15,760
Views
45
CrossRef citations to date
0
Altmetric
Articles

A framework to foster problem-solving in STEM and computing education

ORCID Icon, , , , , & show all

References

  • Artigue, M., and M. Blomhoj. 2013. “Conceptualizing Inquiry-Based Education in Mathematics.” ZDM 45 (6): 797–810. doi:10.1007/s11858-013-0506-6.
  • Artigue, M., and P. Baptist. 2012. “Inquiry in Mathematics Education: Background Resources for Implementing Inquiry in Science and in Mathematics at School.” http://fibonacci.uni-bayreuth.de/resources/resources-for-implementing-inquiry.html .
  • Basey, J. M., and C. D. Francis. 2011. “Design of Inquiry-Oriented Science Labs: Impacts on Students’ Attitudes.” Research in Science & Technological Education 29 (3): 241–255. doi:10.1080/02635143.2011.589379.
  • Beckmann, A. 1989. Zur didaktischen Bedeutung der abbildungsgeometrischen Beweismethode für 12- bis 15-jährige Schüler. Bad Salzdetfurth: Franzbecker.
  • Boero, P. 1999. “Argumentation and Mathematical Proof: A Complex, Productive, Unavoidable Relationship in Mathematics and Mathematics Education.” International Newsletter on the Teaching and Learning of Mathematical Proof 7/8. http://www.lettredelapreuve.org/OldPreuve/Newsletter/990708Theme/990708ThemeUK.html
  • Booth, J. L., K. M. McGinn, C. Barbieri, K. N. Begolli, B. Chang, D. Miller-Cotto, L. K. Young, and J. L. Davenport. 2017. “Evidence for Cognitive Science Principles that Impact Learning in Mathematics.” In Acquisition of Complex Arithmetic Skills and Higher-Order Mathematics, edited by D. C. Geary, D. B. Berch, R. Ochsendorf, and K. M. Koepke, 297–325. London: Elsevier.
  • Brandon, R. N. 1994. “Theory and Experiment in Evolutionary Biology.” Synthese 99: 59–73. doi:10.1007/BF01064530.
  • Bransford, J. D., and B. S. Stein. 1984. The Ideal Problem Solver: Guide for Improving Thinking, Learning and Creativity. New York, NY: Freeman.
  • Chinn, C. A., and B. A. Malhotra. 2002. “Epistemologically Authentic Inquiry in Schools: A Theoretical Framework for Evaluating Inquiry Tasks.” Science Education 86: 175–218. doi:10.1002/sce.10001.
  • Cobern, W. W., D. Schuster, B. Adams, B. Applegate, B. Skjold, A. Undreiu, C. C. Loving, and J. D. Gobert. 2010. “Experimental Comparison of Inquiry and Direct Instruction in Science.” Research in Science & Technological Education 28 (1): 81–96. doi:10.1080/02635140903513599.
  • Dagher, Z. R., and S. Erduran. 2017. “Abandoning Patchwork Approaches to Nature of Science in Science Education.” Canadian Journal of Science, Mathematics and Technology Education 17 (1): 46–52. doi:10.1080/14926156.2016.1271926.
  • De Jong, T., W. R. Van Joolingen, A. Giezma, I. Girault, U. Hoppe, J. Kindermann, A. Kluge, et al. 2010. “Learning by Creating and Exchanging Objects: The SCY Experience.” British Journal of Educational Technology 41 (6): 909–921. doi:10.1111/j.1467-8535.2010.01121.x.
  • Department for Education. 2015. “Statutory Guidance. National Curriculum in England: Science Programmes of Study.” https://www.gov.uk/government/publications/national-curriculum-in-england-science-programmes-of-study/national-curriculum-in-england-science-programmes-of-study
  • Duncan, R. G., C. A. Chinn, and S. Barzilai. 2017. “Grasp of Evidence: Problematizing and Expanding the Next Generation Science Standards’ Conceptualization of Evidence.” Journal of Research in Science Teaching 55: 907–937. doi:10.1002/tea.21468.
  • Edelson, D. C., D. N. Gordin, and R. D. Pea. 1999. “Addressing the Challenges of Inquiry-Based Learning through Technology and Curriculum Design.” Journal of the Learning Sciences 8 (3–4): 391–450. doi:10.1080/10508406.1999.9672075.
  • Engeln, K., M. Euler, and K. Maaß. 2013. “Inquiry-Based Learning in Mathematics and Science: A Comparative Baseline Study of Teachers’ Beliefs and Practices across 12 European Countries.” ZDM 45 (6): 823–836. doi:10.1007/s11858-013-0507-5.
