Araştırma Makalesi
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Yıl 2023, Cilt: 10 Sayı: 5, 119 - 146, 01.09.2023

Muhammed Dağlı Ahmet Feyzi Satıcı

https://doi.org/10.17275/per.23.78.10.5

Öz

Kaynakça

  • Ahmed, A., Hurwitz, D., Gestson, S., & Brown, S. (2021). Differences between Professionals and Students in Their Visual Attention on Multiple Representation Types While Solving an Open-Ended Engineering Design Problem. Journal of Civil Engineering Education, 147(3), 04021005.
  • Ainsworth, S. (1999). The functions of multiple representations. Computers & education, 33(2-3), 131-152.
  • Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and instruction, 16(3), 183-198.
  • Armougum, A., Gaston-Bellegarde, A., Joie-La Marle, C., & Piolino, P. (2020). Expertise reversal effect: Cost of generating new schemas. Computers in Human Behavior, 111, 106406.
  • Bayrak, B. K., & Bayram, H. (2010). Effect of computer aided teaching of acid-base subject on the attitude towards science and technology class. Procedia-Social and Behavioral Sciences, 2(2), 2194-2196.
  • Blayney, P., Kalyuga, S., and Sweller, J. (2015). Using cognitive load theory to tailor instruction to levels of accounting students’ expertise. Journal of Educational Technology & Society, 18(4), 199–210.
  • Büyüköztürk, Ş. (2016). Deneysel desenler: öntest-sontest kontrol grubu, desen ve veri analizi [Experimental designs: pretest-posttest control group, design and data analysis], 5th Edition, Ankara: Pegem Academy Publishing.
  • Bossé, M. J., Adu-Gyamfi, K., & Cheetham, M. (2011). Translations Among Mathematical Representations: Teacher Beliefs and Practices. International Journal for Mathematics Teaching & Learning.
  • Chandrasegaran, A. L., Treagust, D. F., & Mocerino, M. (2007). Enhancing pre-service science teachers' use of multiple levels of representation to describe and explain chemical reactions. School Science Review, 88(325), 115.
  • Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive psychology, 4(1), 55-81.
  • Chen, P. P. S. (1976). The entity-relationship model—toward a unified view of data. ACM Transactions on Database Systems (TODS), 1(1), 9-36.
  • Chi, M. T. H., Feltovich, P. J., and Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121-152.
  • Chi, M. T. H., and Glaser, R. (1985). Problem-solving ability. In R. J. Sternberg (Ed.), Human abilities: An information-processing approach (pp. 227-250). New York: W. H. Freeman and Company.
  • Chiappetta, E. L., Fillman, D. A., & Sethna, G. H. (2004). Procedures for conducting content analysis of science textbooks. Available from the University of Houston, Department of Curriculum and Instruction, Houston, Texas, USA.
  • Connolly, T. M., & Begg, C. E. (2006). A constructivist-based approach to teaching database analysis and design. Journal of Information Systems Education, 17(1), 43.
  • Çağıltay, N. E., Tokdemir, G. (2010). Veritabanı Sistemleri Dersi: Teoriden Pratiğe [Database Systems Course: From Theory to Practice] (1st ed.). Ankara: Ada Printing House Limited Company.
  • Fuchs, L. S., Fuchs, D., Prentice, K., Burch, M., Hamlett, C. L., Owen, R., Hosp, M., and Jancek, D. (2003). Explicitly teaching for transfer: effects on third grade students ‘mathematical problem solving. Journal of Educational Psychology, 95, 293-305.
  • de Croock, M. B. M., van Merriënboer, J. J. G. (2007). Paradoxical effect of information presentation formats and contextual interference on transfer of a complex cognitive skill. Computers in Human Behavior, 23, 1740–1761.
  • Greeno, J. (1978). Natures of problem-solving abilities. Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Hassebrock, F., Johnson, P. E., Bullemer, P., Fox, P. W., & Moller, J. H. (1993). When Less Is More: Representation and Selective Memory in Expert Problem Solving. The American Journal of Psychology, 106(2), 155–189. https://doi.org/10.2307/1423166
  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., ve Tatham, R. L. (2013). Multivariate Data Analysis: Pearson Education Limited.
  • Helsdingen, A. S., Van Gog, T., & Van Merriënboer, J. J. (2011). The effects of practice schedule on learning a complex judgment task. Learning and Instruction, 21(1), 126-136.
  • Jiang, D., Chen, O., Han, Y., & Kalyuga, S. (2023). Improving English language skills through learning Mathematic contents: From the expertise reversal effect perspective. British Journal of Educational Psychology, 00, 1–16. https://doi.org/10.1111/bjep.12596
  • Jonassen, D. H. (2011). Learning to Solve Problems. A Handbook for Designing Problem- Solving Learning Environments. New York and London: Routledge Taylor and Francis Group.
  • Jonassen, D., & Strobel, J. (2006). Modeling for Meaningful Learning Engaged Learning with Emerging Technologies, 1-27. University of Missouri, USA; Concordia University, Canada.
  • Kalyuga, S., Ayres, P., Chandler, P., ve Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38, 23-33.
  • Kalyuga, S., Chandler, P., and Sweller, J. (2000). Incorporating learner experience into the design of multimedia instruction. Journal of educational psychology, 92(1), 126.
  • Khacharem, A., Zoudji, B., and Kalyuga, S. (2015). Expertise reversal for different forms of instructional designs in dynamic visual representations. British Journal of Educational Technology, 46(4), 756–767. doi:10.1111/bjet.12167.
  • Kılıç, E., and Karadeniz, Ş. (2004). Specifying Students’ Cognitive Load and Disorientation Level in Hypermedia. Educational Administration in Theory and Practice, 10(4), 562579.
