Advertisement

The Evolving Role of Attitudes and Competencies in Information and Communication Technology in Education

  • Gerald Knezek
  • Rhonda Christensen
Reference work entry
Part of the Springer International Handbooks of Education book series (SIHE)

Abstract

Attitudes and competencies related to ICT in education have evolved over the past decade from being viewed as separate but related entities to now being viewed as part of an integrated whole. As of 2018 the prevailing view of competencies relevant to teaching and learning with technology is that they often span the cognitive, affective, and psychomotor domains of psychology and are best appraised by concepts such as self-efficacy that lie at the intersection of two or more of these domains. New developments in social media technologies stretch the limits of relevance of psychology as pertaining to the behavior of an individual and move into the realm of sociology, or behaviors of groups of individuals. Noncognitive variables beyond attitudes have assumed a more prominent role in ICT in education, as of the second decade of the twenty-first century. Learning sciences is proposed as one interdisciplinary field holding promise for integrating knowledge and wisdom to chart the best paths forward, contributing to the continual refinement of best pedagogical practices for teaching and learning with technology.

Keywords

Attitudes Competencies Domains of psychology Learning sciences Teaching and learning with technology 

References

  1. Albion, P. (2001). Some factors in the development of self-efficacy beliefs for computer use among teacher education students. Journal of Technology and Teacher Education, 9(3), 321–347.Google Scholar
  2. Allen, J. G., Wasicsko, M. M., & Chirichello, M. (2014). The missing link: Teaching the dispositions to lead. International Journal of Educational Leadership Preparation, 9(1).Google Scholar
  3. Anderson, S. E., & Maninger, R. M. (2007). Preservice teachers’ abilities, beliefs, and intentions regarding technology integration. Journal of Educational Computing Research, 37(2), 151–172.CrossRefGoogle Scholar
  4. Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., Raths, J., & Wittrock, M. C. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York: Pearson, Allyn & Bacon.Google Scholar
  5. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs: Prentice Hall.Google Scholar
  6. Bandura, A. (1993). Perceived self-efficacy in cognitive development and functioning. Educational Psychologist, 28(2), 117–148.CrossRefGoogle Scholar
  7. Bloom, B. S., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of educational objectives, handbook I: The cognitive domain. New York: David McKay Co Inc.Google Scholar
  8. Boston College Libraries. (2017). Technology tools for lesson plans: Blooms taxonomy & technology integration. https://libguides.bc.edu/c.php?g=628962&p=4506921.
  9. Cassidy, S., & Eachus, P. (2002). Developing the computer user self-efficacy (CUSE) scale: Investigating the relationship between computer self-efficacy, gender and experience with computers. Journal of Educational Computing Research, 26(2), 169–189.CrossRefGoogle Scholar
  10. Christensen, R. (2002). Effects of technology integration education on the attitudes of teachers and students. Journal of Research on Technology in Education, 34(4), 411–433.CrossRefGoogle Scholar
  11. Christensen, R., & Knezek, G. (2017). Validating the technology proficiency self assessment for 21st century learning (TPSA C21) instrument. Journal of Digital Learning in Teacher Education, 33(1), 20–31.  https://doi.org/10.1080/21532974.2016.1242391.CrossRefGoogle Scholar
  12. Clark, D. (2015). Blooms taxonomy of learning domains. Available from http://www.nwlink.com/~donclark/hrd/bloom.html#process_levels_knowledge.
  13. Creswell, J. W. (2002). Research design: Qualitative, quantitative, and mixed methods approaches (3rd ed.). Los Angeles: Sage.Google Scholar
  14. Duckworth, A., & Yeager, D. S. (2015). Measurement matters: Assessing personal qualities other than cognitive ability for educational purposes. Educational Researcher, 44(4), 237–251.  https://doi.org/10.3102/0013189X15584327.CrossRefGoogle Scholar
  15. Ertmer, P. A. (1999). Addressing first-and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47–61.CrossRefGoogle Scholar
  16. Ertmer, P. A., Ottenbreit-Leftwich, A. T., Sadik, O., Sendurer, E., & Sendurer, P. (2012). Teacher beliefs and technology integration practices: A critical relationship. Computers & Education, 59(2), 423–435.CrossRefGoogle Scholar
  17. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior. Reading: Addison-Wesley.Google Scholar
  18. Gencturk, E., Gokcek, T., & Gunes, G. (2010). Reliability and validity study of the technology proficiency self-assessment scale. Procedia Social and Behavioral Sciences, 2, 2863–2867.CrossRefGoogle Scholar
  19. Henriques, G. R. (2004). Psychology defined. Journal of Clinical Psychology, 60(12), 1207–1221.CrossRefGoogle Scholar
  20. Hoy, A. W., Hoy, W. K., & Davis, H. A. (2009). Teachers’ self-efficacy beliefs. In K. Wentzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 627–654). New York: Routledge.Google Scholar
  21. Jonassen, D. H. (2000). Computers as mindtools for schools: Engaging critical thinking (2nd ed.). Columbus: Merrill.Google Scholar