  • Epp, S. 1994. “The Role of Proof in Problem Solving.” In Mathematical Thinking and Problem Solving, edited by A. H. Schoenfeld, 257–286. Hillsdale, New Jersey, USA: Lawrence Erlbaum.
  • Erduran, S., and Z. R. Dagher. 2014. Reconceptualizing the Nature of Science for Science Education. Dordrecht, Netherlands: Springer.
  • Ewen, I. 1996. “Strategies for Problem Exploration.” In The Art of Problem Solving, edited by A. S. Posamentier and W. Schulz, 1–82. Thousand Oaks, Cal., USA: Corwin.
  • Forman, E. A. 2018. “The Practice Turn in Learning Theory and Science Education”. In Constructivist Education in an Age of Accountability, edited by D. W. Kritt, 97–111. London: Palgrave Macmillan doi:10.1007/978-3-319-66050-9_5.
  • Frensch, P. A., and J. Funke. 2005 “Thinking and Problem Solving ” In Encyclopedia of Life Support Systems (EOLSS), developed under the Auspices of the UNESCO. Oxford, UK: Eolss. http://hdl.handle.net/20.500.11780/369
  • Funke, J. 2004. “Komplexes Problemlösen - Möglichkeiten deduktivistischen Vorgehens.” In Allgemeine Psychologie und deduktivistische Methodologie, edited by E. Erdfelder and J. Funke, 281–300. Göttingen: Vandenhoeck & Ruprecht.
  • Gray, G. L., F. Constanzo, and M. E. Plesha. 2005. “Problem Solving in Statics and Dynamics: A Proposal for A Structured Approach.” Paper presented at the annual meeting for the American Society for Engineering Education, Portland, Oregon, June 12–15. https://peer.asee.org/15371
  • Greiff, S., A. Kretzschmar, and D. Leutner. 2014. “Problemlösen in der Pädagogischen Psychologie.” Zeitschrift für Pädagogische Psychologie 28 (4): 161–166. doi:10.1024/1010-0652/a000140.
  • Hartmann, S., A. Upmeier Zu Belzen, D. Krüger, and H. A. Pant. 2015. “Scientific Reasoning in Higher Education: Constructing and Evaluating the Criterion-Related Validity of an Assessment of Preservice Science Teachers’ Competencies.” Zeitschrift für Psychologie 223: 47–53. doi:10.1027/2151-2604/a000199.
  • Hmelo-Silver, C. E., R. G. Duncan, and C. A. Chinn. 2007. “Scaffolding and Achievement in Problem-Based and Inquiry Learning: A Response to Kirschner, Sweller, and Clark (2006).” Educational Psychologist 42 (2): 99–107. doi:10.1080/00461520701263368.
  • Hodson, D., and S. L. Wong. 2017. “Going beyond the Consensus View: Broadening and Enriching the Scope of NOS-Oriented Curricula.” Canadian Journal of Science, Mathematics and Technology Education 17 (1): 3–17. doi:10.1080/14926156.2016.1271919.
  • Hussy, W. 1998. Denken und Problemlösen. Stuttgart, Berlin, Köln: Kohlhammer.
  • Jang, H. 2016. “Identifying 21st Century STEM Competencies Using Workplace Data.” Journal of Science Education and Technology 25: 284–301. doi:10.1007/s10956-015-9593-1.
  • Karaca, K. 2017. “A Case Study in Experimental Exploration: Exploratory Data Selection at the Large Hadron Collider.” Synthese 194 (2): 333–354. doi:10.1007/s11229-016-1206-x.
  • Kay, J., M. Barg, A. Fekete, T. Greening, O. Hollands, J. H. Kingston, and K. Crawford. 200. “Problem-Based Learning for Foundation Computer Science Courses.” Computer Science Education 10 (2): 109–128. doi:10.1076/0899-3408(200008)10:2;1-C;FT109.
  • Keys, C. W., and L. A. Bryan. 2001. “Co-Constructing Inquiry-Based Science with Teachers: Essential Research for Lasting Reform.” Journal of Research in Science Teaching 38 (6): 631–645. doi:10.1002/tea.1023.
  • Kind, P., and J. Osborne. 2017. “Styles of Scientific Reasoning: A Cultural Rationale for Science Education?” Science Education 101 (1): 8–13. doi:10.1002/sce.21251.
  • Klahr, D., and K. Dunbar. 1988. “Dual Space Search During Scientific Reasoning.” Cognitive Science 12 (1): 1–48. doi:10.1207/s15516709cog1201_1.