  • Kitchner, K. S. (1983). Cognition, metacognition, and epistemic cognition. Human development, 26(4), 222-232.
  • Kyun, S., Kalyuga, S., and Sweller, J. (2013). The effect of worked examples when learning to write essays in English literature. Journal of Experimental Education, 81(3), 385–408.
  • Larkin, J., McDermot, J., Simon, D. P., and Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335-1342.
  • Lee, J. Y., Donkers, J., Jarodzka, H., & Van Merriënboer, J. J. (2019). How prior knowledge affects problem-solving performance in a medical simulation game: Using game-logs and eye-tracking. Computers in Human Behavior, 99, 268-277.
  • Lim, J., Reiser, R. A., and Olina, Z. (2009). The effects of part-task and whole-task instructional approaches on acquisition and transfer of a complex cognitive skill. Educational Technology Research and Development, 57(1), 61-77.
  • McKillup, S. (2011). Statistics explained: An introductory guide for life scientists. Cambridge University Press.
  • Naylor, J. C., ve Briggs, G. E. (1963). Effects of task complexity and task organization on the relative efficiency of part and whole training methods. Journal of Experimental Psychology, 65, 217-224.
  • Paas, F. G., Van Merriënboer, J. J., & Adam, J. J. (1994). Measurement of cognitive load in instructional research. Perceptual and motor skills, 79(1), 419-430.
  • Rashkovits, R., & Lavy, I. (2021). Mapping Common Errors in Entity Relationship Diagram Design of Novice Designers. International Journal of Database Management Systems, 13(1), 1-19.
  • Reimann, P., and Chi, M. T. H. (1989). Human expertise. In K. J. Gilhooly (Ed.), Human and machine problem solving (pp. 161-191). New York, Plenum Press.
  • Renkl, A., Atkinson, R. K. (2003). Structuring the transition from example study to problem solving in cognitive skill acquisition: A cognitive load perspective. Educational psychologist, 38(1), 15-22.
  • Richardson, J. T. (2011). Eta squared and partial eta squared as measures of effect size in educational research. Educational Research Review, 6(2), 135-147.
  • Schrader, C., & Kalyuga, S. (2022). Expertise reversal effect in a pen-tablet-based learning environment: The role of learningcentered emotions in the interplay between learner expertise and task complexity. British Journal of Educational Psychology, 00, 1– 14. https://doi.org/10.1111/bjep.12547
  • Schwartz, D. L., Chase, C. C., Oppezzo, M. A., and Chin, D. B. (2011). Practicing verses inventing with contrasting cases: the effects of telling first on learning and transfer. Journal of Educational Psychology, 103, 759-775.
  • Seufert, T. (2003). Supporting coherence formation in learning from multiple representations. Learning and instruction, 13(2), 227-237.
  • Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591-611.
  • Simon, H. A. (1978). Information-processing theory of human problem solving. In W. K. Estes (Ed.), Handbook of learning and cognitive processes (Vol. 5, pp. 271-295). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Simon, H. A. (1980). Problem solving and education. In D. T. Tuma and F. Reif (Eds.), Problem solving and education: Issues in teaching and research (pp. 81-96). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Sinnott, J. D. (1989). A model for solution of ill-structured problems: Implications for everyday and abstract problem solving. In J. D. Sinnott (Ed.), Everyday problem solving: Theory and applications (pp. 72-99). New York: Praeger.
  • Sommerhoff, D., Kollar, I., & Ufer, S. (2020). Supporting Mathematical Argumentation and Proof Skills: Comparing the Effectiveness of a Sequential and a Concurrent Instructional Approach to Support Resource-Based Cognitive Skills. Frontiers in psychology, 11, 3664.
  • Spanjers, I. E., van Gog, T., and van Merriënboer, J. G. (2012). Segmentation of worked examples: Effects on cognitive load and learning. Applied Cognitive Psychology, 26(3), 352–358. doi:10.1002/acp.1832.
  • Spector, J. M., and Anderson, T. M. (2000). Holistic and integrated perspectives on learning, technology, and instruction: Understanding complexity. Mahwah, NJ: Lawrence Erlbaum. W.H. Freeman.
  • Sweller, J. (2012). Human cognitive architecture: Why some instructional procedures work and others do not. In K. R. Harris, S. Graham, T. Urdan, C. B. McCormick, G. M. Sinatra, & J. Sweller (Eds.), APA educational psychology handbook, Vol. 1. Theories, constructs, and critical issues (pp. 295–325). American Psychological Association. https://doi.org/10.1037/13273-011
  • Sweller, J. (2020). Cognitive load theory and educational technology. Education Tech Research Dev 68, 1–16. https://doi.org/10.1007/s11423-019-09701-3
  • Sweller, J., Ayres, P., and Kalyuga, S. (2011). Cognitive load theory. New York, NY: Springer.
  • van Gog, T., and Sweller, J. (2015). Not new, but nearly forgotten: The testing effect decreases or even disappears as the complexity of learning materials increases. Educational Psychology Review, 27(2), 247–264. doi:10.1007/s10648-015-9310-x.
  • van Loon-Hillen, N., Van Gog, T., & Brand-Gruwel, S. (2012). Effects of worked examples in a primary school mathematics curriculum. Interactive Learning Environments, 20(1), 89-99.
  • van Merriënboer, J. J. G., Kirschner, P. A., and Kester, L. (2003). Taking the load off a learners’mind: Instructional design for complex learning. Educational Psychologist, 38, 5–13.
  • Voss, J. F., Wolfe, C. R., Lawrence, J. A., and Engles, R. A. (1991). From representation to decision: an analysis of problem solving in international relations. In R. J. Sternberg (Ed.), Complex problem solving. Hillsdale, NJ: Lawrence Erlbaum Associates.

