TY - GEN
T1 - Inferring How Novice Students Learn to Code
T2 - 7th CCF Academic Conference on BigData, CCF BigData 2019
AU - Liang, Yu
AU - Wu, Wenjun
AU - Wu, Lisha
AU - Wang, Meng
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2019.
PY - 2019
Y1 - 2019
N2 - Learning to code on Massive Open Online Courses (MOOCs) has become more and more popular among novice students while inferring how the students learn programming on MOOCs is a challenging task. To solve this challenge, we build a novel Intelligent Programming Tutor (IPT) which integrates the Automated Program Repair (APR) and student cognitive model. We improve an efficient APR engine, which can not only obtain repair results but also identify the types of programming errors. Based on APR, we extend the Conjunctive Factor Model (CFM) by using programming error classification as cognitive skill representation to support the student cognitive model on learning programming. We validate our IPT with the real dataset collected from a Python programming course. The results show that compared with the original CFM, our model can represent programming learning outcomes of students and predict their future performance more reliably. We also compare our student cognitive model with the state-of-the-art Deep Knowledge Tracing (DKT) model. Our model requires less training data and is higher interpretable than the DKT model.
AB - Learning to code on Massive Open Online Courses (MOOCs) has become more and more popular among novice students while inferring how the students learn programming on MOOCs is a challenging task. To solve this challenge, we build a novel Intelligent Programming Tutor (IPT) which integrates the Automated Program Repair (APR) and student cognitive model. We improve an efficient APR engine, which can not only obtain repair results but also identify the types of programming errors. Based on APR, we extend the Conjunctive Factor Model (CFM) by using programming error classification as cognitive skill representation to support the student cognitive model on learning programming. We validate our IPT with the real dataset collected from a Python programming course. The results show that compared with the original CFM, our model can represent programming learning outcomes of students and predict their future performance more reliably. We also compare our student cognitive model with the state-of-the-art Deep Knowledge Tracing (DKT) model. Our model requires less training data and is higher interpretable than the DKT model.
KW - Automated Program Repair
KW - Intelligent tutoring system
KW - Student cognitive model
UR - https://www.scopus.com/pages/publications/85076914801
U2 - 10.1007/978-981-15-1899-7_4
DO - 10.1007/978-981-15-1899-7_4
M3 - 会议稿件
AN - SCOPUS:85076914801
SN - 9789811518980
T3 - Communications in Computer and Information Science
SP - 46
EP - 56
BT - Big Data - 7th CCF Conference, BigData 2019, Proceedings
A2 - Jin, Hai
A2 - Shi, Xuanhua
A2 - Lin, Xuemin
A2 - Lin, Xuemin
A2 - Cheng, Xueqi
A2 - Xiao, Nong
A2 - Huang, Yihua
PB - Springer
Y2 - 26 September 2019 through 28 September 2019
ER -