Automated Analysis of Student Programmer Coding Behavior Patterns (Abstract Only)
Title | Automated Analysis of Student Programmer Coding Behavior Patterns (Abstract Only) |
Publication Type | Conference Paper |
Year of Publication | 2016 |
Authors | Ford, Corey, Staley, Clinton |
Conference Name | Proceedings of the 47th ACM Technical Symposium on Computing Science Education |
Publisher | ACM |
Conference Location | New York, NY, USA |
ISBN Number | 978-1-4503-3685-7 |
Keywords | automated evaluation, coding, educational data mining, Human Behavior, keystroke analysis, learning analytics, Metrics, online ide, pubcrawl |
Abstract | Important information regarding the learning experience and relative preparedness of Computer Science students can be obtained by analyzing their coding activity at a fine-grained level, using an online IDE that records student code editing, compiling, and testing activities down to the individual keystroke. We report results from analyses of student coding patterns using such an online IDE. In particular, we gather data from a group of students performing an assigned programming lab, using the online IDE indicated to gather statistics. We extract high-level statistics from the student data, and apply supervised learning techniques to identify those that are the most salient prediction of student success as measured by later performance in the class. We use these results to make predictions of course performance for another student group, and report on the reliability of those predictions |
URL | http://doi.acm.org/10.1145/2839509.2850540 |
DOI | 10.1145/2839509.2850540 |
Citation Key | ford_automated_2016 |