Date post: | 24-Jan-2017 |
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Engineering |
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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
Outline • Introduction • Research questions • Related work • Overview of the system • User interfaces • Technical implementation • Conclusion and future work
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.
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.
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?
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
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”
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.
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
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