  • Klieme, E., D. Leutner, and J. Wirth. 2005. Problemlösekompetenz von Schülerinnen und Schülern. Diagnostische Ansätze, theoretische Grundlagen und empirische Befunde der deutschen PISA-2000-Studie. Wiesbaden: Verlag für Sozialwissenschaften.
  • KMK – Sekretariat der Ständigen Konferenz der Kultusminister der Länder in der Bundesrepublik Deutschland. 2004a. Bildungsstandards im Fach Chemie für den Mittleren Schulabschluss. München: Wolters Kluwer.
  • KMK – Sekretariat der Ständigen Konferenz der Kultusminister der Länder in der Bundesrepublik Deutschland. 2004b. Bildungsstandards im Fach Biologie für den Mittleren Schulabschluss. München: Wolters Kluwer.
  • KMK – Sekretariat der Ständigen Konferenz der Kultusminister der Länder in der Bundesrepublik Deutschland. 2004c. Bildungsstandards Im Fach Physik Für Den Mittleren Schulabschluss. München: Wolters Kluwer
  • Kolodner, J. L., J. T. Gray, and B. B. Fasse. 2003. “Promoting Transfer through Case-Based Reasoning: Rituals and Practices in Learning by Design™ Classrooms.” Cognitive Science Quarterly 3 (2): 119–170.
  • Koppelt, J., and R. Tiemann. 2008. “Modellierung dynamischer Problemlösekompetenz.” In Zur Didaktik der Chemie und Physik, edited by D. Höttecke, 362–364. Münster: LIT.
  • Krajcik, J. 2015. “Three-Dimensional Instruction: Using a New Type of Teaching in the Science Classroom.” Science Scope 39 (3): 16–18. doi:10.2505/4/ss15_039_03_16.
  • Krämer, P., S. H. Nessler, and K. Schlüter. 2015. “Teacher Students’ Dilemmas When Teaching Science through Inquiry.” Research in Science & Technological Education 33 (3): 325–343. doi:10.1080/02635143.2015.1047446.
  • Kuhn, D., M. Garcia-Mila, A. Zohar, C. Anderson, S. H. White, D. Klahr, and S. M. Carver. 1995. “Strategies of Knowledge Acquisition.” Monographs of the Society for Research in Child Development 60 (4): 1–157. doi:10.2307/1166059.
  • Lehmann, M. 2018. “Relevante mathematische Kompetenzen von Ingenieurstudierenden im ersten Studienjahr – Ergebnisse einer empirischen Untersuchung.” PhD diss., Humboldt-Universität zu Berlin. doi: 10.18452/19315.
  • Lehrer, R., and L. Schauble. 2015. “The Development of Scientific Thinking.” In Vol. 2 Of Handbook of Child Psychology and Developmental Science, edited by R. M. Lerner, 671–715. 7th ed ed. New York: Wiley.
  • Leuders, T., D. Naccarella, and K. Philipp. 2011. “Experimentelles Denken – Vorgehensweisen beim innermathematischen Experimentieren.” JMD 32: 205–231. doi:10.1007/s13138-011-0027-1.
  • Leuders, T., and K. Philipp. 2014. “Mit Beispielen zum Erkenntnisgewinn – Experiment und Induktion in der Mathematik.” Mathematica Didactica 37: 163–190.
  • Lewis, R. W. 1988. “Biology: A Hypothetico-Deductive Science.” The American Biology Teacher 50 (6): 362–366. doi:10.2307/444876.
  • Maaß, K., and M. Artigue. 2013. “Implementation of Inquiry-Based Learning in Day-to-Day Teaching: A Synthesis.” ZDM 45: 779–795. doi:10.1007/s11858-013-0528-0.
  • Maaß, K., and M. Doormann. 2013. “A Model for A Widespread Implementation of Inquiry-Based Learning.” ZDM 45: 887–899. doi:10.1007/s11858-013-0505-7.
  • Mäeots, M., M. Pedaste, and T. Sarapuu. 2011. “Interactions between Inquiry Processes in a Web-Based Learning Environment.” 11th IEEE International Conference on Advanced Learning Technologies in Athens, Georgia, USA, 331–335. doi:10.1109/ICALT.2011.103.
  • Mahootian, F., and T. Eastman. 2008. “Complementary Frameworks of Scientific Inquiry: Hypothetico-Deductive, Hypothetico-Inductive, and Observational-Inductive.” World Futures 64: 128–142. doi:10.1080/02604020701845624.
