Designing a Mobile Digital Backchannel System for Monitoring Sentiments and Emotions in Large Lectures
0 likes395 views
The document outlines the design of a mobile digital backchannel system, Classense, aimed at enabling lecturers to monitor student sentiments and emotions in large lectures in real-time. It details the system's architecture, user interfaces, and technical implementation, which employs microblogging and emoticons for emotional expression. Future work will focus on testing the system's stability and conducting evaluations in actual lecture settings.
Designing a Mobile Digital Backchannel System for Monitoring Sentiments and Emotions in Large Lectures
- 1. Designing a Mobile Digital Backchannel System for Monitoring Sentiments and Emotions in Large Lectures By Peerumporn Jiranantanagorn PhD Candidate School of Computer Science, Mathematics and Engineering Flinders University
- 2. Outline • Introduction • Research questions • Related work • Overview of the system • User interfaces • Technical implementation • Conclusion and future work
- 3. Introduction • In a large lecture, it is difficult for lecturers to process, respond and know overall emotions and sentiments of students in real time while they are teaching.
- 4. Introduction • One way to make a large classroom more manageable and engaging is to use a digital backchannel system. • However, the scattered and sparse nature of posts makes it impossible for the lecturer to get a current overall picture of students’ learning as well as the emotions and sentiments of students in a large lecture.
- 5. Research questions • How to design and develop a system to support lecturer to know students’ real-time morale and the current important discussions during her/his lecture? • How to design and develop a system with a microblogging user interface that allows students to express their sentiments and emotions in a large lecture? • How to design a questionnaire to evaluate the user acceptance and user satisfaction of the system from the perspectives of both the lecturers and the students?
- 6. Related Work • A comparison of the existing digital backchannel systems and our system Backchannel Systems Microblogging Support Post Classification Vote Sentiment Emotion Hotseat (2010) n/a n/a Backstage (2011) n/a n/a ClasCommons (2012) n/a n/a n/a ActiveClass (2003) n/a n/a n/a ClasSense
- 7. Overview of the ClasSense
- 8. Mobile Application for Student
- 9. Web Application for Lecturer
- 10. Technical Implementation • The ClasSense mobile and web applications have been developing using jQuery framework, JavaScript, PHP and MySQL. • All applications are hosted in the cloud. • Emotion expression is currently through emoticons and selecting from Kort’s twelve learning relevant emotions hashtags (Kort 2001), which are “#frustration”, “#disappointment”, “#confusion”, “#satisfaction”, “#hopefulness”, “#confident”, “#dispirited”, “#boredom”, “#dissatisfied”, “#interest”, “#curiosity”, and “#enthusiastic”
- 11. Technical Implementation • Morale score for plotting graph is based on normalising values from SentiStrength score, number of posts and range of score (1…5). • For web application, post ranking is based on morale scores, number of likes and dislikes, number of comments and post time. • For mobile application, post ranking is based on ageing score, number of vote and number of comment.
- 12. Evaluation • The system will be evaluated using questions framed with the – Technology Acceptance Model and – Seven Principles for Good Practice in Undergraduate Education • Still researching on a Usability testing of the system
- 13. Conclusion and Future Work • The ClasSense system has been developed to help lecturer monitor the morale of students and respond to the important issues students have in real-time. • Future work includes – System stability and validity testing – Customise and test the SentiStrength – Pilot and formal evaluation in large lectures