Skip to main content

Advertisement

SpringerLink
Log in

The Role of Simulation-Enabled Design Learning Experiences on Middle School Students’ Self-generated Inherence Heuristics

  • Published:
Journal of Science Education and Technology Aims and scope Submit manuscript

Abstract

In science and engineering education, the use of heuristics has been introduced as a way of understanding the world, and as a way to approach problem-solving and design. However, important consequences for the use of heuristics are that they do not always guarantee a correct solution. Learning by Design has been identified as a pedagogical strategy that can guide individuals to properly connect science learning via design challenges. Specifically, we focus on the effect of simulation-enabled Learning by Design learning experiences on student-generated heuristics that can lead to solutions to problems. A total of 318 middle school students were exposed to a lesson that integrated design practices in the context of energy consumption and energy conservation considerations when designing buildings using an educational CAD tool. The students were pre- and posttested before and after the 2-week long intervention. The data analysis procedures combined qualitative with quantitative methods along with machine learning approaches. Our analysis revealed two distinct groups of students based on their learning achievement: the naive developing heuristic group and semi-knowledgeable fixated heuristic group. Differences between the groups are discussed in terms of performance, as well as implications for the use of computer simulations to improve student learning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Bertin, J. (1983). Semiology of graphics: diagrams, networks, maps. Madison: The University of Wisconsin Press.

    Google Scholar 

  • Bertin, J. (2000). Matrix theory of graphics. Information Design Journal, 10(1), 5–19.

    Article  Google Scholar 

  • Brewer, W. F., & Samarapungavan, A. (1991). Children’s theories vs. scientific theories: differences in reasoning or differences in knowledge?

  • Chinn, C. A., & Malhotra, B. A. (2002). Epistemologically authentic inquiry in schools: a theoretical framework for evaluating inquiry tasks. Sci Educ, 86(2), 175–218.

    Article  Google Scholar 

  • Christodoulou, A., & Osborne, J. (2014). The science classroom as a site of epistemic talk: a case study of a teacher’s attempts to teach science based on argument. J Res Sci Teach, 51(10), 1275–1300.

    Article  Google Scholar 

  • Cimpian, A., & Salomon, E. (2014a). The inherence heuristic: an intuitive means of making sense of the world, and a potential precursor to psychological essentialism. Behav Brain Sci, 37(5), 461–480.

    Article  Google Scholar 

  • Cimpian, A., & Salomon, E. (2014b). Refining and expanding the proposal of an inherence heuristic in human understanding. Behav Brain Sci, 37(5), 506–527.

    Article  Google Scholar 

  • D’Angelo, C., Rutstein, D., Harris, C., Bernard, R., Borokhovski, E., & Haertel, G. (2014). Simulations for STEM learning: systematic review and meta-analysis. Menlo Park: SRI International.

    Google Scholar 

  • Dasgupta, C., Magana, A. J., & Vieira, C. (2019). Investigating the affordances of a CAD enabled learning environment for promoting integrated STEM learning. Computers and Education, 129, 122–142. https://doi.org/10.1016/j.compedu.2018.10.014.

  • de Jong, T. (1991). Learning and instruction with computer simulations. Education and Computing, 6(3-4), 217–229.

    Article  Google Scholar 

  • de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2), 179–201.

    Article  Google Scholar 

  • Dede, C., Salzman, M. C., Loftin, R. B., & Sprague, D. (1999). Multisensory immersion as a modeling environment for learning complex scientific concepts. Modeling and Simulation in Science and Mathematics Education, 282–319.

  • Dori, Y. J., & Belcher, J. (2005). How does technology-enabled active learning affect undergraduate students’ understanding of electromagnetism concepts? The Journal of the Learning Sciences, 14(2), 243–279.

    Article  Google Scholar 

  • Douglass, B. G., & Moustakas, C. (1985). Heuristic inquiry: the internal search to know. Journal of humanistic Psychology, 25(3), 39–55.

    Article  Google Scholar 

  • Dunbar, K. (1995). How scientists really reason: scientific reasoning in real-world laboratories. The nature of insight, 18, 365–395.

    Google Scholar 

  • Feist, G. J., & Gorman, M. E. (1998). The psychology of science: review and integration of a nascent discipline. Review of general psychology, 2(1), 3–47.

    Article  Google Scholar 

  • Fleming, R. (1986). Adolescent reasoning in socio-scientific issues part II: nonsocial cognition. Journal of research in science teaching, 23(8), 689–698.

    Article  Google Scholar 

  • Foundations: inquiry: thoughts, views, and strategies for the K-5 classroom (Vol. 1). Arlington: National_Science_Foundation.

  • Gilbert, J. K., Bulte, A. M., & Pilot, A. (2011). Concept development and transfer in context-based science education. International Journal of Science Education, 33(6), 817–837.