  • Marulcu, I., and M. Barnett. 2016. “Impact of an Engineering Design-Based Curriculum Compared to an Inquiry-Based Curriculum on Fifth Graders’ Content Learning of Simple Machines.” Research in Science & Technological Education 34 (1): 85–104. doi:10.1080/02635143.2015.1077327.
  • Mehalik, M. M., Y. Doppelt, and C. D. Schuun. 2008. “Middle-School Science through Design-Based Learning versus Scripted Inquiry: Better Overall Science Concept Learning and Equity Gap Reduction.” Journal of Engineering Education 97 (1): 71–85. doi:10.1002/j.2168-9830.2008.tb00955.x.
  • Merrill, K. L., S. W. Smith, M. M. Cumming, and A. P. Daunic. 2017. “A Review of Social Problem-Solving Interventions: Past Findings, Current Status, and Future Directions.” Review of Educational Research 87 (1): 71–102. doi:10.3102/0034654316652943.
  • Millar, R., J. Le Maréchal, and A. Tiberghien. 1999. “‘Mapping’ the Domain: Varieties of Practical Work.” In Practical Work in Science Education. Recent Research Studies, edited by J. Leach and A. Paulsen, 33–59. Frederiksberg: Roskilde University Press.
  • National Research Council (NRC). 1996. National Science Education Standards. Washington, DC: National Academy Press.
  • Neumann, I., B. Rösken-Winter, M. Lehmann, C. Duchhardt, A. Heinze, and R. Nickolaus. 2015. “Measuring Mathematical Competences of Engineering Students at the Beginning of Their Studies.” Peabody Journal of Education 90 (4): 465–476. doi:10.1080/0161956X.2015.1068054.
  • NGSS Lead States. 2013. Next Generation Science Standards: For States, by States. Washington, DC: National Academies Press.
  • Nuutila, E., S. Törmä, and L. Malmi. 2005. “PBL and Computer Programming - the Seven Steps Method with Adaptations.” Computer Science Education 15 (2): 123–142. doi:10.1080/08993400500150788.
  • OECD. 2013. PISA 2012 Assessment and Analytical Framework: Mathematics, Reading, Science, Problem Solving and Financial Literacy. Paris, France: OECD. doi:10.1787/9789264190511-en.
  • Osborne, J. 2013. “The 21st Century Challenge for Science Education: Assessing Scientific Reasoning.” Thinking Skills and Creativity 10: 265–279. doi:10.1016/j.tsc.2013.07.006.
  • Osborne, J. 2014. “Scientific Practices and Inquiry in the Science Classroom.” In Handbook of Research on Science Education, edited by N. G. Lederman and S. K. Abell, 579–599. New York, NY: Routledge.
  • Osborne, J., S. Erduran, and S. Simon. 2004. “Enhancing the Quality of Argumentation in School Science.” Journal of Research in Science Teaching 41 (10): 994–1020. doi:10.1002/tea.20035.
  • Oser, F. K., and F. J. Baeriswyl. 2001. “Choreographies of Teaching: Bridging Instruction to Learning.” In AERA’s Handbook of Research on Teaching, edited by V. Richardson, 1031–1065. 4th ed ed. Washington, DC: American Educational Research Association.
  • Pedaste, M., M. Mäeots, L. A. Siiman, T. De Jong, S. Van Riesen, E. T. Kamp, C. C. Manoli, C. Z. Zacharia, and E. Tsourlidaki. 2015. “Phases of Inquiry-Based Learning: Definitions and the Inquiry Cycle.” Educational Research Review 14: 47–61. doi:10.1016/j.edurev.2015.02.003.
  • Polya, G. 1957. How to Solve It. 2nd ed ed. Princeton, NJ: Princeton University Press.
  • Polya, G. 1966. Let Us Teach Guessing. Vol. 1 Of MAA Video Classics. Washington, DC: Mathematical Association of America.
  • Priemer, B., and J. Hellwig. 2018. “Learning about Measurement Uncertainties in Secondary Education: A Model of the Subject Matter.” International Journal of Science and Mathematics Education 16 (1): 45–68. doi:10.1007/s10763-016-9768-0.
  • Przybylla, M., and R. Romeike. 2015. “Concept-Maps als Mittel zur Visualisierung des Lernzuwachses in einem Physical-Computing-Projekt.” In INFOS 2015: Informatik allgemeinbildend begreifen (16. GI-Fachtagung Informatik und Schule), edited by J. Gallenbacher, 247–256. Bonn: Köllen.
  • Pucher, R., and M. Lehner. 2011. “Project Based Learning in Computer Science - A Review of More than 500 Projects.” Procedia - Social and Behavioral Sciences 29: 1561–1566. doi:10.1016/j.sbspro.2011.11.398.
