TY - GEN
T1 - Native language’s effect on Java compiler errors
AU - Reestman, Kyle
AU - Dorn, Brian
N1 - Publisher Copyright:
© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2019/7/30
Y1 - 2019/7/30
N2 - Novice student compilation behaviors are well documented by prior research, but those findings are derived from instructional contexts that are largely in English-dominant locales. Speakers of languages other than English face unique challenges while learning to program like partial localization of applications and programming language syntax based on English-only keywords. This study examines compiler errors of novice programming students from different native language backgrounds to explore potential differences in their error distributions relative to those in English dominant contexts. For example, it is plausible that students from non-English language backgrounds would experience more “unknown identifier” types of errors while programming with English keywords and API methods. Using data from the BlueJ Blackbox database, we analyzed error distributions for users based on country and language group characteristics. Statistical analysis showed a statistically significant difference in error distributions between native language groups; however, effect sizes were very weak indicating that the differences have little practical significance in terms of guiding either language or instructional design. However, these results may support drawing broader inferences from earlier Java compilation behavior studies to global contexts.
AB - Novice student compilation behaviors are well documented by prior research, but those findings are derived from instructional contexts that are largely in English-dominant locales. Speakers of languages other than English face unique challenges while learning to program like partial localization of applications and programming language syntax based on English-only keywords. This study examines compiler errors of novice programming students from different native language backgrounds to explore potential differences in their error distributions relative to those in English dominant contexts. For example, it is plausible that students from non-English language backgrounds would experience more “unknown identifier” types of errors while programming with English keywords and API methods. Using data from the BlueJ Blackbox database, we analyzed error distributions for users based on country and language group characteristics. Statistical analysis showed a statistically significant difference in error distributions between native language groups; however, effect sizes were very weak indicating that the differences have little practical significance in terms of guiding either language or instructional design. However, these results may support drawing broader inferences from earlier Java compilation behavior studies to global contexts.
KW - Blackbox
KW - Compiler error behavior
KW - English as a second language
UR - http://www.scopus.com/inward/record.url?scp=85071320517&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071320517&partnerID=8YFLogxK
U2 - 10.1145/3291279.3339423
DO - 10.1145/3291279.3339423
M3 - Conference contribution
AN - SCOPUS:85071320517
T3 - ICER 2019 - Proceedings of the 2019 ACM Conference on International Computing Education Research
SP - 249
EP - 257
BT - ICER 2019 - Proceedings of the 2019 ACM Conference on International Computing Education Research
PB - Association for Computing Machinery, Inc
T2 - 15th Annual International Computing Education Research Conference, ICER 2019
Y2 - 12 August 2019 through 14 August 2019
ER -