본문 바로가기
KERD
논문KCI 등재

생태계 요소 간 상호작용에 대한 블록 기반 컴퓨터 모델링에서 초등학생들의 인식적, 개념적 측면 탐색

Exploring Epistemic and Conceptual Aspects of Elementary Students Through Block-Based Computational Modeling of Interaction among Various Components of an Ecosystem

0

조회

0

다운로드

0

인용

0

참고문헌

키워드

주제분류교육학

초록

Recent studies suggest that computational models help students maintain a productive line of modeling while interacting with them as non-human participants. This qualitative case study explored how elementary school students used an block coding-based computational model to construct the ecosystem model which represents complex interaction among biotic and abiotic elements and the scientific practices they used to create a more sustainable model. For this study, 6th grade students were guided to act like scientists to predict and modify the ecosystem using one of the computational models, Starlogo-nova. This study found that the computational model was able to stimulate students' scientific practice positively. First, the computational model can facilitate students' creation, evaluation, and revision of the ecosystem models. Second, the computational model can help students formulate theories for more sustainable the ecosystem models. Based on the results of the study, this study concluded that the computer model should be used in future science education.

Abstract

Recent studies suggest that computational models help students maintain a productive line of modeling while interacting with them as non-human participants. This qualitative case study explored how elementary school students used an block coding-based computational model to construct the ecosystem model which represents complex interaction among biotic and abiotic elements and the scientific practices they used to create a more sustainable model. For this study, 6th grade students were guided to act like scientists to predict and modify the ecosystem using one of the computational models, Starlogo-nova. This study found that the computational model was able to stimulate students' scientific practice positively. First, the computational model can facilitate students' creation, evaluation, and revision of the ecosystem models. Second, the computational model can help students formulate theories for more sustainable the ecosystem models. Based on the results of the study, this study concluded that the computer model should be used in future science education.

참고문헌 (31)

  1. Bielik, T., "Working together : Integrating computational modeling approaches to investigate complex phenomena", Journal of Science Education and Technology, 30, pp.40~57 (2021)
  2. Braun, V., "Using thematic analysis in psychology", Qualitative Research in Psychology, 3(2), pp.77~101 (2006)
  3. Coll, R. K., "Models and Modeling", pp.3~21 (2011), Springer
  4. Corbin, J., "Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory" (2014), Sage publications
  5. Han, M. H., "Elementary students’ reasoning patterns represented in constructing models of ‘Food web and food pyramid’", Journal of the Korean Elementary Science Education, 31(1), pp.71~83 (2012)
  6. Jho, H., "A tutorial for applying machine learning to the science education for the gifted : Focus on the daily temperature of Seoul", Journal of Science Education for the Gifted, 13(3), pp.133~149 (2021)
  7. Ke, L., "Supporting students’ meaningful engagement in scientific modeling through epistemolgical messages : A case study of contrasting teaching approaches", Journal of Research in Science Teaching, 58(2), pp.335~365 (2021)
  8. Krüger, J. T., "Two comparative studies of computer simulations and experiments as learning tools in school and out-of-school education", Instructional Science, 50, pp.169~197 (2022)
  9. Ministry of Education, , 2022
  10. NGSS Lead States., "Next Generation Science Standards : For States, by States" (2013), National Academies Press
  11. Nguyen, H., "Impact of computer modeling on learning and teaching systems thinking", Journal of Research in Science Teaching, 58(5), pp.661~688 (2021)
  12. Pierson, A. E., "Balancing the environment : Computational models as interactive participants in a STEM classroom", Journal of Science Education and Technology, 29, pp.101~119 (2020)
  13. Pierson, A. E., "Emotional configurations in STEM classrooms : Braiding feelings, sensemaking, and practices in extended investigations", Science Education, 107(5), pp.1126~1162 (2023)
  14. Puttick, G., "So, we kind of started from scratch, no pun intended : What can students learn from designing games?", Journal of Research in Science Teaching, 61(4), pp.772~808 (2023)
  15. Rachmatuullah, A., "Building a computational model of food webs : Impacts on middle school students’ computational and systems thinking skills", Journal of Research in Science Teaching, 59(4), pp.585~618 (2022)
  16. Scherr, R. E., "Productivity of"collisions generate heat"for reconciling an energy model with mechanistic reasoning : A case study", Physical Review Special Topics—Physics Education Research, 11(1), pp.1~16 (2015)
  17. Schwarz, C. V., "Helping Students Make Sense of the World Using Next Generation Science and Engineering Practices" (2017), NSTA Press
  18. Sengupta, P., "Programming in K-12 science classrooms", Communications of the ACM, 58(11), pp.33~35 (2015)
  19. Sengupta, P., "Integrating computational thinking with K-12 science education using agent-based computation : A theoretical framework", Education and Information Technologies, 18(2), pp.351~380 (2013)
  20. Shim, S. Y., "Framing negotiation : Dynamics of epistemological and positional framing in small groups during scientific modeling", Science Education, 102(1), pp.128~152 (2018)
  21. Shin, W. S., "A case study on application of artificial intelligence convergence education in elementary biological classification learning", Journal of the Korean Elementary Science Education, 39(2), pp.284~295 (2020)
  22. Shute, V. J., "Demystifying computational thinking", Educational Research Review, 22, pp.142~158 (2017)
  23. Tang, X., "Assessing computational thinking: A systematic review of empirical studies", Computers & Education, 148, pp.1~22 (2020)
  24. Uhm, J., "Changes in teaching practices of elementary school teachers in scientific modeling classes : Focused on modeling pedagogical content knowledge(PCK)", Journal of the Korean Association for Science Education, 40(5), pp.543~563 (2020)
  25. Uhm, J., "Exploring the agency of a student leader in collaborative scientific modeling classes in an elementary school", Journal of the Korean Association for Science Education, 41(4), pp.339~358 (2021)
  26. Xiang, L., "Supporting three-dimensional learning on ecosystems using an agent-based computer model", Journal of Science Education and Technology, 31, pp.473~489 (2022)
  27. Wagh, A., "Evobuild : A quickstart toolkit for programming agent-based models of evolutionary processes", Journal of Science Education and Technology, 27, pp.131~146 (2018)
  28. Weintrop, D., "Defining computational thinking for mathematics and science classrooms", Journal of Science Education and Technology, 25(1), pp.127~147 (2016)
  29. Wilensky, U., "Thinking in levels : A dynamic systems perspective to making sense of the world", Journal of Science Education and Technology, 8(1), pp.3~19 (1999)
  30. Wilkerson, M., "Balancing curricular and pedagogical needs in computational construction kits : Lessons from the DeltaTick project", Science Education, 99(3), pp.465~499 (2015)
  31. Yang, Y. Y., "Exploring challenges and suggestions faced by pre-service biology teachers in developing a Netlogo practical program for an ecology course", Biology Education, 52(1), pp.13~31 (2024)

관련 논문AI추천

이 간행물의 다른 논문 (13건)