CE  Vol.5 No.2 , February 2014
The Pedagogical Implications of Implementing New Technologies to Enhance Student Engagement and Learning Outcomes
Abstract: This paper reports the findings of research conducted by three teacher educators about the effects on teaching and learning from implementing a variety of digital technologies in their undergraduate courses. The aim of this study was to assess the degree to which certain university supported digital technologies assisted in promoting student engagement and participation in collaborative learning. The data are based on the semester long experiences of the three lecturers and their students. From this data emerged an holistic picture that highlights which of the implemented digital technologies constrains or enables particular pedagogical aspects such as communication of course requirements; student engagement, meaningful formative feedback; and deep connections between course elements. This picture assisted the authors in generating a matrix for implementing certain digital technologies that cater for diverse learning styles, and diversely experience an interest in using technology. The theoretical framework for building the matrix is based on Collins, Brown and Newman’s (1990) Cognitive Apprenticeship Model. It is also underpinned by the suggestions that as “teachers” we too often overlook whether or not our students have the requisite skills to engage with technologies because of tacit assumptions about how this generation of students wants to learn. Likewise, the same can be said of those who provide professional development sessions for staff who are learning how to use new technologies and who often appear to make similar assumptions.
Cite this paper: Sammel, A. , Weir, K. & Klopper, C. (2014). The Pedagogical Implications of Implementing New Technologies to Enhance Student Engagement and Learning Outcomes. Creative Education, 5, 104-113. doi: 10.4236/ce.2014.52017.

[1]   Arbaugh, J. B. (2008). Does the community of inquiry framework predict outcomes in online MBA courses? International Review of Research in Open and Distance Learning, 9.

[2]   Boston, W., Diaz, S. R., Gibson, A. M., Ice, P., Richardson, J., & Swan, K. (2009). An exploration of the relationship between indicators of the Community of Inquiry framework and retention in online programs. Journal of Asynchronous Learning Networks, 13, 67-83.

[3]   Brown, A. L. (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. Journal of the Learning Sciences, 2, 141-178.

[4]   Brown, A. L., & Campione, J. C. (1996). Psychological theory and the design of innovative learning environments: On procedures, principles, and systems. In R. Glaser (Ed.), Innovations in learning: New environments for education (pp. 289-325). Mahwah, NJ: Erlbaum.

[5]   Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18, 32-41.

[6]   Collins, A. (1992). Toward a design science of education. In E. Scanlon, & T. O’Shea (Eds.), New directions in educational technology (pp. 15-22). New York: Springer-Verlag.

[7]   Collins, A., Brown, J. S., & Newman, S. E. (1990). 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.

[8]   Felder, R. M. & Brent, R. (2005). Understanding student differences. Journal of Engineering Education, 94, 57-72.

[9]   Garrison, D. R., & Arbaugh, J. B. (2007). Researching the community of inquiry framework: Review, issues, and future directions. Internet and Higher Education, 10, 157-172.

[10]   Garrison, D. R., & Cleveland-Innes, M. (2005). Facilitating cognitive presence in online learning: Interaction is not enough. American Journal of Distance Education, 19, 133-148.

[11]   Goodhue, D., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19, 213-236.

[12]   Levy, Y. (2008). An empirical development of critical value factors (CVF) of online learning activities: An application of activity theory and cognitive value theory. Computers & Education, 51, 1664-1675.

[13]   Liaw, S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51, 864-873.

[14]   McGill, J., & Klobas, J. E. (2009). A task-technology fit view of learning management system impact. Computers & Education, 52, 496508.

[15]   Moosmayer, D. C., & Siems, F. U. (2012). Values education and student satisfaction: German business students’ perceptions of universities’ value influences. Journal of Marketing for Higher Education, 22, 257-272.

[16]   Richardson, J., & Swan, K. (2003). Examining social presence in online courses in relation to students’ perceived learning and satisfaction. Journal of Asynchronous Learning Networks, 7, 68-88.

[17]   Rubin, B., Fernandes, R., & Avgerinou, M. D. (2013). The effects of technology on the Community of Inquiry and satisfaction with online courses. Internet and Higher Education, 17, 48-57.

[18]   Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Education, 49, 396-413.

[19]   Seymour, E., & Hewitt, M. N. (1997). Talking about Leaving: Why Under-graduates leave the Sciences. Boulder, CO, Oxford: Westview Press.

[20]   Swan, K. (2002). Building learning communities in online courses: The importance of interaction. Education, Communication & Information, 2, 23-49.

[21]   The Design-Based Research Collective (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32, 5-8.

[22]   Worthen, B. R., Sanders, J. R., & Fitzpatrick, J. L. (1996). Program evaluation: Alternative approaches and practical guidelines (2nd ed.). New York: Longman.