TY - GEN
T1 - Adaptive User Interface for Smart Programming Exercise
AU - Watanobe, Yutaka
AU - Rahman, Md Mostafizer
AU - Vazhenin, Alexander
AU - Suzuki, Jun
N1 - Funding Information:
This research was funded by the Japan Society for the Promotion of Science (JSPS) KAKENHI (Grant Number 19K12252).
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - An adaptive user interface for smart programming exercise and its platform is presented. The proposed adaptive user interface is oriented to repetitive exercises with many pro-gramming tasks through different learning phases. The learning phases include searching, reading, coding, testing, debugging, and refactoring, and the learner can receive smart assistance in each phase. The content of the smart assistance can be adjusted by modes predefined for each phase. The configuration and contents of the user interface are controlled by the system according to the transition of the learner's state and activities. The smart assistance is realized by different types of materials and automatic assessment systems for program codes as well as by machine learning models for the recommendation, code completion, bug highlighting, program repairing, and program transformation. In this paper, the state transition graph to organize the adaptive user interface and its smart assistant modes for each learning phase are presented. The prototype of the user interface as well as the architecture of its platform including the automatic assessment system, different machine learning models, and Iogging ecosystem are also demonstrated.
AB - An adaptive user interface for smart programming exercise and its platform is presented. The proposed adaptive user interface is oriented to repetitive exercises with many pro-gramming tasks through different learning phases. The learning phases include searching, reading, coding, testing, debugging, and refactoring, and the learner can receive smart assistance in each phase. The content of the smart assistance can be adjusted by modes predefined for each phase. The configuration and contents of the user interface are controlled by the system according to the transition of the learner's state and activities. The smart assistance is realized by different types of materials and automatic assessment systems for program codes as well as by machine learning models for the recommendation, code completion, bug highlighting, program repairing, and program transformation. In this paper, the state transition graph to organize the adaptive user interface and its smart assistant modes for each learning phase are presented. The prototype of the user interface as well as the architecture of its platform including the automatic assessment system, different machine learning models, and Iogging ecosystem are also demonstrated.
KW - Adaptive User Interface
KW - Machine Learning
KW - Programming Education
KW - Smart Learning
UR - http://www.scopus.com/inward/record.url?scp=85125954853&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125954853&partnerID=8YFLogxK
U2 - 10.1109/TALE52509.2021.9678757
DO - 10.1109/TALE52509.2021.9678757
M3 - Conference contribution
AN - SCOPUS:85125954853
T3 - TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings
SP - 588
EP - 594
BT - TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Engineering, Technology and Education, TALE 2021
Y2 - 5 December 2021 through 8 December 2021
ER -