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The Interplays Between Teachers’ Self-Efficacy and Problem-Solving Competence in Technology-Mediated, Open-Ended Professional Development

  • Hui-Chen Durley
  • Xun Ge
Chapter

Abstract

This chapter is a report of a study conducted to explore the development of teachers’ technology-integration self-efficacy in an open-ended professional development (PD) environment. Six elementary teachers participated in problem-solving activities (i.e., collaboratively and independently) related to technology integration. Using videos to present authentic problems often encountered by teachers in their classrooms, the PD focused on technology-supported, authentic problem solving in an instructional context involving English learners (ELs, i.e., students whose home languages are not English). Think-aloud protocols were employed to explore teachers’ PD experiences regarding their problem-solving competence and self-efficacy in technology integration. Post-PD interviews were conducted to understand how group collaboration influenced individual judgments for technology integration. The results revealed that technology-integration self-efficacy and problem-solving competence influenced each other. This study implies that teacher PD should focus on supporting discursive interactions and real case scenarios to foster teachers’ problem-solving competence and enhance their positive self-efficacy in technology integration for everyday classroom problem solving.

Keywords

Collective intelligence Competence Collaboration Open-ended learning environments Problem solving Professional development Self-efficacy Technology integration 

References

  1. Ary, D., Jacobs, L. C., & Razavieh, A. (1996). Introduction to research in education (5th ed.). Orlando, FL: Harcourt Brace & Company.Google Scholar
  2. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191.CrossRefGoogle Scholar
  3. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  4. Barron, A. E., Kemker, K., Harmes, C., & Kalaydjian, K. (2003). Large-scale research study on technology in K-12 schools: Technology integration as it relates to the national technology standards. Journal of Research on Technology in Education, 35(4), 489–507.CrossRefGoogle Scholar
  5. Brinkerhoff, J. (2006). Effects of a long-duration, professional development academy on technology skills, computer self-efficacy, and technology integration beliefs and practices. Journal of Research on Technology in Education, 39(1), 22–43.CrossRefGoogle Scholar
  6. Brookfield, S. (1987). Significant personal learning. In D. Boud & V. Griffin (Eds.), Appreciating adult learning: From learners’ perspective (pp. 65–75). London, England: Kogan Page.Google Scholar
  7. Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32–42.CrossRefGoogle Scholar
  8. Buxton, C. A., Salinas, A., Mahotiere, M., Lee, O., & Secada, W. G. (2013). Leveraging cultural resources through teacher pedagogical reasoning: Elementary grade teachers analyze second language learners’ science problem solving. Teaching and Teacher Education, 32, 31–42.CrossRefGoogle Scholar
  9. Carney, M. B., Brendefur, J. L., Thiede, K., Hughes, G., & Sutton, J. (2016). Statewide mathematics professional development: Teacher knowledge, self-efficacy, and beliefs. Educational Policy, 30(4), 539–572.CrossRefGoogle Scholar
  10. Collins, A. (1991). Cognitive apprenticeship and instructional technology. In L. Idol & B. F. Jones (Eds.), Educational values and cognitive instruction: Implications for reform (pp. 121–138). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  11. Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 453–494). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  12. Cranton, P., & Carusetta, E. (2004). Perspectives on authenticity in teaching. Adult Education Quarterly, 55(1), 5–22.CrossRefGoogle Scholar
  13. Creswell, J. W. (1998). Qualitative inquiry and research design: Choosing among five traditions. Thousand Oaks, CA: Sage.Google Scholar
  14. Dewey, J. (1984). Experience and education. In Merriam (Ed.), S. B. Selected writings on philosophy and adult education, (pp. 13–17). Malabar, Florida: Robert E. Krieger Publishing.Google Scholar
  15. Duffy, T. M. & Cunningham, D. J. (1996). Constructivism: implications for the design and delivery of instruction. In Jonassen, D. H. (Ed.), Handbook of research for educational communications and technology: A project of association for educational communications and technology, (pp. 170–198). New York, NY: Macmillan Library Reference USA.Google Scholar
  16. Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data (Rev. ed.). Cambridge, MA: The MIT Press.Google Scholar
  17. Eseryel, D., Law, V., Ifenthaler, D., Ge, X., & Miller, R. (2014). An investigation of the interrelationships between motivation, engagement, and complex problem solving in game-based learning. Journal of Educational Technology & Society, 17(1), 42–53.Google Scholar
  18. Farrell, T. S., & Ives, J. (2015). Exploring teacher beliefs and classroom practices through reflective practice: A case study. Language Teaching Research, 19(5), 594–610.CrossRefGoogle Scholar
  19. Gagne, R. M., Briggs, L. J., & Wager, W. W. (1998). Principle of instructional design (3rd ed.). New York: Holt, Rinehart, and Winston, Inc..Google Scholar
  20. Ge, X., Chen, C. H., & Davis, K. A. (2005). Scaffolding novice instructional designers’ problem-solving processes using question prompts in a web-based learning environment. Journal of Educational Computing Research, 33(2), 219–248.CrossRefGoogle Scholar
