Can undergraduates learn programming with a "Virtual Professor"? Findings from a pilot implementation of a blended instructional strategy

Dan Cernusca, Clayton Price
2013 ASEE Annual Conference & Exposition Proceedings   unpublished
Missouri -Columbia. He also holds a B.S. and a Ph.D. from the University of Sibiu, Romania with a specialization in manufacturing technologies and respectively cutting-tools design. His research interests include Design-Based Research in technology-enabled learning contexts, technology-mediated problem solving, applications of dynamic modeling for learning of complex topics, and the impact of epistemic beliefs on learning with technology. Prof. Clayton E Price, Missouri University of Science
more » ... Technology Professor Price has varied interests in the sciences, having earned degrees in geology/geophysics, mathematics, and computer science. He has taught at S&T for 32 years, currently in the computer science department. He teaches introductory programming classes in C++ and the numerical analysis courses. As assistant to the chairman, he advises freshmen and transfer students. Price's interests center on his teaching and investigating new paradigms for delivering a university education. He has recently developed on-line material for his core programming course, and hopes to expand this effort in the future. ABSTRACT This study presents the main findings from the pilot implementation of a blended instructional strategy in one section of a multi-section course of introduction to programming with C++. The implemented strategy blended pre-recorded online lectures and homework assignments, with one weekly optional face-to-face meeting. The same instructor taught both the blended instruction and the traditional face-to-face lecture. The focus of this study was twofold: a) determine potential negative impact of the blended format, and b) identify the major predictors of final performance in this course. A one-way ANOVA analysis indicated no statistically significant differences in final course score between the control and the treatment groups. The analysis of a proposed path analysis model showed that self-efficacy, perceived engagement and perceived difficulty are significant predictors of students' final performance in the course.
doi:10.18260/1-2--19282 fatcat:xho3j4lh3zh53caxdr5zetu4di