TY - GEN
T1 - Modeling student learning outcomes in studying programming language course
AU - Wang, Shanshan
AU - Han, Yong
AU - Wu, Wenjun
AU - Hu, Zhenghui
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/11
Y1 - 2017/5/11
N2 - Learning outcome assessment is of great significance in the field of traditional on-campus teaching especially on the courses of programming languages. In this work, we take advantage of the data offered by our programming assignment judge system and propose a new IRT-BKT model for estimation of learning outcome. This new framework: Item Response Theory (IRT) model that estimates students' initial knowledge status, and joins it with the discrimination and difficulty of each skill estimated to evaluate the probability of knowing a skill before training it. We then estimate parameters learn, guess, and slip probabilities of Bayesian Knowledge Tracing (BKT) Model. Using real data, we show that IRT-BKT model outperforms Item Response Theory and Bayesian Knowledge Tracing in terms of prediction accuracy.
AB - Learning outcome assessment is of great significance in the field of traditional on-campus teaching especially on the courses of programming languages. In this work, we take advantage of the data offered by our programming assignment judge system and propose a new IRT-BKT model for estimation of learning outcome. This new framework: Item Response Theory (IRT) model that estimates students' initial knowledge status, and joins it with the discrimination and difficulty of each skill estimated to evaluate the probability of knowing a skill before training it. We then estimate parameters learn, guess, and slip probabilities of Bayesian Knowledge Tracing (BKT) Model. Using real data, we show that IRT-BKT model outperforms Item Response Theory and Bayesian Knowledge Tracing in terms of prediction accuracy.
KW - Bayesian Knowledge Tracing
KW - Item Response Theory
KW - Learning Outcome
KW - Programming Language
UR - https://www.scopus.com/pages/publications/85020233458
U2 - 10.1109/ICIST.2017.7926768
DO - 10.1109/ICIST.2017.7926768
M3 - 会议稿件
AN - SCOPUS:85020233458
T3 - 7th International Conference on Information Science and Technology, ICIST 2017 - Proceedings
SP - 263
EP - 270
BT - 7th International Conference on Information Science and Technology, ICIST 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Information Science and Technology, ICIST 2017
Y2 - 16 April 2017 through 19 April 2017
ER -