Project for the Summer School on Ubiquitous Computing, Oulu, Finland, 10-15 June 2019 Loukas Konstantinou • Cyprus University of Technology Vlad Manea • University of of Copenhagen Thaha Mohammed • Aalto University Aku Visuri • University of Oulu UBISS 2019 • Oulu, Finland Problem Lecture Boredom 23.000 universities Webometrics 50% time boring for students Götz 60% of students bored Guardian 2019 end year course evaluations Context Lecture Atmosphere Many signals: lecturer speaks, slides unfold, students roll eyes, ... Context sensing: possible with available, unobtrusive technologies Insufficient research to quantify classroom audience engagement Method Study Setup 16 lecture videos from YouTube ⨉ 5 audio clips per video ⨉ 4 workshop students ⨉ 1 rating per clip = 320 data points Results Model Accuracies Supervised Machine Learning, Classification Support Vector Machines 65% Dreadful - Cheerful 72% Timid - Assertive 75% Vague - Clear 55% Monotonous - Dynamic Lessons Learned Models gave good results for the time and resources we had Android sound mobile sensing was particularly challenging Opportunity for holistic sensing of classrooms at scale

1. How Engaging Is Your Lecture?
Aku • Loukas • Thaha • Vlad
UBISS 2019 • Oulu, Finland

2. Video

3. Problem
Lecture Boredom
23.000 universities Webometrics
50% time boring for students Götz
60% of students bored Guardian
2019 end year course evaluations

4. Context
Lecture Atmosphere
Many signals: lecturer speaks, slides
unfold, students roll eyes, ...
Context sensing: possible with
available, unobtrusive technologies
Insufficient research to quantify
classroom audience engagement

5. Input
Sound + Ratings
Lecture sound recordings for
classroom atmosphere
Ratings given by us as ground truth
of audience engagement

6. Method
Study Setup
16 lecture videos from YouTube ⨉
5 audio clips per video ⨉
4 workshop students ⨉
1 rating per clip =
320 data points

7. Output
Lecture Properties
Independently assess 4 types of
lecture property ranges Maggiani
1. Dreadful - Cheerful
2. Timid - Assertive
3. Monotonous - Dynamic
4. Vague - Clear

8. Visualization
Monotonous
Dynamic

9. Results
Model Accuracy
Machine Learning Classification
65%
Dreadful - Cheerful
72%
Timid - Assertive
75%
Vague - Clear
55%
Monoton - Dynamic

10. Prototype
Functionality
1. User clicks Start Recording
2. App records sound
3. App analyzes sound
4. User sees engagement
ratings over time
5. User clicks Stop Recording
Technology
● Client Javascript
● Server Python
● Model Python

11. Lessons
Learned
Models gave good results for the
time and resources we had
Android sound mobile sensing was
particularly challenging
Opportunity for holistic sensing of
classrooms at scale

12. Thank You
Loukas Konstantinou • Cyprus University of Technology
Vlad Manea • University of of Copenhagen
Thaha Mohammed • Aalto University
Aku Visuri • University of Oulu
UBISS 2019 • Oulu, Finland