mLearnCon 2015 Speaker

1. Hardware Sensor
Integration for mobile
Learning
Prasoon Nigam
CTO / Vice President –
Technology
Stratbeans Consulting
www.StratBeans.com
prasoon@stratbeans.com
@prasoonnigam

2. Coverage
• What are the sensors and other sensory elements.
• Why sensor integration has revolutionary potential in learning
context – ILT vs CBT.
• When is the right time for sensor integration – are we late ?
• How to integrate basic to complex sensors to enhance learning
content – demo, if time permits.

3. WHAT
What are the sensors and other sensory elements

4. SENSED : THE
IMAGE
PROCESSED : IDENTIFIED IT BE RELATED
WITH XMEN
INTEGRATED: WHY IS HE SHOWING ME
THIS, HOW IS IT CONNECTED TO HIS TALK ?

5. What is a Sensor
Sensor “receives”, “transmitted” information which

6. LIGHT SEE
What is a Sensor
Sensor “receives”, “transmitted” information which
Eye
Webcam

7. Sensory Universe : Sensors, Transmitter,
Processor
Processor
Integrator
SensorTransmitter

8. Sensory Universe : Sensors, Transmitter,
Processor
Processor
Integrator
SensorTransmitter

9. WHY
Why sensor integration has revolutionary potential in learning
context – ILT vs CBT.

10. Lets spot sensory universe elements
involved in ILT situation
ILT : Instructor
Learner
MORE SENSORY ELEMENTS => MORE INTELL

11. Do you know the name of the XMEN
Instructor with…
ILT : Instructor
Learner
x x x
x x
NO EAR, NO EYES
MUCH SMALLER BRAIN
BRANCHING SIMULTION, SCORES ASSESSMENT
MUTANT POWER
PRESENT EVERYWHERE AT SAME TIME,
Answer is :
Madame CBT
MAGNETO STOLE HER SENSORS

12. Moment of Truth …
ILT : Instructor
x x
x x
CBT : Self Paced
NO EAR, NO EYES
MUCH SMALLER BRAIN
BRANCHING SIMULTION, SCORES ASSESSMENT
MUTANT POWER
PRESENT EVERYWHERE AT SAME TIME,
Answer is :
Madame CBT
MAGNETO STOLE HER SENSORS

13. From “OR” to“AND”
O
R
ILT : Instructor CBT : Self Paced Learner
• Personalized
• Intelligent
• Adapts with Learner’s need
• Scalable
• Self Paced
• Geography Independent
Think, if we are able to give Madame CBT her se
“intelligent AND scala

14. Best of Both Worlds : The Smart
CBT
• Adapting Content to match learner’s
state of mind
• Adapt interactivity
• Adapt Engagement
• Advices learner to take a break
• Interprets Brain Waves to determine
cognitive state of mind
• Hands Free Navigation
• Speech Detection
• Movement Detection
• Customized Gestures
• Detect Confidence Level through
speech analysis of learner
• Location based learning
Smart CBT/mLearning

15. WHEN
When is the right time for sensor integration – are we late ?

16. Time
Number of Sensors
Internet Of
Things
TIME IS
“NOW”
FOR
mLEARNING
TO
LEVERAGE
THE
SENSORS

17. The Irony !
LEARNING CONTENT
in IRON WALLS
The IRON WALL b
• mLearning
• Sensors on dev

18. HOW
Low hanging fruits + Advance Ideas

19. Simple to Complex Sensor Integration
Ideas
1.Brain wave detection
2.Hands free navigation of content using
Hand Gestures
3.Augmentation of content using AR
apps
4.Voice based response
5.Confidence level detection

20. 1: Detect Brain Attention Level
Application Area:
• Depending on brain attention level, user is
suggested to
• BREAK
• ENGAGE
• INTERACT
SENSOR :EEG
DEMO AVAILABLE
Brain
Wave
Type
Implication
Gamma Problem solving, concentration
Beta Active Mind, Busy
Alpha Restful, Relaxed
Theta Drowsiness

21. 1: Detect Brain Attention LevelSENSOR :EEG
DEMO AVAILABLE
- Operating condition for learnin
- Adaptive content
- Grade a content, before relea

22. 1 : Hands-Free Navigation of
Content using Hand GesturesApplication Area:
• Performance support system in Restaurant. Staff
can refer to recipe steps, while preparing the food
SENSOR : CAMERA
DEMO AVAILABLE

23. 2: Augmentation of Content using
Augmented Reality AppsApplication Area:
• A complex process flow, comes alive with videos
that explain that step in depth
• A user is attempting a check your knowledge, clue
appears for the knowledge options
• School children is reading about planetary system,
Screen comes live with video, explaining about the
planet he clicked
SENSOR : CAMERA
DEMO AVAILABLE

24. 3: Voice Based Response
Application Area:
• Response to an assessment can be spoken out
instead of typed
SENSOR : MIC
DEMO AVAILABLE
DO NOT WASH
DO NOT DRY
WASH IN COLD
WATER
GIVE IT YOUR
MOM
DO NOT WASH
What does this mean?
What is the Color of an
apple?
What is Gravity?
How do you determine
time?
What does this mean?

25. 4: Confidence Level Detection
Application Area:
• A Customer support executive (voice process),
speaks out an answer and using speech analytics,
he is tested on various performance parameters
like “speed of response”, “tone”, “confidence”
SENSOR : MIC

26. 5: Distraction Detection
Application Area:
• A learner is showing patterns like “shaking of legs”,
“no significant movement”, “body temperature” ,
“heart rate” and the content pops-up
• BREAK : Suggest the user to take a break
• ENGAGE : Put more engaging level of the
same content - “Watch a video to understand
better”
• INTERACT : Put a CYU question related with
the topic
SENSOR : BIO-WEARABLE

27. Final Note
• Sensor integration is NOW a reality – wearable, camera &
brain wave are top picks
• Demand sensor smart content from your content
development/technology integration teams
• Take baby steps
• Yes SCORM / xAPI complaint sensor integrated content are easily
doable
• Discuss what are the other possible application of smart
content
Lets pledge to give Madame CBT her

28. Future
Possibility
Questions ?
prasoon@StratBeans.com
@prasoonnigam

29. END