Aggregation and Mapping of Social Media Attribute Names Extracted from Chat Conversation for Personalized E-Learning

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.