The effects of Multiple Representation Method and Prior Knowledge Level on Problem Solving Skills and Cognitive Load

Yıl 2023, Cilt: 10 Sayı: 5, 119 - 146, 01.09.2023

Muhammed Dağlı Ahmet Feyzi Satıcı

https://doi.org/10.17275/per.23.78.10.5

Öz

In this study, the effect of multiple representation method and prior knowledge level in solving ill-structured problems was investigated. Quasi-experimental and 2x2 (multiple representation method x prior knowledge level) factorial designs were used in the study. The study group consists of 39 undergraduate students. The dependent variables of the study were determined as problem solving skills, cognitive load, and permanence. The independent variables were considered as multiple representation method (step-by-step, holistic) and prior knowledge levels (novice, expert). Prior knowledge level test, problem solving skills test and cognitive load scale developed to measure the variables were used as data collection tools within the scope of the study. The data was analysed with two-way analysis of variance and independent groups t-test as the data obtained from the data collection tools exhibited normal distribution. In the results that were significant in the analysis, Cohen (d) in the independent groups t-tests and eta-square (η2) in the two-way analysis of variance were also shown. As a result of the research, it is evident that the level of prior knowledge and the interaction (interaction effect) of the level of prior knowledge and the multiple representation method affect problem solving skills. In addition, it was concluded that the interaction of multiple representation method and prior knowledge level was statistically significant in terms of cognitive load variable. The findings supported the expertise reversal effect.

