Predicting Success In Undergraduate Parallel Programming Via Probabilistic Causality Analysis

Keywords

Causality; Data analytics; Education; Parallel programming; Predictors

Abstract

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.

Publication Date

8-3-2018

Publication Title

Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2018

Number of Pages

347-352

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/IPDPSW.2018.00066

Socpus ID

85052242439 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85052242439

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