A Twitter-based Recommendation System for MOOCs based on Spatiotemporal Event Detection

Wang, Yuanyuan; Maruyama, Naoki; Yasui, Goki; Kawai, Yukiko; Akiyama, Toyokazu

Permalink

https://hdl.handle.net/2142/98880 Copy

Description

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

Issue Date

2017

Keyword(s)

• Twitter
• MOOC
• Spatiotemporal events
• Recommendation
• Communication

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.

Publisher

iSchools

Series/Report Name or Number

iConference 2017 Proceedings Vol. 2

Type of Resource

text

Language

en

Permalink

http://hdl.handle.net/2142/98880

Copyright and License Information

Copyright 2017 is held by the authors.

Owning Collections