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The Future of Education
The Future of Education
Using EEG to Improve Massive Open Online Courses Feedback Interaction
Haohan Wang, Yiwei Li, Xiaobo Hu, Yucong Yang, Zhu Meng, Kai-min Chang
Language Technologies Institute
Carnegie Mellon University
This work was supported by the National Science Foundation under Cyberlearning Grant IIS1124240. The opinions expressed are those of the authors and do not necessarily represent the views of the Institute, or the National Science Foundation.
Challenge for MOOC
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No real-time feedback from the student
Instructor one-way push
Idea Pilot Discuss
Improve learning experience in MOOC?
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Real-time feedback from the student
Instructor one-way push Brain sensing
Idea Pilot Discuss
• Electroencephalography (EEG) – Voltage signal arised from large areas of
coordinated neural activity – Rhythmic fluctuations in EEG has been
associated with emotion (Marosi, E., et al., 2002), engagement (Berka et al., 2007), etc.
• New consumer-friendly EEG device – NeuroSky MindBand: Single-channel on
forehead, dry electrode, $100 USD – Detect human emotional responses (Heraz,
& Frasson, 2009) and reading difficulty (Mostow et al., 2011) in ITS
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Our Approach: Brain Imaging
Idea Pilot Discuss
• Can EEG detect confusion? • How does it compare to human?
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Research Questions
Idea Pilot Discuss
• 10 college students • Each watched 10 two-minutes
video clips extracted from MOOC (http://open.163.com) – 5 easy videos (e.g. elementary
algebra, geometry, etc.) – 5 difficult videos (e.g. quantum
mechanics, stem cell, etc.)
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A Pilot Study
Idea Pilot Discuss
Self-reported confusion
Content difficulty
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Rest for 30 sec
Watch a 2-min video (record EEG)
Self-reported confusion (1-7)
Observer-rated confusion (1-7)
Experiment Process
Idea Pilot Discuss
Content difficulty (easy, difficult)
• Classifier: Gaussian Naïve Bayes • Features: EEG signals (averaged over time) • Labels:
– Content difficulty (binary) – Self-reported confusion (map to binary by median split)
• Cross-validation: Across trials and students
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Can EEG Detect Confusion?
Idea Pilot Discuss
Student-Specific Student-Independent
Trial 1 … Trial 9 Trial 10
EEG Features
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time 2 min
Measure Purpose Raw waveform Raw EEG sampled at 512 Hz
Attention / Meditation Proprietary measures, sampled at 1 Hz
Gamma (30-100 Hz) History of idea, attentive focus
Beta (12-29 Hz) Fully-awake, alert, excitement
Alpha (8-11 Hz) Deeply-relaxed, passive-awareness
Theta (4-7 Hz) Drowsiness, unconscious
Delta (1-3 Hz) Sleep, unaware
mean
Given FFT
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0%
20%
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100%
AVG
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Student
Self-reported confusion (ACC = 57%* and 51%)
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* * *
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0%
20%
40%
60%
80%
100%
AVG
S1
S2
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Accu
racy
Student
Content difficulty (ACC = 67%* and 57%*)
Student-specific Student-independent
Can EEG Detect Confusion?
Idea Pilot Discuss
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2.5
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3.5
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4.5
1 2 3 4 5 6 7
Clas
sifie
r
Student
EEG classifier (CORR = 0.168)
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Obs
erve
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Student
Human observer (CORR = 0.170)
How does it compare to human?
Idea Pilot Discuss
Applications for MOOC
• 7% completion rate (Jordan, 2013) – Help instructors compose MOOC
lessons that optimize engagement
• Students don’t know when to ask for help (Aleven, et al., in press) – Personalize learning experience
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• Class project for a group of Biotechnology Innovation and Computation students
• “Confusion” may be confounded • Not real MOOC data
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Limitations
Idea Pilot Discuss
Self-reported confusion
Content difficulty
Instructor effectiveness
Student knowledge
• No comparison with other detectors
Discussion
• MOOC provides huge amount of data – One more source of data that directly measures brain!
• Signals are noisy… – Brain imaging technology will continue to get better
and more available • Not scalable…
– Portable EEG devices may become house-hold accessories like microphone and webcam
• Not what MOOCs need… – Tell us what you think is useful!
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Take Home Message
1. Weak but above-chance performance for using EEG to detect when a student is confused
2. Availability of consumer-friendly EEG devices
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Questions
• Haohan Wang – Biotechnology Innovation and Computation,
Carnegie Mellon University – E-mail: [email protected]
• Kai-min Kevin Chang
– Language Technologies Institute, Carnegie Mellon University
– E-mail: [email protected]
17 Idea Pilot Discuss
