Files in this item



application/pdf2pt19_Wang-Twitter.pdf (513kB)
(no description provided)PDF


Title:A Twitter-based Recommendation System for MOOCs based on Spatiotemporal Event Detection
Author(s):Wang, Yuanyuan; Maruyama, Naoki; Yasui, Goki; Kawai, Yukiko; Akiyama, Toyokazu
Spatiotemporal events
Abstract:Nowadays, students utilize MOOCs (e.g., Coursera, edX) and SNS services (e.g., LINE, Twitter, Facebook, Tumblr) in courses for learning. This paper presents a Twitter-based recommendation system to search and communication, and it is associated with a web page by detecting spatiotemporal events such as opinions, questions, or impressions about courses on Twitter. Through it, users can grasp popular courses or avoid crowded courses referring to time periods while they browse any web pages. Moreover, the system also enables users to communicate with others browsing the similar pages or users' locations about the similar pages. For this, the system extracts relevance between different pages by detecting tweets of each page in each time period with machine learning algorithms and the number of unique Twitter users. Thus, the system presents a ranking of recommended pages, a tag cloud of tweets and a list of tweets which are related to recommended pages to help users obtain the latest information about recommended pages. In this paper, we propose that students utilize the system to enhance interaction among and with others in actual classrooms. This promises to enlarge the learning effects of students and improve the student collaboration.
Issue Date:2017
Citation Info:Wang, Y., Maruyama, N., Yasui, G., Kawai, Y. & Akiyama, T. (2017). A Twitter-based Recommendation System for MOOCs based on Spatiotemporal Event Detection. In iConference 2017 Proceedings, Vol. 2 (pp. 152-155).
Series/Report:iConference 2017 Proceedings Vol. 2
Genre:Conference Poster
Rights Information:Copyright 2017 is held by the authors.
Date Available in IDEALS:2017-12-05

This item appears in the following Collection(s)

Item Statistics