Predicting performance in an introductory computer science course
Communications of the ACM
Predicting student performance in a beginning computer science class
SIGCSE '86 Proceedings of the seventeenth SIGCSE technical symposium on Computer science education
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Computer managed, open question, open book assessment
Proceedings of the 2nd conference on Integrating technology into computer science education
Using artificial neural nets to predict academic performance
SAC '96 Proceedings of the 1996 ACM symposium on Applied Computing
Contributing to success in an introductory computer science course: a study of twelve factors
Proceedings of the thirty-second SIGCSE technical symposium on Computer Science Education
Grade and ability predictions in an introductory programming course
ACM SIGCSE Bulletin
SIGCPR '02 Proceedings of the 2002 ACM SIGCPR conference on Computer personnel research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A placement examination for computer science II
ACM SIGCSE Bulletin
Proceedings of the 35th conference on Winter simulation: driving innovation
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There is a limit on the amount of time a faculty member may devote to each student. As a consequence, a faculty member must quickly determine which student needs more attention than others throughout a semester. One of the most demanding courses in the CS curriculum is a data structures course. This course has a tendency for high drop rates at our university. A pre-assessment exam is developed for the data structures class in order to provide feedback to both faculty and students. This exam helps students determine how well prepared they are for the course. In order to determine a student's chance of success in this course, a Genetic Program-based experiment is constructed based upon the pre-assessment exam. The result is a model that produces an average accuracy of 79 percent.