You are here:

Using an Intelligent Web Browser for Teacher Professional Development: Preliminary Findings from Simulated Learners
PROCEEDING

, , University of Utah, United States

Society for Information Technology & Teacher Education International Conference, in Savannah, GA, United States ISBN 978-1-939797-13-1 Publisher: Association for the Advancement of Computing in Education (AACE), Chesapeake, VA

Abstract

Open-ended learning environments that leverage online resources hold a wealth of information for pre-service teachers to acquire the knowledge that is necessary to successfully implement technologies in the classroom. Effective learning in such environments require that teachers engage in self-regulated learning processes. This often proves to be difficult, and can lead to poor learning outcomes. In this paper, we briefly outline the theoretical and analytical underpinnings of nBrowser, an intelligent web browser that trains and fosters pre-service teachers’ ability to regulate certain aspects of their own learning while building lesson plans that integrate technologies into the classroom. In doing so, we provide preliminary findings from a 100 simulated learners conducted with nSimulator to test the underlying assumption of the network-based model, which allows the system to appraise learner behaviors and prescribe instructional content.

Citation

Poitras, E. & Fazeli, N. (2016). Using an Intelligent Web Browser for Teacher Professional Development: Preliminary Findings from Simulated Learners. In G. Chamblee & L. Langub (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 3037-3041). Savannah, GA, United States: Association for the Advancement of Computing in Education (AACE). Retrieved December 15, 2018 from .

View References & Citations Map

References

  1. Azevedo, R. (2008). The role of self-regulation in learning about science with hypermedia. In D. Robinson& G. Schraw (Eds.), Recent innovations in educational technology that facilitate student learning (pp. 127-156).
  2. Biswas, G., Kinnebrew, J.S., & Mack, D.L.C. (2013). How do students’ learning behaviors evolve in scaffolded open-ended learning environments? Proceedings of the 21st International Conference on Computers in Education. Bali, Indonesia.
  3. Kramarski, B., & Michalsky, T. (2009). Investigating preservice teachers' professional growth in self-regulated learning environments. Journal of Educational Psychology, 101(1), 161.
  4. Kramarski, B., & Michalsky, T. (2010). Preparing preservice teachers for self-regulated learning in the context of technological pedagogical content knowledge. Learning and Instruction, 20, 434-447.
  5. Lajoie, S.P., & Azevedo, R. (2006). Teaching and learning in technology-rich environments. In P. Alexander& P. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 803-821). Mahwah, NJ: Erlbaum.
  6. Greene, J.A., & Azevedo, R. (2007). A theoretical review of Winne and Hadwin’s model of self-regulated learning: New perspectives and directions. Review of Educational Research, 77(3), 334-372.
  7. McCalla, G., & Champaign, J. (2013). Simulated learners. Intelligent Systems, IEEE, 28(4), 67-71.
  8. Winne, P., & Hadwin, A. (2008). The weave of motivation and self-regulated learning. In D. Schunk, & B. Zimmerman (Eds.), Motivation and self-regulated learning: Theory, research, and applications (pp. 297-314).

These references have been extracted automatically and may have some errors. If you see a mistake in the references above, please contact info@learntechlib.org.

Presentation

Slides w/Audio View

Slides

Also Read

Related Collections