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Reasoning Scaffold Model for Instructional Simulation Development and Application

Year 2015, , 143 - 169, 17.11.2014
https://doi.org/10.14812/cuefd.54365

Abstract

Numerous studies have been carried out in computer-mediated and computer-supported learning environments. They have reported the effectiveness of scaffolding strategies for engaging students in the learning process. These studies in variety of subjects such as economics, mathematics have focused mostly on developing ill-structured problem solving, decision making, and critical thinking skills and rarely on argumentation skills. On the other hand, concept attainment is discussed and studied intensively from various theoretical perspectives for more than 50 years. Theory-based concept learning claims that people understand and explain new situations based on their prior experience which has resemblance to a theory in itself. It is compatible with constructivist approaches. For a basic 8th Grade Genetic Simulation, a scaffolding model is developed according to the theory-based concept view and scientific discovery learning. Toulmin argumentation model as scaffolding strategy is functionalized by Socratic questioning technique to engage learner in analysis and reflection in a simulated concept learning environment. In this paper, the model for scaffolded simulation and the development process are explained. The issues arising from a successful application study of simulation model are presented.

References

  • Aldağ, H. (2005a). The Effects of Textual ant Graphical-Textual Argumentation Software As Cognitive Tools on Development of Argumentation Skills. Unpublished doctoral thesis. Çukurova University, Adana-Turkey
  • Aldag, H. (2005b). Problems in university students’ argumentative writing ant text analysis, Biltek International Informatics Congress,10-12 June, 2005, Eskişehir. Turkey.
  • Aldağ, H. (2006a). Toulmin Tartışma Modeli. Ç.Ü. Sosyal Bilimler Enstitüsü Dergisi, Cilt 15, Sayı 1, s.13-34
  • Aldag, H. & Doganay A. (2006b). The Effects of Textual ant Graphical-Textual Argumentation Software As Cognitive Tools on Development of Argumentation Skills, 6th International Conference on Argumentation of the International Society for the Study of Argumentation (ISSA), 27-30 June, University of Amsterdam-Holland
  • Akbulut-Taş, M. (2010). The effect of explicit instruction ant implicit learning of concept ant generalization structure on the classification ant explanation behavior, retention of the classification ant explanation behavior ant transfer. Unpublished doctoral thesis. Çukurova University-Turkey
  • Bangert-Drowns, R., Kulik, J., & Kulik, C. (1985). Effectiveness of computer-based education in secondary schools. Journal of Computer Based Instruction, 12, 59-68.
  • Biben, R.F. (1980). Using inquiry effectively. Theory into Practice 19(2), 87-92.
  • Brant, G., Hooper, E., & Sugrue, B. (1991). Which comes first the simulation or the lecture? Journal of Educational Computing Research, 7, 469-481.
  • Bruner, J.S. (1961). The act of discovery. Harvard Educational Review, 31, 21-32.
  • Bruner J.S., Goodnow, G.G., & Austin, G. A. (1967). A study of Thinking. New York, NY: Science Edition.
  • Carlsen, D.D., & Andre, T. (1992). Use of a microcomputer simulation and conceptualchange text to overcome students preconceptions about electric circuits. Journal of Computer-Based Instruction, 19, 105-109.
  • Carr, C. S. (1999). The effect of computer-supported collaborative argumentation (CSCA) on argumentation skills in second-year law student. Unpublished doctoral dissertation, The Pennsylvania State University, Pennsylvania.
  • Chambers, S.K., Haselhuhn, C., Andre, T., Mayberry, C., Wellington, S., Krafka, A., Volmer, J., & Berger, J. (1994, April). The acquisition of a scientific understanding of electricity: Hands-on versus computer simulation experience; conceptual change versus didactic text. Paper presented at the Annual Meeting of the American Educational Research Association, New Orleans, LA.
  • Chinn, C.A., & Brewer, W.F. (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research, 63, 1-51.
  • Cho, Kyoo-Lak (2001). The effects of argumentation scaffols on argumentation and problem solving in an online colloborative problem solving environment. Unpublished Doctoral Dissertation. The Pennsylvania State University, Pennsylvania.
