Author_Institution :
Univ. of Kansas, Lawrence, KS, USA
Abstract :
A motivational system that rewards students partly based on the higher of their scores on frequent short quizzes or on block exams has been in place for the past four years in the EE service courses at the University of Kansas. These courses are offered to majors in mechanical and aerospace engineering in spring semesters and to majors in civil, architectural, and petroleum engineering in fall semesters. Papers presented by the author at recent FIE Conferences have described this successful motivational strategy but only for two EE service courses offered once each during the academic year. The motivational model was extended in the 2009-2010 academic year with interesting results to courses for majors in electrical and computer engineering. In this paper, six different courses (sophomores to seniors) are examined to demonstrate the effects of varying parameters in the model. In addition to the levels of students, variations are examined for widely different class sizes, class times of the day, fixed or variable percentages for the higher score components, numbers and lengths of blocks per term, fixed or unannounced schedules for short quizzes, policies on excused absences from the short quizzes, and exemptions for the final exam. Comparisons for these six courses reveal a remarkably consistent and simple motivational format that can be applied to undergraduate classes: (1) Explain the system so that its advantages are clear to each student, (2) Keep it simple to avoid confusion, and (3) Meet student expectations for those taught by the same professor in previous courses.
Keywords :
educational technology; engineering education; human factors; aerospace engineering; architectural engineering; civil engineering; computer engineering; electrical engineering; excused absence policy; mechanical engineering; petroleum engineering; short quizzes; student motivational strategy; undergraduate class; Analytical models; Computational modeling; Computers; Conferences; Data analysis; Data models; Springs; EE service courses; learning outcomes; model variations; motivational strategy;