    Article  Google Scholar 

  • Hennessy, S., Wishart, J., Whitelock, D., Deaney, R., Brawn, R., La Velle, L., … Winterbottom, M. (2007). Pedagogical approaches for technology-integrated science teaching. Computers & Education, 48(1), 137–152.

  • Hogan, K. (2002). Small groups’ ecological reasoning while making an environmental management decision. Journal of research in science teaching, 39(4), 341–368.

    Article  Google Scholar 

  • Jansson, D. G., & Smith, S. M. (1991). Design fixation. Design Studies, 12(1), 3–11.

    Article  Google Scholar 

  • Karmiloff-Smith, A. (1988). The child is a theoretician, not an inductivist. Mind & Language, 3(3), 183–196.

    Article  Google Scholar 

  • Klahr, D., Fay, A. L., & Dunbar, K. (1993). Heuristics for scientific experimentation: a developmental study. Cognitive Psychology, 25(1), 111–146.

    Article  Google Scholar 

  • Kolodner, J. L. (2002a). Facilitating the learning of design practices: lessons learned from an inquiry into science education. Journal of Industrial Teacher Education, 39(3), 9–40.

    Google Scholar 

  • Kolodner, J. L. (2002b). Learning by design™: iterations of design challenges for better learning of science skills. Cognitive Studies, 9(3), 338–350.

    Google Scholar 

  • Kolodner, J. L., Crismond, D., Gray, J., Holbrook, J., & Puntambekar, S. (1998). Learning by design from theory to practice. In Proceedings from the proceedings of the international conference of the learning sciences.

    Google Scholar 

  • Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., … Ryan, M. (2003a). Problem-based learning meets case-based reasoning in the middle-school science classroom: putting learning by design (tm) into practice. The Journal of the Learning Sciences, 12(4), 495–547.

  • Kolodner, J. L., Gray, J., & Fasse, B. B. (2003b). Promoting transfer through case-based reasoning: Rituals and practices in learning by design classrooms. Cognitive Science Quarterly, 3(2), 183–232.

    Google Scholar 

  • Kuhn, D. (1989). Children and adults as intuitive scientists. Psychological review, 96(4), 674–689.

    Article  Google Scholar 

  • Maeyer, J., & Talanquer, V. (2010). The role of intuitive heuristics in students’ thinking: ranking chemical substances. Science Education, 94(6), 963–984.

    Article  Google Scholar 

  • Niaz, M. (2001). Understanding nature of science as progressive transitions in heuristic principles. Science Education, 85(6), 684–690.

    Article  Google Scholar 

  • NSF. (2002). Foundations: A monograph for professionals in science, mathematics, and technology education. Inquiry. Thoughts, Views, and Strategies for the K-5 Classroom (Vol. 2). Arlington, VA: National Science Foundation.

  • Pearl, J. (1984). Heuristics: intelligent search strategies for computer problem solving. Reading: Addison-Wesley.

    Google Scholar 

  • Rehn, D. A., Moore, E. B., Podolefsky, N. S., & Finkelstein, N. D. (2013). Tools for high-tech tool use: a framework and heuristics for using interactive simulations. Journal of teaching and learning with technology, 2(1), 31–55.

    Google Scholar 

  • Roth, W. (1993). An investigation of problem framing and solving in a grade 8 open-inquiry science program. J Learn Sci, 3(2), 165–204.

    Article  Google Scholar 

  • Rutten, N., van Joolingen, W. R., & van der Veen, J. T. (2012). The learning effects of computer simulations in science education. Comput Educ, 58(1), 136–153.

    Article  Google Scholar 

  • Sampson, V., & Walker, J. P. (2012). Argument-driven inquiry as a way to help undergraduate students write to learn by learning to write in chemistry. Int J Sci Educ, 34(10), 1443–1485.

    Article  Google Scholar 

  • Schauble, L., Klopfer, L. E., & Raghavan, K. (1991). Students’ transition from an engineering model to a science model of experimentation. J Res Sci Teach, 28(9), 859–882.

    Article  Google Scholar 

  • Seah, Y. Y., & Magana, A. J. (2019). Exploring students’ experimentation strategies in engineering design using an educational CAD tool. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-018-9757-x.

  • Seroussi, D. E. (1995). Heuristic hypotheses in problem solving: an example of conceptual issues about scientific procedures. Science Education, 79(6), 595–609.

    Article  Google Scholar 

  • Shaffer, D. W., & Resnick, M. (1999). “Thick” authenticity: new media and authentic learning. Journal of Interactive Learning Research, 10(2), 195–215.

    Google Scholar 

  • Soler, L., Trizio, E., Nickles, T., & Wimsatt, W. (2012). Characterizing the robustness of science: after the practice turn in philosophy of science (Vol. 292). Berlin: Springer Science & Business Media.

    Book  Google Scholar 

  • Swaak, J., & de Jong, T. (2001). Discovery simulations and assessment of intuitive knowledge. J Comput Assist Learn, 17(3), 284–294.