  • Coleman, T.G., & Randall, J.E. (1986).Human-pc: A comprehensive physiological model [Computer software]. Jackson: University of Mississippi Medical Center.
  • Davis, E. A., & Linn, M. C. (2000). Scaffolding students' knowledge integration: Prompts for reflection in KIE. International Journal of Science Education, 22(8), 819-837.
  • de Jong, T. (1991). Learning and instruction with computer simulations. Education & Computing, 6, 217-229.
  • de Jong, T. & van Joolingen, W.R. (1998) Scientific discovery learning with computer simulations of conceptual domain, Journal Review of Educational Research, 68 (2), 179-202
  • diSessa, A., Abelson, H., & Ploger, D. (1991). An overview of Boxer. Journal of Mathematical Behavior, 10, 3-15.
  • Driver, R., J. Leach, R. Millar, & P. Scott (1996). Young’s People Images Of Science, Buckingham: Open University Pres.
  • Driver, R., P.Newton, & J.Osborne (2000). Establishing the norms of scientific argumentation in classroom, Science Education, 20, 1059-1073.
  • Dunbar, K. (1993). Concept discovery in a scientific domain. Cognitive Science, 17, 397-434.
  • Duschl, R. A., K. Ellenbogen, & S. Erduran (1997). Promoting argumentation in middle school science classrooms: A Project SEPIA evaluation. The Annual Meeting of the National Association of Research in Science Teaching.
  • Edmundson, K.M. (2000). Assessing science understanding through concept maps. In J. J. Mintzes, J. H. Wandersee, J. D. Novak (Eds.), Assessing science understanding: A human constructivist view (pp. 19–40). San Diego: Academic Press.
  • Eggen & Kauchak (2001). Strategies for Teacher. (4th ed)Needham Heights, MA: Alyn and Bacon.
  • Ferrari, M. and Michelene T. H. Chi (1998), The nature of naive explanations of natural selection, Learning Research and Development Center, University of Pittsburgh, USA.
  • Friedler, Y., Nachmias, R., & Linn, M.C. (1990). Learning scientific reasoning skills in microcomputer-based laboratories. Journal of Research in Science Teaching, 27, 173-191.
  • Gagne, R.M., Briggs, L. J., & Walter, W.W. (1992) Principles of instructional design (4th. ed.). New York, NY: Harcourt Brace Jovanovich
  • Gall, J. & Hannafin, M. (1994). A framework for the study of hypertext. Instructional Science,. 22, 207-232.
  • Ge, X. & Land, S.M. (2003), Scaffolding Students’ Problem-Solving Processes in an ll-Structured Task Using Question Prompts and Peer Interactions, ETR&D, 51(1), 2003, pp. 21-38.
  • Ge, X., & Land, S. M. (2004). A conceptual framework for scaffolding II-structured problem-solving processes using question prompts and peer interactions. Educational Technology Research and Development, 52(2), 5-22.
  • Schauble, L., Glaser, R., Raghavan, K., & Reiner, M. (1991). Causal models and experimentation strategies in scientific reasoning. Journal of the Learning Sciences, 1 (2), 201-238.
  • Grimes, P.W. & Willey, T.E. (1990). The effectiveness of microcomputer simulations in the principles of economics course. Computers and Education, 14, 81-86.
  • Guzdial, M. (1995). Software-realized scaffolding to facilitate programming for science learning. Interactive Learning Environments, 4(1), 1-44.
  • Hannafin, M.J. & Land S.M. (2000). Technology and student-centered learning in higher education: issues and Practice, Journal of Higher Education, 12(1), 3-30
  • Hogan, K., & Fisherkeller, J. (2000). Dialogue as data: Assessing students’ scientific reasoning with interactive protocols. In J. J. Mintzes, J. D. Novak, & J. W. Wandersee (Eds.), Assessing science understanding: A human constructivist view (pp. 96–124). San Diego, CA: Academic Press.
  • Jackson, S. L., Krajcik, J., & Soloway, E. (1998). The Design of Guided Learning-Adaptable Scaffolding inInteractive Learning Environments. Human Factors in Computing Systems: CHI '98 Conference Proceedings, Los Angeles.
  • Jonassen, D. H. (2006). On the role of concepts in learning and instructionaldesign. Educational Technology research and Development, 54(2), 177-196.
  • Joyce, B., Weil, M. & Calhoun, E. (2000). Models of Teaching (6th ed) Boston: Allyn and Bacon
  • Karataş-Coşkun, M. (2011). Kavram Öğretimi. Karahan Kitabevi, Adana-Turkey
  • Kelly, G.A. (1955). The Psychology of Personal Constructs. Norton. New York,
  • Kelly, G. A (1963) . A Theory of Personality. Norton, NewYork.
