"Designing a Mobile Digital Backchannel System for Monitoring Sentiments and Emotions in Large Lectures" was presented by Peerumporn Jiranantanagorn on 28 September 2015 in the 24th Australasian Software Engineering Conference (ASWEC 2015) at the University of Adelaide, South Australia.

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

14. Thanks for listening