  21. Ge, X., & Land, S. M. (2003). Scaffolding students’ problem-solving processes in an ill-structured task using question prompts and peer interactions. Educational Technology Research and Development, 51(1), 21–38.CrossRefGoogle Scholar
  22. Ge, X., & Land, S. M. (2004). A conceptual framework for scaffolding III-structured problem-solving processes using question prompts and peer interactions. Educational Technology Research and Development, 52(2), 5–22.CrossRefGoogle Scholar
  23. Ge, X., Law, V., & Huang, K. (2016). Detangling the interrelationships between self-regulation and ill-structured problem solving in problem-based learning. Interdisciplinary Journal of Problem-Based Learning, 10(2), 11.CrossRefGoogle Scholar
  24. Griffin, C. C., Dana, N. F., Pape, S. J., Algina, J., Bae, J., Prosser, S. K., & League, M. B. (2018). Prime online: Exploring teacher professional development for creating inclusive elementary mathematics classrooms. Teacher Education and Special Education, 41(2), 121–139.CrossRefGoogle Scholar
  25. Gunawardena, C. N., Lowe, C. A., & Anderson, T. (1997). Analysis of a global online debate and the development of an interaction analysis model for examining social construction of knowledge in computer conferencing. Journal of educational computing research, 17(4), 397–431.CrossRefGoogle Scholar
  26. Hannafin, M., Land, S., & Oliver, K. (1999). Open learning environments: Foundations, methods, and models. In C. M. Reigeluth (Ed.), Instructional-design theories and models, Vol. 2: A new paradigm of instructional theory (pp. 115–140). Mahwah, NJ: Erlbaum.Google Scholar
  27. Jonassen, D. H. (2014). Assessing problem solving. In Handbook of research on educational communications and technology (pp. 269–288). New York: Springer.CrossRefGoogle Scholar
  28. Jonassen, D. H., & Carr, C. S. (2000). Mindtools: Affording multiple knowledge representations for learning. Computers as Cognitive Tools, 2, 165–196.Google Scholar
  29. Jonassen, D. H. (1999). Designing constructivist learning environments. Instructional design theories and models: A new paradigm of instructional theory, 2, 215-239.Google Scholar
  30. Jonassen, D. H., Peck, K. L., & Wilson, B. G. (1999). Learning with technology: A constructivist perspective. Upper Saddle River, NJ: Prentice Hall.Google Scholar
  31. Koh, J. H. L., Chai, C. S., & Lim, W. Y. (2017). Teacher professional development for TPACK-21CL: Effects on teacher ICT integration and student outcomes. Journal of Educational Computing Research, 55(2), 172–196.CrossRefGoogle Scholar
  32. Krug, K., Love, J., Mauzey, E., & Dixon, W. (2015). Problem solving ability confidence levels among student teachers after a semester in the classroom. College Student Journal, 49(3), 331–340.Google Scholar
  33. Land, S. M. (2000). Cognitive requirements for learning with open-ended learning environment. Educational Technology Research & Development, 48(3), 61–79.CrossRefGoogle Scholar
  34. Land, S. M., & Hannafin, M. J. (1996). A conceptual framework for the development of theories-in-action with open-ended learning environment. Educational Technology Research & Development, 44(3), 37–53.CrossRefGoogle Scholar
  35. Land, S. M., & Hannafin, M. J. (2000). Student-centered learning environment. In D. H. Jonassen & S. M. Land (Eds.), Theoretical foundations of learning environments (pp. 1–23). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  36. Lindeman, E. C. (1961). The meaning of adult education. Canada: Harvest House Ltd.Google Scholar
  37. McFadden, J., Ellis, J., Anwar, T., & Roehrig, G. (2014). Beginning science teachers’ use of a digital video annotation tool to promote reflective practices. Journal of Science Education and Technology, 23(3), 458–470.CrossRefGoogle Scholar
  38. McVay, G. J., Murphy, P. R., & Yoon, S. W. (2008). Good practice in accounting education: Classroom configuration and technological tools for enhancing the learning environment. Accounting Education: An International Journal, 17(1), 41–63.CrossRefGoogle Scholar
  39. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017.CrossRefGoogle Scholar
  40. Poon, C. L., Tan, S., Cheah, H. M., Lim, P. Y., & Ng, H. L. (2015). Student and teacher responses to collaborative problem solving and learning through digital networks in Singapore. In Assessment and teaching of 21st century skills (pp. 199–212). Dordrecht, The Netherlands: Springer.Google Scholar
  41. Schreier, M. (2014). Qualitative content analysis. In The SAGE handbook of qualitative data analysis (pp. 170–183).Google Scholar
  42. Smith, J. B. (1994). Collective intelligence in computer-based collaboration. CRC Press.Google Scholar
  43. Van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. (1994). The think aloud method—A practical guide to modeling cognitive processes. San Diego, CA: Academic.Google Scholar
  44. Wang, S. K., Hsu, H. Y., Reeves, T. C., & Coster, D. C. (2014). Professional development to enhance teachers’ practices in using information and communication technologies (ICTs) as cognitive tools: Lessons learned from a design-based research study. Computers & Education, 79, 101–115.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Hui-Chen Durley
    • 1
  • Xun Ge
    • 2
  1. 1.Oklahoma City Public SchoolsOklahoma CityUSA
  2. 2.University of OklahomaNormanUSA

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