  • Kelly, G. J., S. Druker, & C. Chen, (1998). Students’ reasoning about electiricity: combining performance assessments with argumentation analysis. International Journal of Science Education, 20, 849-872.
  • Klahr, D., Fay, A.L., & Dunbar, K. (1993). Heuristics for scientific experimentation: A developmental study. Cognitive Psychology, 25, 111-146.
  • Klayman, J., & Ha, Y-W. (1987). Confirmation, disconfirmation, and information in hypothesis testing. Psychological Review, 94, 211-228.
  • Kuhn, D., Schauble, L., & Garcia-Mila, M. (1992). Cross-domain development of scientific reasoning. Cognition and Instruction, 9, 285-327.
  • Köroğlu (2009). The effect of argumentation scaffolds in simulation on academic success and argumentation structures-use in the 8th grade genetic unit. Unpublished master thesis. Çukurova University, Adana-Turkey.
  • Lasley II, T.j., Matczynski, T.J., & Rowley, J. B. (2002). Instructional Models: Strategies for Teaching in a Diverse Society. Belmon, CA: Wadsworth/Thomson Learning.
  • Lavoie, D.R., & Good, R. (1988). The nature and use of predictions skills in a biological computer simulation. Journal of Research in Science Teaching, 25, 335-360.
  • Lenat, D. B. (1982) Heuretics: Theoretical and Experimental Study of Heuristic Rules. AAAI, 159-163
  • Lunsford, K. J. (2002). Contextualizing Toulmin’s model in the writing classroom: a case study. Written Communication 19(1), 76-109.
  • Mazur, J. M. (2004). Conversation analysis for educational technologists: theoretical and methodological issues for researching the structures, processes and meaning of online talk. In D. H. Jonassen (Ed.), Handbook of Research on Educational Communications and Technology. Mahwah, NJ: Lawrence Erlbaum Associates.
  • Merill, M.D. (1983). Componet display theory. In C. M. Reigeluth (Ed.),Instructional Design Theories and Models: An Overview of Their Current Status. Hillsdale, NJ: Lawrence Erlbaum Associates
  • Ferrari, M. & Chi, M.T.H. (1998). The nature of naive explanations of natural selection. International Journal of Science Education,20(10), 1231-1256
  • Njoo, M., & de Jong, T. (1993). Exploratory learning with a computer simulation for control theory: Learning processes and instructional support. Journal of Research in Science Teaching, 30, 821-844.
  • Reigeluth, C.M., & Schwartz, E. (1989). An instructional theory for the design of computer-based simulations. Journal of Computer-Based Instruction, 16, 1-10.
  • Rieber, L.P. (1990). Using computer animated graphics in science instruction with children. Journal of Educational Psychology, 82, 135-140.
  • Rieber, L.P., & Parmley, M.W. (1995). To teach or not to teach? Comparing the use of computer-based simulations in deductive versus inductive approaches to learning with adults in science. Journal of Educational Computing Research, 14, 359-
  • Rivers, R. H., & Vockell, E. (1987). Computer simulations to stimulate scientific problem solving. Journal of Research in Science Teaching, 24, 403-415.
  • Russell, T. L. (1983). Analyzing arguments in science classroom discourse: Can teachers’ questions distort scientific authority,” Journal of Research in Science Teaching, 20, 27-45.
  • Schauble, L., Glaser, R., Raghavan, K., & Reiner, M. (1991). Causal models and experimentation strategies in scientific reasoning. The Journal of the Learning Sciences, 1, 201-239.
  • Schonfeld, D. (2000). Teaching Evolution in Secondary Schools: Historical Context, Social Concerns, and Stumbling Blocks, Department of Education, Kalamazoo College Kalamazoo, Michigan.
  • Shaw, M. L. G. (1981). Recent Advances in Personal Construct Technology. London: Academic Press.
  • Shute, V.J., & Glaser, R. (1990). A large-scale evaluation of an intelligent discovery world: Smithtown. Interactive Learning Environments, 1, 51-77.
  • Simmons, P.E., & Lunetta, V.N. (1993). Problem-solving behaviors during a genetics computer simulation: beyond the expert/novice dichotomy. Journal of Research in Science Teaching, 30, 153-173.
  • Southerland, S. A., Smith, M. U ., &Cummins, C. L. (2000). "What do you mean by that?" Using Structured Interviews to Assess Science Understanding. In J. J. Mintzes, J. H. Wandersee, & J. P. Novak (Eds)., Assessing science understanding: A human constructivist view. (Chapter 6). Academic Press.
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Öğretimsel Simulasyonlar için Akıl Yürütme Desteği Modeli: Tasarım, Geliştirme ve Uygulama