  22. Jones, A., & Issroff, K. (2005). Learning technologies: Affective and social issues in computer-supported collaborative learning. Computers & Education, 44(4), 395–408.CrossRefGoogle Scholar
  23. Keengwe, J., Onchwari, G., & Wachira, P. (2008). The use of computer tools to support meaningful learning. AACE Journal, 16(1), 77–92.Google Scholar
  24. Kim, C., Kim, M. K., Lee, C., Spector, J. M., & DeMeester, K. (2013). Teacher beliefs and technology integration. Teaching and Teacher Education, 29, 76–85.CrossRefGoogle Scholar
  25. Knezek, G., & Christensen, R. (2008). The importance of information technology attitudes and competencies in primary and secondary education. In J. Voogt & G. Knezek (Eds.), International handbook of information technology in primary and secondary education (pp. 321–328). New York: Springer New York.Google Scholar
  26. Koh, J., & Frick, T. (2009). Instructor and student classroom interactions during technology skills instruction for facilitating preservice teachers’ computer self-efficacy. Journal of Educational Computing Research, 40, 221–228.CrossRefGoogle Scholar
  27. Krathwohl, D. R., Bloom, B. S., & Masia, B. B. (1973). Taxonomy of educational objectives, the classification of educational goals. Handbook II: Affective domain. New York: David McKay Co Inc.Google Scholar
  28. Lei, J. (2009). Digital natives as preservice teachers: What technology preparation is needed? Journal of Computing in Teacher Education, 25(3), 87–97.Google Scholar
  29. Liu, S. H. (2011). Factors related to pedagogical beliefs of teachers and technology integration. Computers & Education, 56, 1012–1022.CrossRefGoogle Scholar
  30. Meirink, J. A., Meijer, P. C., Verloop, N., & Bergen, T. C. M. (2009). Understanding teacher learning in secondary education: The relations of teacher activities to changed beliefs about teaching and learning. Teaching and Teacher Education, 25(1), 89–100.CrossRefGoogle Scholar
  31. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054.  https://doi.org/10.1111/j.1467-9620.2006.00684.x.CrossRefGoogle Scholar
  32. Morshead, R. W. (1965). Taxonomy of educational objectives handbook II: Affective domain. Studies in Philosophy in Education, 4(1), 164–170.CrossRefGoogle Scholar
  33. Niederhauser, D., & Stoddart, T. (2001). Teachers’ instructional perspectives and use of educational software. Teaching and Teacher Education, 17, 15–31.CrossRefGoogle Scholar
  34. Pajares, M. F. (2002). Overview of social cognitive theory and of self-efficacy. Retrieved 20 Mar 2015 from http://www.emory.edu/EDUCATION/mfp/eff.html.
  35. Richardson, W. (2013). Students first, not stuff. Educational Leadership, 70(6), 10–14.Google Scholar
  36. Rogers, P. L. (2000). Barriers to adopting emerging technologies in education. Journal of Educational Computing Research, 22(4), 455–472.CrossRefGoogle Scholar
  37. Roschelle, J., Grover, S., & Kolodner, J. (2014). CIRCL primer: Learning sciences. In CIRCL Primer Series. Retrieved from http://circlcenter.org/learning-sciences/.
  38. Sang, G., Valcke, M., van Braak, J., & Tondeur, J. (2010). Student teachers’ thinking processes and ICT integration: Predictors of prospective teaching behaviors and educational technology. Computers & Education, 54(1), 103–112.CrossRefGoogle Scholar
  39. Sawyer, R. K. (2005). The new science of learning. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  40. Scherr, L. (2006). Writing effective student learning outcomes: Part I. Bloom’s taxonomy, cognitive, psychomotor, and affective domains. Available from http://www.mccc.edu/~virtcoll/DVK/VIC101/writing_learning_objectives.pdf.
  41. Schrum, L., & Levin, B. B. (2016). Educational technologies and twenty-first century leadership for learning. International Journal of Leadership in Education, 19(1), 17–39.CrossRefGoogle Scholar
  42. Sedlacek, W. E. (2011). Using noncognitive variables in assessing readiness for higher education. Readings on Equal Education, 25, 187–205.Google Scholar
  43. Shechtman, N., DeBarger, A. H., Dornsife, C., Rosier, S., & Yarnall, L. (2013). Promoting grit, tenacity, and perseverance: Critical factors for success in the 21st century. Menlo Park: Center for Technology in Learning, SRI International.Google Scholar
  44. Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4–14.CrossRefGoogle Scholar
  45. Sneed, O. (2016, May 9). Integrating technology with Bloom’s taxonomy. TeachOnline. Arizona State University. https://teachonline.asu.edu/2016/05/integrating-technology-blooms-taxonomy/#more-3357.
  46. Stoeckl, S. (2016). Five reasons why the 2016 ISTE standards for students matter. https://www.iste.org/explore/articleDetail?articleid=685. 13 July 2016.
  47. Vygotsky, L. S. (1978). Mind in society. Cambridge, MA: Harvard University Press.Google Scholar
  48. Ward, L., & Parr, J. M. (2010). Revisiting and reframing use: Implications for the integration of ICT. Computers and Education, 54(1), 113–122.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of North TexasDentonUSA

Section editors and affiliations

  • Gerald Knezek
    • 1
  • Rhonda Christensen
    • 2
  1. 1.University of North TexasDentonUSA
  2. 2.University of North TexasDentonUSA

Personalised recommendations