TY - GEN
T1 - Semantic Enrichment of Twitter News for Differentiated STEAM Education
AU - Kim, Yongsung
AU - Ji, Seonmi
AU - Jung, Seungwon
AU - Moon, Jihoon
AU - Hwang, Eenjun
N1 - Funding Information:
ACKNOWLEDGMENT This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. R0190-16-2012, High Performance Big Data Analytics Platform Performance Acceleration Technologies Development) and Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2016R1D1A1A09919590).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/25
Y1 - 2018/5/25
N2 - Recently, STEAM education is attracting much attention as a new educational method. In the STEAM education lesson, the latest science/technology materials are usually used to arouse or increase the learner's interest. Twitter is one of the most effective mediums for learners to easily access such materials. A large amount of news is generated every second on Twitter and disseminated quickly to the public. However, such news is not appropriate for learning because it does not take into account various levels of learners or relevance to the class subjects. In this paper, to solve these problems, we propose a semantic enrichment scheme for the latest science/technology Twitter news for differentiated STEAM education lessons. Our proposed scheme enables the learners to browse news of desired topics and their relevant materials, even filtered by the user level.
AB - Recently, STEAM education is attracting much attention as a new educational method. In the STEAM education lesson, the latest science/technology materials are usually used to arouse or increase the learner's interest. Twitter is one of the most effective mediums for learners to easily access such materials. A large amount of news is generated every second on Twitter and disseminated quickly to the public. However, such news is not appropriate for learning because it does not take into account various levels of learners or relevance to the class subjects. In this paper, to solve these problems, we propose a semantic enrichment scheme for the latest science/technology Twitter news for differentiated STEAM education lessons. Our proposed scheme enables the learners to browse news of desired topics and their relevant materials, even filtered by the user level.
KW - education using twitter
KW - multimedia in education
KW - news in education
KW - semantic annotation
KW - steam education
UR - http://www.scopus.com/inward/record.url?scp=85046079477&partnerID=8YFLogxK
U2 - 10.1109/BigComp.2018.00078
DO - 10.1109/BigComp.2018.00078
M3 - Conference contribution
AN - SCOPUS:85046079477
T3 - Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018
SP - 487
EP - 490
BT - Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018
Y2 - 15 January 2018 through 18 January 2018
ER -