Year 2015, , 143 - 169, 17.11.2014
https://doi.org/10.14812/cuefd.54365

Abstract

Bilgisayar destekli öğretim çevrelerinde yeni destekleme stratejileri ile yapılan çalışmaların etkili olduğu görülmektedir. Bu çalışmalar genellikle ekonomi veya matematik gibi alanlarda iyi-yapılandırılmamış problem çözümleri, karar verme ve eleştirel düşünme üzerine yapılmıştır. Tartışma kuramın destekleme stratejileri ile ilişkilendirildiği bilgisayar destekli çalışmaların sayısı fazla değildir. Bunun yanında kavram öğretimi en az 50 yıldır farklı kuramsal yaklaşımlar ile çalışılmıştır. Kuram temelli kavram öğrenme bireyin kendine-özgü bilgi ve deneyimler temelinde olayları ilişkilendirdiğini veya açıkladığı savunmaktadır. Geleneksel öğretim tasarımı yaklaşımları ile kıyaslanıldığında kuram-temelli kavram öğrenme, yapıcı öğrenme yaklaşımı ile daha uyumlu görünmektedir. Bu ilke temelinde 8. Sınıf genetic ünitesindeki kavramların buluş yoluyla öğretimi için bir simulasyon modeli geliştirilmiş ve uygulanmıştır. Tartışma Modeli, öğrencinin inceleme ve yansıtma davranışlarını harekete geçirme amacıyla destek stratejisi olarak kullanılmıştır. Toulmin’in Tartışma modeli sokratik sorgulama tekniği ile buluşa destek yapı olarak işlevselleştirilmiştir. Bu çalışmada geliştirilen akıl yürütme-destekli simulasyon modeli ve modelin işleyiş süreci tanıtılacaktır

