TY - GEN
T1 - A Random Forest Algorithm for Predicting Computer Programming Skill Associated with Learning Styles
AU - Anistyasari, Yeni
AU - Hidayati, Shintami C.
AU - Harimurti, Rina
AU - Ekohariadi,
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The complexity of programming concepts such variables, loops, arrays, and functions contribute roadblocks for students learning to code. Predicting computer programming skills using machine learning is commonplace. It enables early identification of student at risk of programming failure and prompt implementation of successful early intervention strategies. Artificial intelligence relies to acquire information and rules from complicated data in order to foresee outcomes and patterns in behavior. In contrast to statistical approaches, machine learning seeks to improve prediction performance by making more accurate forecasts. Hence, we set out to investigate, using machine learning techniques, partially the Random Forest Algorithm's (RFA) to predict vocational high school students' proficiency in computer programming. The outcomes indicated a pass prediction of 90.23 percent, a failure prediction of 55 percent, an overall accuracy of 88.79 percent, and a total performance indicator of 91 percent across all classification cutoffs.
AB - The complexity of programming concepts such variables, loops, arrays, and functions contribute roadblocks for students learning to code. Predicting computer programming skills using machine learning is commonplace. It enables early identification of student at risk of programming failure and prompt implementation of successful early intervention strategies. Artificial intelligence relies to acquire information and rules from complicated data in order to foresee outcomes and patterns in behavior. In contrast to statistical approaches, machine learning seeks to improve prediction performance by making more accurate forecasts. Hence, we set out to investigate, using machine learning techniques, partially the Random Forest Algorithm's (RFA) to predict vocational high school students' proficiency in computer programming. The outcomes indicated a pass prediction of 90.23 percent, a failure prediction of 55 percent, an overall accuracy of 88.79 percent, and a total performance indicator of 91 percent across all classification cutoffs.
KW - computer programming skill
KW - learning styles
KW - predictive analysis
KW - random forest algorithm
UR - http://www.scopus.com/inward/record.url?scp=85182026164&partnerID=8YFLogxK
U2 - 10.1109/ICVEE59738.2023.10348199
DO - 10.1109/ICVEE59738.2023.10348199
M3 - Conference contribution
AN - SCOPUS:85182026164
T3 - 2023 6th International Conference on Vocational Education and Electrical Engineering: Integrating Scalable Digital Connectivity, Intelligence Systems, and Green Technology for Education and Sustainable Community Development, ICVEE 2023 - Proceeding
SP - 162
EP - 166
BT - 2023 6th International Conference on Vocational Education and Electrical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Vocational Education and Electrical Engineering, ICVEE 2023
Y2 - 14 October 2023 through 15 October 2023
ER -
