Project Summary The purpose of this study is to investigate computer self-efficacy as a possible barrier to Cooperative Extension employees completing online non-credit technical training courses. Computer self-efficacy is a motivational construct that concerns students' perceptions of their computer skills and ability to perform computer-related tasks (Cassidy and Eachus, 2002). In an online survey, students in an online Adobe Contribute Fundamentals class (beginning web editing) who agreed to participate in the study were asked a series of background questions and computer self-efficacy questions. A post-course reflective questionnaire was administered to students participating in the study after the course end date. Even if participants did not't complete the course they were asked to submit their answers. The questionnaire asked students to reflect upon the course and their experience as an online learner. Pre- and post-course questionnaire data will be analyzed to measure computer self-efficacy and how it relates to course completion rates. Pre-course questionnaire This is the survey that was administered to participants prior to the start of the online Adobe Contribute course. This survey asks a series of background and computer self-efficacy questions. Self-efficacy questions based on Computer User Self-Efficacy Scale (Cassidy & Eachus, 2002). Post-course questionnaire This questionnaire asked participants to reflect upon the course and their experiences as online learners (this is mostly to gain helpful insights into the instructional design/delivery process). Adapted from Student Distance Education Survey (Texas A&M Kingsville, http://education.tamuk.edu/techsurvey/student_distance_education_survey.htm)

Annotated List of Helpful Resources & References Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191-215. Bandura, A. (1986). Social foundations of thought and action. Englewood, CA: Prentice Hall. Cassidy, S and Eachus, P. (2002). Developing the Computer User Self-Efficacy (CSUE) Scale: Investigating the Relationship Between Computer Self-Efficacy, Gender and Experience with Computers. Educational Computing Research, 26(2), 133-153. Compeau, D., and Higgins, C. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly, June, 189-211. Derouin, R., Fritzsche, B., and Salas, E. (2005). E-Learning in Organizations. Journal of Management, December, 31(6), 920-940. Gist, M.E., C. Schwoerer, and B. Rosen. (1989). Effects of alternative training methods on self-efficacy and performance in computer software-training. Journal of Applied Psychology, 74 (6): 884-91. Lim, C. (2001). Computer Self-Efficacy, Academic Self-Concept, and Other Predictors of Satisfaction and Future Participation of Adult Distance Learners. The American Journal of Distance Education, 15, 2, 41-51. Mungania, P, and Reio, T.G., Jr. (2005). If E-Learners Get There, Will They Stay? The Role of E-Learning Self-Efficacy. Online Submission, Paper presented at the Academy of Human Resource Development International Conference (AHRD) (Estes Park, CO, Feb 24-27, 2005) p1110-1117 (Symp. 48-2). Welsh, E.T., Wanberg, C.R., Brown, E.G., & Simmering, M.J. (2003). E-Learning: Emerging uses, empirical results and future directions. International Journal of Training and Developoment, 7:245-258.

Career Relevance & Impact A world of knowledge and research has opened up to me in the last year as I've participated in the Wisconsin Teaching Scholars program. While working on this research project, one question leads to the next and I find myself being much more thoughtful and engaged in my teaching and in my instructional design. Problems or concerns in my teaching have truly become opportunities for professional growth.

The Problem In 1999, Cooperative Extension Technology Services started offering online courses for end-user software training for Extension staff and faculty located in 72 Wisconsin counties. Prior to that time, considerable travel time and expense was required for course participants and instructors to attend classroom-based courses in Madison or a few other locations throughout the state. In recent years we've experienced a very rapid adoption of e-learning. While we continue to offer some traditional classroom-based courses and web conferences, the majority of our end-user software training is now conducted online using a course management system called Desire2Learn. In Cooperative Extension, the use of online courses for professional development has outpaced academic research on the topic. While our use of Desire2Learn has rapidly increased in recent years, very little research has been done on teaching and learning in our internal online environment. The high rate drop-out rate in these courses (average 50 percent) has been frustrating but generally accepted as the norm. In the e-learning trade publications drop out rates reportedly range from 20-70 percent; however, it's challenging to track down research and statistics on this issue where it pertains to non-academic environments. I'm experiencing a high drop-out rate in my online courses (50-60 percent) and course evaluation data has not provided adequate insight into the cause of this problem. This study is the first small step towards researching what's going on within Cooperative Extension's online learning environment in an effort to reduce the number of staff and faculty who drop out of their online professional development courses. If computer self-efficacy is a barrier for our course participants, and if learners with low computer self-efficacy can be identified at the start of a course, then appropriate modifications to the course design could be made, interventions with those learners could be set in motion, and higher learning outcomes could be achieved.

Preliminary Findings, Results, Conclusions, & Implications Pre- and Post-course survey data are being analyzed. Of the 30 people registered for the course, 15 people finished (50 percent). 14 out of the original 30 enrolled agreed to participate in the study, and of those 10 completed the course (71 percent). Computer self-efficacy scores have been calculated for the 14 study participants (see chart below). The preliminary results seem to show that low computer self-efficacy scores may indeed have been a barrier to participants completing this online course; however, participants with the lowest computer self-efficacy scores still finished the course and some participants with fairly high computer self-efficacy scores dropped out. It's likely that other factors influenced persistence rates as well and I hope to find some trends in the post-course questionnaire data.

Pre-course computer self-efficacy scores

"While addressing barriers is important, it is even more pertinent to find ways to reduce or eliminate them altogether. The implication...is to consistently seek solution to the barriers that e-learners encounter." (Mungania and Reio, 2005) Bandura (1986) noted that high self-efficacy alone is not enough to make people perform certain activities. The preliminary data of this study indicate that even participants with high computer self-efficacy won’t necessarily persist in an online course. In the next phrase of this study I will explore other possible barriers faced by Cooperative Extension staff and faculty engaged in e-learning such as: Motivation (internal & external) Problems with technology Learning preferences/styles Learning environment Level of support from organization