References

  • Aldağ, H. (2005a). The Effects of Textual ant Graphical-Textual Argumentation Software As Cognitive Tools on Development of Argumentation Skills. Unpublished doctoral thesis. Çukurova University, Adana-Turkey
  • Aldag, H. (2005b). Problems in university students’ argumentative writing ant text analysis, Biltek International Informatics Congress,10-12 June, 2005, Eskişehir. Turkey.
  • Aldağ, H. (2006a). Toulmin Tartışma Modeli. Ç.Ü. Sosyal Bilimler Enstitüsü Dergisi, Cilt 15, Sayı 1, s.13-34
  • Aldag, H. & Doganay A. (2006b). The Effects of Textual ant Graphical-Textual Argumentation Software As Cognitive Tools on Development of Argumentation Skills, 6th International Conference on Argumentation of the International Society for the Study of Argumentation (ISSA), 27-30 June, University of Amsterdam-Holland
  • Akbulut-Taş, M. (2010). The effect of explicit instruction ant implicit learning of concept ant generalization structure on the classification ant explanation behavior, retention of the classification ant explanation behavior ant transfer. Unpublished doctoral thesis. Çukurova University-Turkey
  • Bangert-Drowns, R., Kulik, J., & Kulik, C. (1985). Effectiveness of computer-based education in secondary schools. Journal of Computer Based Instruction, 12, 59-68.
  • Biben, R.F. (1980). Using inquiry effectively. Theory into Practice 19(2), 87-92.
  • Brant, G., Hooper, E., & Sugrue, B. (1991). Which comes first the simulation or the lecture? Journal of Educational Computing Research, 7, 469-481.
  • Bruner, J.S. (1961). The act of discovery. Harvard Educational Review, 31, 21-32.
  • Bruner J.S., Goodnow, G.G., & Austin, G. A. (1967). A study of Thinking. New York, NY: Science Edition.
  • Carlsen, D.D., & Andre, T. (1992). Use of a microcomputer simulation and conceptualchange text to overcome students preconceptions about electric circuits. Journal of Computer-Based Instruction, 19, 105-109.
  • Carr, C. S. (1999). The effect of computer-supported collaborative argumentation (CSCA) on argumentation skills in second-year law student. Unpublished doctoral dissertation, The Pennsylvania State University, Pennsylvania.
  • Chambers, S.K., Haselhuhn, C., Andre, T., Mayberry, C., Wellington, S., Krafka, A., Volmer, J., & Berger, J. (1994, April). The acquisition of a scientific understanding of electricity: Hands-on versus computer simulation experience; conceptual change versus didactic text. Paper presented at the Annual Meeting of the American Educational Research Association, New Orleans, LA.
  • Chinn, C.A., & Brewer, W.F. (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research, 63, 1-51.
  • Cho, Kyoo-Lak (2001). The effects of argumentation scaffols on argumentation and problem solving in an online colloborative problem solving environment. Unpublished Doctoral Dissertation. The Pennsylvania State University, Pennsylvania.
  • Coleman, T.G., & Randall, J.E. (1986).Human-pc: A comprehensive physiological model [Computer software]. Jackson: University of Mississippi Medical Center.
  • Davis, E. A., & Linn, M. C. (2000). Scaffolding students' knowledge integration: Prompts for reflection in KIE. International Journal of Science Education, 22(8), 819-837.
  • de Jong, T. (1991). Learning and instruction with computer simulations. Education & Computing, 6, 217-229.
  • de Jong, T. & van Joolingen, W.R. (1998) Scientific discovery learning with computer simulations of conceptual domain, Journal Review of Educational Research, 68 (2), 179-202
  • diSessa, A., Abelson, H., & Ploger, D. (1991). An overview of Boxer. Journal of Mathematical Behavior, 10, 3-15.
  • Driver, R., J. Leach, R. Millar, & P. Scott (1996). Young’s People Images Of Science, Buckingham: Open University Pres.
  • Driver, R., P.Newton, & J.Osborne (2000). Establishing the norms of scientific argumentation in classroom, Science Education, 20, 1059-1073.
  • Dunbar, K. (1993). Concept discovery in a scientific domain. Cognitive Science, 17, 397-434.
