TY - GEN
T1 - An Image-Based Vocabulary Learning System Based on Multi-Agent System
AU - Tangworakitthaworn, Preecha
AU - Owatsuwan, Preeyapol
AU - Nongyai, Nutsima
AU - Arayapong, Nongnapas
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Promoting and developing English language skills are essential for second language learners. This paper presents the design and development of a mobile application for promoting learners' experiences in learning the English vocabularies by formulating the compound words generated from images. The proposed system is based on the multi-Agent system (MAS) covering three main agents: matching learners agent, combination vocabulary agent, and feedback agent. The innovation of the proposed approach is that many learners (matching learners agent) can formulate the new vocabulary that is formed from the combination algorithm (combination vocabulary agent) and learners can experience new vocabulary represented by using the image results (feedback agent). The two experiments of the system performance evaluation are reported. The experimental results show that the performance of the vocabularies' detection and the sub processes execution were achieved the high accuracy.
AB - Promoting and developing English language skills are essential for second language learners. This paper presents the design and development of a mobile application for promoting learners' experiences in learning the English vocabularies by formulating the compound words generated from images. The proposed system is based on the multi-Agent system (MAS) covering three main agents: matching learners agent, combination vocabulary agent, and feedback agent. The innovation of the proposed approach is that many learners (matching learners agent) can formulate the new vocabulary that is formed from the combination algorithm (combination vocabulary agent) and learners can experience new vocabulary represented by using the image results (feedback agent). The two experiments of the system performance evaluation are reported. The experimental results show that the performance of the vocabularies' detection and the sub processes execution were achieved the high accuracy.
KW - Image Processing
KW - Image-Based Vocabulary Learning
KW - MAS
KW - Multi Agent System
KW - Object Detection
KW - Ontology
KW - Technology-Enhanced Learning
KW - WordNet
UR - https://www.scopus.com/pages/publications/85074222613
U2 - 10.1109/JCSSE.2019.8864170
DO - 10.1109/JCSSE.2019.8864170
M3 - Conference contribution
AN - SCOPUS:85074222613
T3 - JCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering: Knowledge Evolution Towards Singularity of Man-Machine Intelligence
SP - 324
EP - 329
BT - JCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th International Joint Conference on Computer Science and Software Engineering, JCSSE 2019
Y2 - 10 July 2019 through 12 July 2019
ER -