    Article  Google Scholar 

  • Taleyarkhan, M., Dasgupta, C., Garcia, J. M., & Magana, A. J. (2018). Investigating the impact of using a CAD simulation tool on students’ learning of design thinking. Journal of Science Education and Technology, 27(4), 334–347.

    Article  Google Scholar 

  • Tversky, A., & Kahneman, D. (1975). Judgment under uncertainty: heuristics and biases utility, probability, and human decision making (pp. 141–162). Berlin: Springer.

    Book  Google Scholar 

  • Tytler, R. (2001). Dimensions of evidence, the public understanding of science and science education. International Journal of Science Education, 23(8), 815–832.

    Article  Google Scholar 

  • van Joolingen, W. R., de Jong, T., & Dimitrakopoulout, A. (2007). Issues in computer supported inquiry learning in science. Journal of Computer Assisted Learning, 23(2), 111–119.

    Article  Google Scholar 

  • Veermans, K., van Joolingen, W., & de Jong, T. (2006). Use of heuristics to facilitate scientific discovery learning in a simulation learning environment in a physics domain. International Journal of Science Education, 28(4), 341–361.

    Article  Google Scholar 

  • Vieira, C., Seah, Y. Y., & Magana, A. J. (2018). Students’ experimentation strategies in design: is process data enough? Computer Applications in Engineering Education, 25(5), 1903–1914.

    Article  Google Scholar 

  • Von Aufschnaiter, C., Erduran, S., Osborne, J., & Simon, S. (2008). Arguing to learn and learning to argue: case studies of how students’ argumentation relates to their scientific knowledge. Journal of research in science teaching, 45(1), 101–131.

    Article  Google Scholar 

  • Walker, J., Sampson, V., Southerland, S., & Enderle, P. (2016). Using the laboratory to engage all students in science practices. Chemistry Education Research and Practice, 17(4), 1098–1113.

    Article  Google Scholar 

  • Xie, C., Zhang, Z., Nourian, S., Pallant, A., & Hazzard, E. (2014). A time series analysis method for assessing engineering design processes using a CAD tool. International Journal of Engineering Education, 30(1), 218–230.

    Google Scholar 

  • Xie, C., Schimpf, C., Chao, J., Nourian, S., & Massicotte, J. (2018). Learning and teaching engineering design through modeling and simulation on a CAD platform. Computer Applications in Engineering Education, 26(4), 824–840. https://doi.org/10.1002/cae.21920.

    Article  Google Scholar 

  • Yellamraju, T., & Boutin, M. (2018). Clusterability and clustering of images and other “real” high-dimensional data. IEEE Transactions on Image Processing., 27(4), 1927–1938.

    Article  Google Scholar 

  • Yellamraju, T., Magana, A. J., & Boutin, M. (2019). Investigating students’ habits of mind in a course on digital signal processing. To appear in IEEE Transactions on Education.

  • Yilmaz, S., & Seifert, C. M. (2011). Creativity through design heuristics: a case study of expert product design. Design Studies, 32(4), 384–415.

    Article  Google Scholar 

  • Yzerbyt, V. Y., & Demoulin, S. (2014). Inherence heuristic versus essentialism: issues of antecedence and cognitive mechanism. Behavioral and Brain Sciences, 37(5), 505–506.

    Article  Google Scholar 

  • Zacharia, Z. C. (2007). Comparing and combining real and virtual experimentation: an effort to enhance students’ conceptual understanding of electric circuits. Journal of Computer Assisted Learning, 23(2), 120–132.

    Article  Google Scholar 

Download references

Funding

This research was funded by the US National Science Foundation under the award DRL #1503436.

Author information

Authors and Affiliations

  1. Computer and Information Technology and Engineering Education, Purdue University, Knoy Hall Building, Room 231, 401 N. Grant Street, West Lafayette, IN, 47907-1421, USA

    Alejandra J. Magana

  2. Computer and Information Technology, Purdue University, West Lafayette, IN, USA

    Sindhura Elluri, Chandan Dasgupta & Ying Ying Seah

  3. Educational Technology, Indian Institute of Technology Bombay, Mumbai, India

    Chandan Dasgupta

  4. Purdue Polytechnic Institute and Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, USA

    Aasakiran Madamanchi

  5. Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA

    Mireille Boutin

Authors
  1. Alejandra J. Magana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sindhura Elluri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chandan Dasgupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying Ying Seah
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Aasakiran Madamanchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mireille Boutin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alejandra J. Magana.

Ethics declarations

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent and parental consent were obtained from all individual participants included in the study.

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Magana, A.J., Elluri, S., Dasgupta, C. et al. The Role of Simulation-Enabled Design Learning Experiences on Middle School Students’ Self-generated Inherence Heuristics. J Sci Educ Technol 28, 382–398 (2019). https://doi.org/10.1007/s10956-019-09775-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10956-019-09775-x

Keywords

Search

Navigation