  • Duschl, R. A., K. Ellenbogen, & S. Erduran (1997). Promoting argumentation in middle school science classrooms: A Project SEPIA evaluation. The Annual Meeting of the National Association of Research in Science Teaching.
  • Edmundson, K.M. (2000). Assessing science understanding through concept maps. In J. J. Mintzes, J. H. Wandersee, J. D. Novak (Eds.), Assessing science understanding: A human constructivist view (pp. 19–40). San Diego: Academic Press.
  • Eggen & Kauchak (2001). Strategies for Teacher. (4th ed)Needham Heights, MA: Alyn and Bacon.
  • Ferrari, M. and Michelene T. H. Chi (1998), The nature of naive explanations of natural selection, Learning Research and Development Center, University of Pittsburgh, USA.
  • Friedler, Y., Nachmias, R., & Linn, M.C. (1990). Learning scientific reasoning skills in microcomputer-based laboratories. Journal of Research in Science Teaching, 27, 173-191.
  • Gagne, R.M., Briggs, L. J., & Walter, W.W. (1992) Principles of instructional design (4th. ed.). New York, NY: Harcourt Brace Jovanovich
  • Gall, J. & Hannafin, M. (1994). A framework for the study of hypertext. Instructional Science,. 22, 207-232.
  • Ge, X. & Land, S.M. (2003), Scaffolding Students’ Problem-Solving Processes in an ll-Structured Task Using Question Prompts and Peer Interactions, ETR&D, 51(1), 2003, pp. 21-38.
  • Ge, X., & Land, S. M. (2004). A conceptual framework for scaffolding II-structured problem-solving processes using question prompts and peer interactions. Educational Technology Research and Development, 52(2), 5-22.
  • Schauble, L., Glaser, R., Raghavan, K., & Reiner, M. (1991). Causal models and experimentation strategies in scientific reasoning. Journal of the Learning Sciences, 1 (2), 201-238.
  • Grimes, P.W. & Willey, T.E. (1990). The effectiveness of microcomputer simulations in the principles of economics course. Computers and Education, 14, 81-86.
  • Guzdial, M. (1995). Software-realized scaffolding to facilitate programming for science learning. Interactive Learning Environments, 4(1), 1-44.
  • Hannafin, M.J. & Land S.M. (2000). Technology and student-centered learning in higher education: issues and Practice, Journal of Higher Education, 12(1), 3-30
  • Hogan, K., & Fisherkeller, J. (2000). Dialogue as data: Assessing students’ scientific reasoning with interactive protocols. In J. J. Mintzes, J. D. Novak, & J. W. Wandersee (Eds.), Assessing science understanding: A human constructivist view (pp. 96–124). San Diego, CA: Academic Press.
  • Jackson, S. L., Krajcik, J., & Soloway, E. (1998). The Design of Guided Learning-Adaptable Scaffolding inInteractive Learning Environments. Human Factors in Computing Systems: CHI '98 Conference Proceedings, Los Angeles.
  • Jonassen, D. H. (2006). On the role of concepts in learning and instructionaldesign. Educational Technology research and Development, 54(2), 177-196.
  • Joyce, B., Weil, M. & Calhoun, E. (2000). Models of Teaching (6th ed) Boston: Allyn and Bacon
  • Karataş-Coşkun, M. (2011). Kavram Öğretimi. Karahan Kitabevi, Adana-Turkey
  • Kelly, G.A. (1955). The Psychology of Personal Constructs. Norton. New York,
  • Kelly, G. A (1963) . A Theory of Personality. Norton, NewYork.
  • Kelly, G. J., S. Druker, & C. Chen, (1998). Students’ reasoning about electiricity: combining performance assessments with argumentation analysis. International Journal of Science Education, 20, 849-872.
  • Klahr, D., Fay, A.L., & Dunbar, K. (1993). Heuristics for scientific experimentation: A developmental study. Cognitive Psychology, 25, 111-146.
  • Klayman, J., & Ha, Y-W. (1987). Confirmation, disconfirmation, and information in hypothesis testing. Psychological Review, 94, 211-228.
  • Kuhn, D., Schauble, L., & Garcia-Mila, M. (1992). Cross-domain development of scientific reasoning. Cognition and Instruction, 9, 285-327.
  • Köroğlu (2009). The effect of argumentation scaffolds in simulation on academic success and argumentation structures-use in the 8th grade genetic unit. Unpublished master thesis. Çukurova University, Adana-Turkey.
  • Lasley II, T.j., Matczynski, T.J., & Rowley, J. B. (2002). Instructional Models: Strategies for Teaching in a Diverse Society. Belmon, CA: Wadsworth/Thomson Learning.
