Computer Science – Performance
Scientific paper
Jan 2011
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011aas...21734601r&link_type=abstract
American Astronomical Society, AAS Meeting #217, #346.01; Bulletin of the American Astronomical Society, Vol. 43, 2011
Computer Science
Performance
Scientific paper
A common problem frequently encountered in large introductory classes is the anonymity experienced by students. An effort is underway at Ball State University to explore the impact of technology on reducing this anonymity and improving student performance and success. In preparation for this study, performance and success measures for students in a previous class have been examined to provide background for construction of a model for formal testing and a control group for comparison of future results. Student performance measures obtained early in the course and final course grades were examined to identify potential early warning indicators that might be used to plan interventions much earlier than the traditional midterm course reports used to alert freshmen at academic risk. Class participation scores were based on data obtained with a personal response system (i>clicker). The scores were scaled to reflect about 80% comprehension and 20% attendance. Homework scores were obtained using the LON-CAPA Course Management System and instructional materials created by the author. Substantial linear correlations exist between 1) Exam 1 Scores after Four Weeks and 2) Raw Class Participation Scores for the First Six Weeks and the Final Course Score. A more modest linear correlation was found between 3) Homework Scores for First Six Weeks and Final Course Score. Of these three measures, only Class Participation Scores identified all students who ultimately received course grades lower than C. Several students scored in the danger zone according to Homework and Class Participation Scores but earned course grades of C or better. It appears that an early warning plan based on Class Participation Scores would permit effective identification of at-risk students early in the course.
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