Anahtar Kelimeler

Problem solving Cognitive load Prior knowledge Ill defined problems Worked examples Multiple representation

Kaynakça

  • Ahmed, A., Hurwitz, D., Gestson, S., & Brown, S. (2021). Differences between Professionals and Students in Their Visual Attention on Multiple Representation Types While Solving an Open-Ended Engineering Design Problem. Journal of Civil Engineering Education, 147(3), 04021005.
  • Ainsworth, S. (1999). The functions of multiple representations. Computers & education, 33(2-3), 131-152.
  • Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and instruction, 16(3), 183-198.
  • Armougum, A., Gaston-Bellegarde, A., Joie-La Marle, C., & Piolino, P. (2020). Expertise reversal effect: Cost of generating new schemas. Computers in Human Behavior, 111, 106406.
  • Bayrak, B. K., & Bayram, H. (2010). Effect of computer aided teaching of acid-base subject on the attitude towards science and technology class. Procedia-Social and Behavioral Sciences, 2(2), 2194-2196.
  • Blayney, P., Kalyuga, S., and Sweller, J. (2015). Using cognitive load theory to tailor instruction to levels of accounting students’ expertise. Journal of Educational Technology & Society, 18(4), 199–210.
  • Büyüköztürk, Ş. (2016). Deneysel desenler: öntest-sontest kontrol grubu, desen ve veri analizi [Experimental designs: pretest-posttest control group, design and data analysis], 5th Edition, Ankara: Pegem Academy Publishing.
  • Bossé, M. J., Adu-Gyamfi, K., & Cheetham, M. (2011). Translations Among Mathematical Representations: Teacher Beliefs and Practices. International Journal for Mathematics Teaching & Learning.
  • Chandrasegaran, A. L., Treagust, D. F., & Mocerino, M. (2007). Enhancing pre-service science teachers' use of multiple levels of representation to describe and explain chemical reactions. School Science Review, 88(325), 115.
  • Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive psychology, 4(1), 55-81.
  • Chen, P. P. S. (1976). The entity-relationship model—toward a unified view of data. ACM Transactions on Database Systems (TODS), 1(1), 9-36.
  • Chi, M. T. H., Feltovich, P. J., and Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121-152.
  • Chi, M. T. H., and Glaser, R. (1985). Problem-solving ability. In R. J. Sternberg (Ed.), Human abilities: An information-processing approach (pp. 227-250). New York: W. H. Freeman and Company.
  • Chiappetta, E. L., Fillman, D. A., & Sethna, G. H. (2004). Procedures for conducting content analysis of science textbooks. Available from the University of Houston, Department of Curriculum and Instruction, Houston, Texas, USA.
  • Connolly, T. M., & Begg, C. E. (2006). A constructivist-based approach to teaching database analysis and design. Journal of Information Systems Education, 17(1), 43.
  • Çağıltay, N. E., Tokdemir, G. (2010). Veritabanı Sistemleri Dersi: Teoriden Pratiğe [Database Systems Course: From Theory to Practice] (1st ed.). Ankara: Ada Printing House Limited Company.
  • Fuchs, L. S., Fuchs, D., Prentice, K., Burch, M., Hamlett, C. L., Owen, R., Hosp, M., and Jancek, D. (2003). Explicitly teaching for transfer: effects on third grade students ‘mathematical problem solving. Journal of Educational Psychology, 95, 293-305.
  • de Croock, M. B. M., van Merriënboer, J. J. G. (2007). Paradoxical effect of information presentation formats and contextual interference on transfer of a complex cognitive skill. Computers in Human Behavior, 23, 1740–1761.
  • Greeno, J. (1978). Natures of problem-solving abilities. Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Hassebrock, F., Johnson, P. E., Bullemer, P., Fox, P. W., & Moller, J. H. (1993). When Less Is More: Representation and Selective Memory in Expert Problem Solving. The American Journal of Psychology, 106(2), 155–189. https://doi.org/10.2307/1423166
  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., ve Tatham, R. L. (2013). Multivariate Data Analysis: Pearson Education Limited.
