TY - GEN
T1 - Aggregation and mapping of social media attribute names extracted from chat conversation for personalized E-learning
AU - Al-Abri, Amal
AU - Jamoussi, Yassine
AU - Alkhanjari, Zuhoor
AU - Kraiem, Naoufel
N1 - Funding Information:
supported by Ministry of Manpower,
Publisher Copyright:
© 2019 IEEE.
PY - 2019/2/19
Y1 - 2019/2/19
N2 - Nowadays learners are using social media applications to perform collaborative learning tasks. Learners from the same class can perform the task through discussions and conversations using different applications. These discussions generate valuable chat conversations contain information related to learners' characteristics. For educators, to understand the personal characteristics of those learners, collecting and analyzing these conversations is required. Due to the varied structure of these applications, an aggregation and mapping mechanism is required. This paper presents an aggregation model for the conversation data collected from different social media applications. This aggregation is built, based on the attributes required to enhance personalization services. The attributes identified have been used to construct a unified format for the chat data generated, using different social web applications. To perform the aggregation task, similarity matching technique using ontology-based model has been adopted. The promising results from the matching process indicate the usefulness of the model.
AB - Nowadays learners are using social media applications to perform collaborative learning tasks. Learners from the same class can perform the task through discussions and conversations using different applications. These discussions generate valuable chat conversations contain information related to learners' characteristics. For educators, to understand the personal characteristics of those learners, collecting and analyzing these conversations is required. Due to the varied structure of these applications, an aggregation and mapping mechanism is required. This paper presents an aggregation model for the conversation data collected from different social media applications. This aggregation is built, based on the attributes required to enhance personalization services. The attributes identified have been used to construct a unified format for the chat data generated, using different social web applications. To perform the aggregation task, similarity matching technique using ontology-based model has been adopted. The promising results from the matching process indicate the usefulness of the model.
KW - Aggregation model
KW - Collaborative learning
KW - Personalization
KW - Semantic mapping
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=85063196107&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063196107&partnerID=8YFLogxK
U2 - 10.1109/ICBDSC.2019.8645567
DO - 10.1109/ICBDSC.2019.8645567
M3 - Conference contribution
AN - SCOPUS:85063196107
T3 - 2019 4th MEC International Conference on Big Data and Smart City, ICBDSC 2019
BT - 2019 4th MEC International Conference on Big Data and Smart City, ICBDSC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th MEC International Conference on Big Data and Smart City, ICBDSC 2019
Y2 - 15 January 2019 through 16 January 2019
ER -