  • Reif, F. 1983. “How Can Chemists Teach Problem Solving? Suggestions Derived from Studies of Cognitive Processes.” Journal of Chemical Education 60 (11): 948–953. doi:10.1021/ed060p948.
  • Reiss, K., and A. Heinze. 2004. “Beweisen und Begründen in der Geometrie. Zum Einfluss des Unterrichts auf Schülerleistungen und Schülerinteresse.” In Beiträge zum Mathematikunterricht 2004, edited by A. Heinze and S. Kuntze, 465–468. Hildesheim: Franzbecker.
  • Riga, F., M. Winterbottom, E. Harris, and L. Newby. 2017. “Inquiry-Based Science Education.” In Science Education, edited by K. S. Taber and B. Akpan, 247–261. Rotterdam: Sense. doi:10.1007/978-94-6300-749-8.
  • Schauble, L. 1990. “Belief Revision in Children: The Role of Prior Knowledge and Strategies for Generating Evidence.” Journal of Experimental Child Psychology 49 (1): 31–57. doi:10.1016/0022-0965(90)90048-D.
  • Schoenfeld, A. 1985. Mathematical Problem Solving. San Diego, CA, USA: Academic Press.
  • Schulz, S., and N. Pinkwart. 2016. “Towards Supporting Scientific Inquiry in Computer Science Education.” WiPSCE ‘16 - Proceedings of the 11th Workshop in Primary and Secondary Computing Education, 45–53. New York, NY: ACM.
  • Sfard, A. 1998. “On Two Metaphors for Learning and the Dangers of Choosing Just One.” Educational Researcher 27 (2): 4–13. doi:10.3102/0013189X027002004.
  • Smit, R., H. Weitzel, R. Blank, F. Rietz, J. Tardent, and N. Robin. 2017. “Interplay of Secondary Pre-Service Teacher Content Knowledge (CK), Pedagogical Content Knowledge (PCK) and Attitudes regarding Scientific Inquiry Teaching within Teacher Training.” Research in Science & Technological Education 35 (4): 477–499. doi:10.1080/02635143.2017.1353962.
  • Steinle, F. 2002. “Experiments in History and Philosophy of Science.” Perspectives on Science, MIT Press Journals 10 (4): 408–432. doi:10.1162/106361402322288048.
  • Van Joolingen, W. R., and T. De Jong. 1997. “An Extended Dual Search Space Model of Scientific Discovery Learning.” Instructional Science 25: 307–346. doi:10.1023/A:1002993406499.
  • Van Joolingen, W. R., T. De Jong, A. W. Lazonder, E. R. Savelsbergh, and S. Manlove. 2005. “Co-Lab: Research and Development of an Online Learning Environment for Collaborative Scientific Discovery Learning.” Computers in Human Behavior 21: 671–688. doi:10.1016/j.chb.2004.10.039.
  • Vollrath, H. J. 1992. “Zur Rolle des Begriffs im Problemlöseprozeß des Beweisens.” Mathematische Semesterberichte 39 (2): 127–136. doi:10.1007/BF03186465.
  • Walpuski, M., and A. Schulz. 2011. “Erkenntnisgewinnung durch Experimente - Stärken und Schwächen deutscher Schülerinnen und Schüler.” Chimica didacticae 104: 6–27.
  • Wilhelm, P., and J. J. Beishuizen. 2003. “Content Effects in Self-Directed Inductive Learning.” Learning and Instruction 13: 381–402. doi:10.1016/S0959-4752(02)00013-0.
  • Williams, P. J., and K. Otrel-Cass. 2017. “Teacher and Student Reflections on ICT-Rich Science Inquiry.” Research in Science & Technological Education 35 (1): 88–107. doi:10.1080/02635143.2016.1248928.
  • Windschitl, M., J. Thompson, and M. Braaten. 2008. “Beyond the Scientific Method: Model-Based Inquiry as a New Paradigm of Preferences for School Science Investigations.” Science Education 92: 941–967. doi:10.1002/sce.20259.
  • Woods, D. R. 2000. “An Evidence-Based Strategy for Problem Solving.” Journal of Engineering Education 89 (3): 443–459. doi:10.1002/j.2168-9830.2000.tb00551.x.
  • Wu, M., and R. Adams. 2006. “Modelling Mathematics Problem Solving Item Responses Using a Multidimensional IRT Model.” Mathematics Education Research Journal 18 (2): 93–113. doi:10.1007/BF03217438.