pushing the awareness envelope
Post on 27-Jun-2015
162 Views
Preview:
DESCRIPTION
TRANSCRIPT
Pushing the awareness envelope: from interactions
to emotionsIsrael GutiérrezDerick Leony
University Carlos III of Madrid
Awareness across spaces with learning analytics
Data collection
Process /Analysis
Inter-ventions
• Subject: Teachers and students
• Tool: Learning analytics with traces across spaces
• Objective:
• Recommend: classON and affective recommender
Guess what?
Company 1
Company 2
0 2,5 5 7,5 10
Number of months to reach 1 million users
Guess what?
Company 1
Company 2
0 2,5 5 7,5 10
Number of months to reach 1 million users
Guess what?
Company 1
Company 2
0 2,5 5 7,5 10
Number of months to reach 1 million users
Online learning and... flipping the classroom
• Lectures at home
• Homework in class
• Let’s support this new types of classes!!!
Flipped session = computer lab session• Observations in lab sessions
• Inefficient interactions
• Problems orchestrating the session
• Definition of metrics (ICALT’12)
• Waiting time
• Order
Proposed solution
Data collection
Process /Analysis
Inter-ventions
Events from web sensors
Attach info to augmented physical space
Recommend interactions
• Awareness tool supporting teachers
• Context
• Lab sessions (f2f) = flipped session
• Regulate interactions
• Empower teacher for improving orchestration
http://www.class-on.org
Student interface
BART SIMPSONLISA SIMPSON
Teacher interface
25
32
BUSY
Teacher interface
BART SIMPSON
LISA SIMPSON
BUSY
Orchestration aspects that could be improved• Based on the 5+3 aspects framework
(ECTEL’12)
• Management
• Intervention
• Assessment
• Design
classON Evaluation
• Quantitative data: not enough improvement
• Teachers perception
• info in a glance
• fair distribution of feedback
• Students perception
• more fair (time/order)
• trust the help is coming
classON Further Steps
• Suggest peer support interactions
• New strategies for feedback dispatching
• Annotation system
• Exploitation of qualitative data in questions & answers
• New experiment(authoring tool): Superpowers to teachers (workshop at Madrid Science Week)
• Visualization + gamification (explored in my stay at KUL, but in the context of Research 2.0)
Ambient displays• Not the main focus of
user attention
• Used for
• Awareness
• Persuade
• Contexts
• Health / good habits
• Design factors
• Appeal (usability + aesthetics)
• Learnability: match user’s expectations
• Awareness: match user’s interests
• Distraction: low
Gamification
• Applying game design and game mechanics to non-game contexts
• Engage users in a new system
• Engage users keep using the system
• Metaphors for ambient displays using game concepts
GambientFication
• KU Leuven group
• TiNYARM
• Use iPads as ambient displays
• Use during working hours
• Use a holder as a photo frame
• 10 last days of the stay
Ambient displays + Gamification (Powered by Analytics)
Evaluation
• Awareness of research activity in the short/medium/long term
• Appeal (usability + aesthetics)
• Distraction
• User perception
• Awareness/reflection/sense-making/behavior change
And now, let’s jump to emotions...
13/09/2012 EEE Meeting 2012 1
Context
● Life-cycle of Learning Analytics process
DataCollection
Process /Analysis
Inter-ventions
Events fromVirtual
Machine
URLsBash commands
FilesCompilations
13/09/2012 EEE Meeting 2012 2
Data collection
● Collect events from:
– Web browser
– Text editor
– Programming tools
– Session
● More information:
– Partial grades
– Forums
13/09/2012 EEE Meeting 2012 3
Problem statement
● Detecting emotions in educational settings
– Affective computing (Picard et al., 1997)
– Use of physical sensors
– Applied mainly in ITS
● Recommender systems in TEL
– SoA Review (Draschler et al., 2011)
– There are no implementations that
consider the affective state of the learner
13/09/2012 EEE Meeting 2012 4
Objectives
● Detect learner emotions from the events generated within a learning environment
● Recommend learning resources based on the detected emotions (and other variables)
● Reflecting affective states into the virtual environment
● Providing awareness of affective states
13/09/2012 EEE Meeting 2012 5
Proposed solution
● Complete Learning Analytics cycle
DataCollection
Process /Analysis
Inter-ventions
Events fromVirtual
Machine
Detectingaffective
state
RecommendLearning
Resources
13/09/2012 EEE Meeting 2012 6
Classification of emotions
● Two prevalent ways:
– Two-dimensional features (valence, arousal)
– Discrete sets (e.g. Ekman's basic emotions: anger, disgust, fear, joy, sadness, surprise)
● Learning-domain set proposed by D'Mello et al. (2007): Boredom, confusion, frustration, eureka, motivation, neutral
13/09/2012 EEE Meeting 2012 7
Processing events
● Input: Events collected during a working session
● Methods: Hidden Markov Models, GSP
13/09/2012 EEE Meeting 2012 8
Intervention: Recommend resources
● Resource recommendation
● User-based (vs item) collaborative filtering
● Modified process:
– Define preference model
– Calculate user similarity including emotions
– Select neighbors of learner
– Obtain list of recommended items
– Filter recommendations according to current emotion
● Implementation based on Apache Mahout
13/09/2012 EEE Meeting 2012 9
Evaluation
● Affective state detection
– Learner feedback
– Instructor feedback
– External sensors:
● Galvanic skin response
● Facial gestures
● Resource recommendation
– Information system metrics:
● Precision and recall
13/09/2012 EEE Meeting 2012 10
First Prototype
● Widget: Learning Resource Affective Recommender
● Among the winners of the 3rd ROLE Widget Enchantment
● Paper accepted in workshop MATEL 2012
● Demo available!
13/09/2012 EEE Meeting 2012 11
Future work
● Implementing two sensors for emotion: Galvanic skin response (Arduino-based), facial gestures (OpenCV-based)
● Analyzing the inclusion of other variables:
– Grades, session duration, amount of events
● Experiments planned:
– Detection: Madrid's Science Week 2012
– Recommendation: C programming class
● Deployment on Amazon EC2
● Integration with LearnGLASS
13/09/2012 EEE Meeting 2012 12
Thank you!
Questions?
top related