  • Helsdingen, A. S., Van Gog, T., & Van Merriënboer, J. J. (2011). The effects of practice schedule on learning a complex judgment task. Learning and Instruction, 21(1), 126-136.
  • Jiang, D., Chen, O., Han, Y., & Kalyuga, S. (2023). Improving English language skills through learning Mathematic contents: From the expertise reversal effect perspective. British Journal of Educational Psychology, 00, 1–16. https://doi.org/10.1111/bjep.12596
  • Jonassen, D. H. (2011). Learning to Solve Problems. A Handbook for Designing Problem- Solving Learning Environments. New York and London: Routledge Taylor and Francis Group.
  • Jonassen, D., & Strobel, J. (2006). Modeling for Meaningful Learning Engaged Learning with Emerging Technologies, 1-27. University of Missouri, USA; Concordia University, Canada.
  • Kalyuga, S., Ayres, P., Chandler, P., ve Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38, 23-33.
  • Kalyuga, S., Chandler, P., and Sweller, J. (2000). Incorporating learner experience into the design of multimedia instruction. Journal of educational psychology, 92(1), 126.
  • Khacharem, A., Zoudji, B., and Kalyuga, S. (2015). Expertise reversal for different forms of instructional designs in dynamic visual representations. British Journal of Educational Technology, 46(4), 756–767. doi:10.1111/bjet.12167.
  • Kılıç, E., and Karadeniz, Ş. (2004). Specifying Students’ Cognitive Load and Disorientation Level in Hypermedia. Educational Administration in Theory and Practice, 10(4), 562579.
  • Kitchner, K. S. (1983). Cognition, metacognition, and epistemic cognition. Human development, 26(4), 222-232.
  • Kyun, S., Kalyuga, S., and Sweller, J. (2013). The effect of worked examples when learning to write essays in English literature. Journal of Experimental Education, 81(3), 385–408.
  • Larkin, J., McDermot, J., Simon, D. P., and Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335-1342.
  • Lee, J. Y., Donkers, J., Jarodzka, H., & Van Merriënboer, J. J. (2019). How prior knowledge affects problem-solving performance in a medical simulation game: Using game-logs and eye-tracking. Computers in Human Behavior, 99, 268-277.
  • Lim, J., Reiser, R. A., and Olina, Z. (2009). The effects of part-task and whole-task instructional approaches on acquisition and transfer of a complex cognitive skill. Educational Technology Research and Development, 57(1), 61-77.
  • McKillup, S. (2011). Statistics explained: An introductory guide for life scientists. Cambridge University Press.
  • Naylor, J. C., ve Briggs, G. E. (1963). Effects of task complexity and task organization on the relative efficiency of part and whole training methods. Journal of Experimental Psychology, 65, 217-224.
  • Paas, F. G., Van Merriënboer, J. J., & Adam, J. J. (1994). Measurement of cognitive load in instructional research. Perceptual and motor skills, 79(1), 419-430.
  • Rashkovits, R., & Lavy, I. (2021). Mapping Common Errors in Entity Relationship Diagram Design of Novice Designers. International Journal of Database Management Systems, 13(1), 1-19.
  • Reimann, P., and Chi, M. T. H. (1989). Human expertise. In K. J. Gilhooly (Ed.), Human and machine problem solving (pp. 161-191). New York, Plenum Press.
  • Renkl, A., Atkinson, R. K. (2003). Structuring the transition from example study to problem solving in cognitive skill acquisition: A cognitive load perspective. Educational psychologist, 38(1), 15-22.
  • Richardson, J. T. (2011). Eta squared and partial eta squared as measures of effect size in educational research. Educational Research Review, 6(2), 135-147.
  • Schrader, C., & Kalyuga, S. (2022). Expertise reversal effect in a pen-tablet-based learning environment: The role of learningcentered emotions in the interplay between learner expertise and task complexity. British Journal of Educational Psychology, 00, 1– 14. https://doi.org/10.1111/bjep.12547
