). Binary Search - A lecture in CE freshman seminar series: Ten Puzzling problems in computer engineering. 22 Jul 2014, http://www.ece.ucsb.edu/~parhami/pres_folder/f35-frosh-sem-binary-search.p df.
  • Parhami, B. (2009). Puzzling Problems in Computer Engineering. IEEE Computer, 42(3), 26-29.
  • Parhami, B. (2008). Motivating computer engineering freshmen through mathematical and logical puzzles. Education, IEEE Transactions on, 52(3), 360-364.
  • Norman, D. A. (1973). Memory, knowledge, and the answering of questions. In R. l. Solso(Ed.), Contemporary issues in cognitive psychology: The Loyola sumposium(pp.135-165). Washington, DC: Winston.
  • National Research Council. (2009). Report of a Workshop on The Scope and Nature of Computational Thinking. National Academies Press.
  • Mislevy, R. J., Almond, R. G., & Lukas, J. F. (2004). A brief introduction to evidence-centered design(CSE report 632). National Center for Research on Evaluation, Standards, and Student Testing.
  • Michalewicz, Z., Falkner, N., & Sooriamurthi, R. (2011). Puzzle-based learning: An introuction to critical thinking and problem solving. Decision Line, 42(5), 6-9.
  • Michalewicz, Z. & Michalewicz, M. (2010). Puzzle-based learning: An introduction to critical thinking, mathematics, and problem solving. Melbourne: Hybrid Publishers.
  • Merrick, K. E. (2010). An empirical evaluation of puzzle-based learning as an interest approach for teaching introductory computer science. Education, IEEE Transactions on, 53(4), 677-680.
  • Mayer, R. (1998). Cognitive theory for education: What teachers need to know. In N. Lambert & B. McCombs (Eds), How students learn: Reforming schools through learner-centered education (pp.353-378). Washington, DC: American Psychological Association.
  • Maheshwari, A. (1996). Learning and information technology: An experimental investigation of computer-based representation to support reflective thinking. Doctoral Dissertation. Case Western Reserve University.
  • Lin, X., Hmelo, C., Kinzer, C., & Secules, T. (1999). Designing technology to support reflection. Educational Technology Research and Development, 47(3), 43-62.
  • Levitin, A., & Papalaskari, M. A. (2002). Using puzzles in teaching algorithms. ACM SIGCSE Bulletin, 34(1), 292-296.
  • Lave, J., & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. New York: Cambridge University Press.
  • Kuhn, T. S.(1962). The structure of scientific revolutions. Chicago, IL: University of Chicago Press.
  • Hannafin, M., Land, S., & Oliver, K. (1999). Open learning enviromnents: Foundations, methods, and models. In C. Reigeluth(Ed.), Instructional design theories and models. Mahwah, NJ: Lawrence Erlbaum Associates.
  • Guzdial, M. (2008). Education: Paving the way of computational thinking. Communications of the ACM, 51(8), 25-27.
  • Falkner, N., Sooriamurthi, R., & Michalewicz, Z. (2010). Puzzle-based learning for engineering and computer science. IEEE Computer, 43(4), 20-28.
  • Eggen, P. D., & Kauchak, D. P. (2011). Strategies and models for teachers: Teaching content and thinking skills. Boston, MA: Pearson.
  • Denning, P. J. (2009). The profession of IT Beyond computational thinking. Communications of the ACM, 52(6), 28-30.
  • Davis, G. A., Rimm, S. B., & Siegle, D. (2011). Education of the gifted and talented(6th ed.). Boston: Pearson.
  • Dai, D. Y., & Chen, F. (2013). Three paradigms of gifted education: In search of conceptual clarity in research and practice. Gifted child quarterly, 57(3), 151-168.
  • Cuban, L. (1984). How teachers taught. New York: Longman.
  • Corno, L., & Snow, R. (1986). Adapting teaching to individual differences among learners. In M. Wittrock (Ed.), Third handbook of research on teaching (pp.570-604). New York: Macmillan.
  • Computational Thinking 능력 향상을 위한 로봇 프로그래밍 교수학습 모형. 박사학위 논문
    이은경 한국교원대학교 [2009]
  • Collins, A. (2009). Cognitive apprenticeship In Sawyer, R. K.(eds.), The Cambridge handbook of learning science, edited by R. Keith Sawyer. New York: Cambridge University Press.
  • CSTA & ISTE (2011b). Computational Thinking teacher resources second edition. 24 MAY 2014, https://csta.acm.org/Curriculum/sub/CurrFiles/472.11CTTeacher Resources_2ed-SP-vF.pdf.
  • CSTA & ISTE (2011a). Operational definition of computational thinking for K-12 Education. 24 MAY 2014, https://csta.acm.org/Curriculum/sub/CurrFiles/Comp ThinkingFlyer.pdf
  • Burton, R., Brown, J. S., & Fischer, G. (1984). Skiing as a model of instruction. In B. Rogoff and J. Lave(Eds.), Everyday cognition: Its developmental and social context(pp. 139-150). Cambridge, MA: Harvard University Press. Code.org (2014). Computational Thinking. 3 AUGUST 2014, http://learn.code.org/unplugged/unplug2.pdf.
  • Beugelsdijk, S. (2008). Strategic human resource practices and product innovation. Organization Studies, 29(6), 821-847.
  • Bell, T., Witten, I. H., & Fellows, M. (2010). Computer science unplugged. 10 AUGUST 2014, http://csunplugged.org/sites/default/files/download_icons/unplugged TeachersMar2010-USletter.pdf.
  • Barab, S. A., MaKinster, J., & Scheckler, R. (2003). Designing system dualities: Characterizing a web-supported teacher professional development community. Information Society 19(3), 237-256.