Predicting Success in Undergraduate Parallel Programming via Probabilistic Causality Analysis

We employ probabilistic causality analysis to study the performance data of 301 students from the upper-level undergraduate parallel programming class at the University of Central Florida. To our surprise, we discover that good performance in our lower-level undergraduate programming CS-1 and CS-II classes is not a significant causal factor that contributed to good performance in our parallel programming class. On the other hand, good performance in systems classes like Operating Systems, Information Security, Computer Architecture, Object Oriented Software and Systems Software coupled with good performance in theoretical classes like Introduction to Discrete Structures, Artificial Intelligence and Discrete Structures-II are strong indicators of good performance in our upper-level undergraduate parallel programming class. We believe that such causal analysis may be useful in identifying whether parallel and distributed computing concepts have effectively penetrated the lower-level computer science classes at an institution.