In education, sensor technologies are regarded with interest and new systems making use of physiological data are developed with the aim of augmenting cognition and personalize learning. This paper maps most common biomarkers associated to learning and discusses mainstream approaches adopted in education. It is claimed that students’ data should be used to support self-awareness and reflection and Feeler, a design-in-progress tool that fosters re-flection about learning experiences through EEG data is presented. Feeler de-sign is carried out with research-based design methodology. The research bor-rows from experiential learning theory and design approaches based on inquiry and experience. Feeler design makes use of time, personal experience, the dis-play of hidden information and incompleteness as key elements for reflection. Feeler research aims to discuss the possibilities and challenges of biomarkers in learning and education. The next stages of Feeler research include testing the prototype and iterate the design.

1. Feeler
Supporting awareness and reflection
about learning through EEG data
September 2015
Aalto School of Arts, Design and Architecture
Eva Durall, Teemu Leinonen

2. 1. Introduction
Research areas approached in Feeler prototype

3. 1. Introduction
EEG Skin
Conductance
Heart Rate Pupil
Dilation /
Blink Rate
Body
Posture /
Gaze
Expression
Recognition
Cognitive
Load
x x x x x
Attention x x x
Meditation x x
Mental
Fatigue
x x x x x
Alertness x x x x x
Emotions x x x x x x
Stress x x x x
EEG as one of most popular biomarkers for monitoring components of
learning
Most frequently used real-time biomarkers using non-invasive technology

4. 2. Research question
What should be the role of
biomarkers in learning?

5. 3. Feeler Prototype
Feeler prototype developed in collaboration with
Niklas Pöllönen (electronics and programming).
Feeler is composed by a digital app and a set of modules with which the
user physically interacts during the study session. EEG data is collected
through Mindwave device.
Mindwave device

6. 3. Feeler Prototype / Tangible objects
Feeler tangible objects
In Feeler, a study session is structured in 3 moments: relaxation, study
and self-assessment. Each module guides the user through visual and
haptic feedback.

7. Relaxation module
Relaxation is achieved through
calm breathing. Users are asked to
adjust their breath to the rhythm set
by a led light during 5 minutes.
3. Feeler Prototype / Tangible objects

8. 3. Feeler Prototype / Tangible objects
Study module
Users can set the amount of
time that takes the study
module. Feedback about time
progression is given through a
grid of leds that illuminates as
time passes.
At the end of the session, a
gentle vibration indicates that
time is over.

9. Self-assessment module
In the last module, learners are
expected to dedicate 3 minutes to
reflect about the session.
At the back of the boxes, users find
the following questions:
- How did you feel during the
session?
- What do you expect from EEG
data?
- What would you change for the
next session?
3. Feeler Prototype / Tangible objects

10. Connection between modules.
3. Feeler Prototype / Tangible objects
In order to change task, users need to connect the modules. A
smooth vibration indicates successful connection.

11. 3. Feeler Prototype / Digital app
Screen captures of Feeler digital app
A desktop app guides users through the different Feeler modules. At
the end of the session, user’s brain wave data is visualized.

12. Connection between modules.
3. Feeler Prototype / Digital app
Feeler data visualization

13. 4. Methods
Research-based Design (Leinonen, 2008 & 2010)

14. 5. Designing for reflection
Reflection levels that interactive technology
can support (Fleck and Fitzpatrick, 2010)
1. Revisiting
2. Revisiting with explanation
3. Dialogic reflection
4. Transformative reflection
5. Critical reflection

15. 5. Designing for reflection
- Slow technology
- Design for interruption (Hallnäas & Redström, 2001)
- Inquisitive Design
- Design for conflict and inquiry (Dalsgaard, 2008)
- Technology as Experience
- Design for openness and incompleteness (McCarthy & Wright,
2004)

16. 6. Conclusion
- The focus of learning technology based on monitoring
physiological data should be on empowering students
through reflection and awareness.
- Design strategies for supporting reflection:
- Creation of time.
- Asking reflective questions.
- Leave some aspects incomplete.

17. Thanks!
Further information:
eva.durall@aalto.fi