  • Lavoie, D.R., & Good, R. (1988). The nature and use of predictions skills in a biological computer simulation. Journal of Research in Science Teaching, 25, 335-360.
  • Lenat, D. B. (1982) Heuretics: Theoretical and Experimental Study of Heuristic Rules. AAAI, 159-163
  • Lunsford, K. J. (2002). Contextualizing Toulmin’s model in the writing classroom: a case study. Written Communication 19(1), 76-109.
  • Mazur, J. M. (2004). Conversation analysis for educational technologists: theoretical and methodological issues for researching the structures, processes and meaning of online talk. In D. H. Jonassen (Ed.), Handbook of Research on Educational Communications and Technology. Mahwah, NJ: Lawrence Erlbaum Associates.
  • Merill, M.D. (1983). Componet display theory. In C. M. Reigeluth (Ed.),Instructional Design Theories and Models: An Overview of Their Current Status. Hillsdale, NJ: Lawrence Erlbaum Associates
  • Ferrari, M. & Chi, M.T.H. (1998). The nature of naive explanations of natural selection. International Journal of Science Education,20(10), 1231-1256
  • Njoo, M., & de Jong, T. (1993). Exploratory learning with a computer simulation for control theory: Learning processes and instructional support. Journal of Research in Science Teaching, 30, 821-844.
  • Reigeluth, C.M., & Schwartz, E. (1989). An instructional theory for the design of computer-based simulations. Journal of Computer-Based Instruction, 16, 1-10.
  • Rieber, L.P. (1990). Using computer animated graphics in science instruction with children. Journal of Educational Psychology, 82, 135-140.
  • Rieber, L.P., & Parmley, M.W. (1995). To teach or not to teach? Comparing the use of computer-based simulations in deductive versus inductive approaches to learning with adults in science. Journal of Educational Computing Research, 14, 359-
  • Rivers, R. H., & Vockell, E. (1987). Computer simulations to stimulate scientific problem solving. Journal of Research in Science Teaching, 24, 403-415.
  • Russell, T. L. (1983). Analyzing arguments in science classroom discourse: Can teachers’ questions distort scientific authority,” Journal of Research in Science Teaching, 20, 27-45.
  • Schauble, L., Glaser, R., Raghavan, K., & Reiner, M. (1991). Causal models and experimentation strategies in scientific reasoning. The Journal of the Learning Sciences, 1, 201-239.
  • Schonfeld, D. (2000). Teaching Evolution in Secondary Schools: Historical Context, Social Concerns, and Stumbling Blocks, Department of Education, Kalamazoo College Kalamazoo, Michigan.
  • Shaw, M. L. G. (1981). Recent Advances in Personal Construct Technology. London: Academic Press.
  • Shute, V.J., & Glaser, R. (1990). A large-scale evaluation of an intelligent discovery world: Smithtown. Interactive Learning Environments, 1, 51-77.
  • Simmons, P.E., & Lunetta, V.N. (1993). Problem-solving behaviors during a genetics computer simulation: beyond the expert/novice dichotomy. Journal of Research in Science Teaching, 30, 153-173.
  • Southerland, S. A., Smith, M. U ., &Cummins, C. L. (2000). "What do you mean by that?" Using Structured Interviews to Assess Science Understanding. In J. J. Mintzes, J. H. Wandersee, & J. P. Novak (Eds)., Assessing science understanding: A human constructivist view. (Chapter 6). Academic Press.
  • Strike, K. A. & Posner, G. J. (1985) A conceptual change view of learning and understanding in L.H.T. West, A.L. Pines (Eds.), Cognitive structure and conceptual change, Academic Press, New York
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There are 79 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Article
Authors

Habibe Aldağ

Ahmet Doğanay

Lütfiye Sema Köroğlu-ulutaş This is me

Publication Date November 17, 2014
Submission Date May 28, 2015
Published in Issue Year 2015

Cite

APA Aldağ, H., Doğanay, A., & Köroğlu-ulutaş, L. S. (2014). Reasoning Scaffold Model for Instructional Simulation Development and Application. Çukurova Üniversitesi Eğitim Fakültesi Dergisi, 44(1), 143-169. https://doi.org/10.14812/cuefd.54365

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