  • Schwartz, D. L., Chase, C. C., Oppezzo, M. A., and Chin, D. B. (2011). Practicing verses inventing with contrasting cases: the effects of telling first on learning and transfer. Journal of Educational Psychology, 103, 759-775.
  • Seufert, T. (2003). Supporting coherence formation in learning from multiple representations. Learning and instruction, 13(2), 227-237.
  • Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591-611.
  • Simon, H. A. (1978). Information-processing theory of human problem solving. In W. K. Estes (Ed.), Handbook of learning and cognitive processes (Vol. 5, pp. 271-295). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Simon, H. A. (1980). Problem solving and education. In D. T. Tuma and F. Reif (Eds.), Problem solving and education: Issues in teaching and research (pp. 81-96). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Sinnott, J. D. (1989). A model for solution of ill-structured problems: Implications for everyday and abstract problem solving. In J. D. Sinnott (Ed.), Everyday problem solving: Theory and applications (pp. 72-99). New York: Praeger.
  • Sommerhoff, D., Kollar, I., & Ufer, S. (2020). Supporting Mathematical Argumentation and Proof Skills: Comparing the Effectiveness of a Sequential and a Concurrent Instructional Approach to Support Resource-Based Cognitive Skills. Frontiers in psychology, 11, 3664.
  • Spanjers, I. E., van Gog, T., and van Merriënboer, J. G. (2012). Segmentation of worked examples: Effects on cognitive load and learning. Applied Cognitive Psychology, 26(3), 352–358. doi:10.1002/acp.1832.
  • Spector, J. M., and Anderson, T. M. (2000). Holistic and integrated perspectives on learning, technology, and instruction: Understanding complexity. Mahwah, NJ: Lawrence Erlbaum. W.H. Freeman.
  • Sweller, J. (2012). Human cognitive architecture: Why some instructional procedures work and others do not. In K. R. Harris, S. Graham, T. Urdan, C. B. McCormick, G. M. Sinatra, & J. Sweller (Eds.), APA educational psychology handbook, Vol. 1. Theories, constructs, and critical issues (pp. 295–325). American Psychological Association. https://doi.org/10.1037/13273-011
  • Sweller, J. (2020). Cognitive load theory and educational technology. Education Tech Research Dev 68, 1–16. https://doi.org/10.1007/s11423-019-09701-3
  • Sweller, J., Ayres, P., and Kalyuga, S. (2011). Cognitive load theory. New York, NY: Springer.
  • van Gog, T., and Sweller, J. (2015). Not new, but nearly forgotten: The testing effect decreases or even disappears as the complexity of learning materials increases. Educational Psychology Review, 27(2), 247–264. doi:10.1007/s10648-015-9310-x.
  • van Loon-Hillen, N., Van Gog, T., & Brand-Gruwel, S. (2012). Effects of worked examples in a primary school mathematics curriculum. Interactive Learning Environments, 20(1), 89-99.
  • van Merriënboer, J. J. G., Kirschner, P. A., and Kester, L. (2003). Taking the load off a learners’mind: Instructional design for complex learning. Educational Psychologist, 38, 5–13.
  • Voss, J. F., Wolfe, C. R., Lawrence, J. A., and Engles, R. A. (1991). From representation to decision: an analysis of problem solving in international relations. In R. J. Sternberg (Ed.), Complex problem solving. Hillsdale, NJ: Lawrence Erlbaum Associates.
Toplam 58 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Alan Eğitimleri, Eğitim Psikolojisi
Bölüm Research Articles
Yazarlar

Muhammed Dağlı 0000-0002-6395-2792

Ahmet Feyzi Satıcı 0000-0002-3469-1626

Erken Görünüm Tarihi 22 Ağustos 2023
Yayımlanma Tarihi 1 Eylül 2023
Kabul Tarihi 13 Haziran 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 10 Sayı: 5

Kaynak Göster

APA Dağlı, M., & Satıcı, A. F. (2023). The effects of Multiple Representation Method and Prior Knowledge Level on Problem Solving Skills and Cognitive Load. Participatory Educational Research, 10(5), 119-146. https://doi.org/10.17275/per.23.78.10.5
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