Robot -mediated Interaction (RMI) Research: Design an evidence-based framework of learning when undertaking tasks of measurable complexity in a 3D virtual world. The students’ aim is to communicate solutions to problems which involve the programming of a robot to navigate specific circuits.  # Experiences lead to personal strategies for teamwork, planning, organizing, applying, analyzing, creating and reflection. # Measured as Essential Skills for Wales Baccalaureate Qualification, UK. 
 Evidence required by UK Education Authority for post-16 qualification.

1. Engineering active
learning: LEGO robots and
3D virtual worlds
Dr. Michael Vallance
Future University Hakodate, Japan

2. about FUN

6. research

7. Robot -mediated Interaction (RMI)
Research: Design an evidence'based+ framework+ of+ learning when
undertaking tasks+of+measurable+complexity in a 3D+virtual+world.
The students’ aim is to communicate solutions to problems which involve the
programming of a robot to navigate specific circuits.
# Experiences lead to personal strategies for teamwork, planning, organizing, applying,
analyzing, creating and reflection.
# Measured as Essential Skills for Wales Baccalaureate Qualification, UK.
Evidence required by UK Education Authority for post-16 qualification.

8. “The acquisition of knowledge and skills does
not necessarily constitute learning.The latter
occurs when the learner connects the
knowledge or skill to previous experience,
integrates it fully in terms of value, and is able to
actively use it in meaningful and even novel ways”
(Hase, 2011).
* self-determined learning
* student-centred learning

9. Learner involvement in
the environment of learning.
Learner generates
contextually
relevant content.
Spontaneous and organic
(structured, organized,
coherent, integrated)
learning experiences.
True collaboration between
teacher and learner,
and learner and learner.
Flexible curricula.
Flexible assessment.
Heutagogical
characteristics
for active learning
Hase, S. (2011), Learner defined curriculum: heutagogy and action learning in
vocational training. Southern Institute of Technology Journal of Applied
Research, Special Edition: Action research and action learning in vocational
education and training.
Heutagogy (hjuːtəgɒdjiː)

10. context

11. http://spectrum.ieee.org/energy/nuclear/24-hours-at-fukushima
context motivated
by 3/11

12. People … especially university-age … need to be better informed
and equipped to make sense of information and make subsequent
independent decisions.
International collaboration and communication are essential now
and in the future.
Simulations can be used to prepare for disaster and recovery.
As educators,
what can we learn from this disaster?

13. Robot Task Complexity
RTC$=$Σ$Mv1$+$Σ$Sv2$+$Σ$SW$+$Σ$Lv3$$
Why robots?
• Provides closed, highly defined tasks.
• Task complexity can be quantified.
• Tasks can be replicated (same level of complexity but
different maneuvers).
• Provoke behaviors and communicative exchanges which
can be located on a framework for analysis.
Circuit$Task$Complexity$
CTC$=$Σ$(d$+$m$+$s+$o)

14. Why virtual spaces?
• Active 3D communication space.
• Simulated context (cf. NASA, Los Alamos, USA DoD).
• Immersion .. flow .. impact on learning.
• Future of online communication (cf. Rift, Glasses,
avatar, AR).
• Remote control of virtual & real robots.
• Determine the pedagogy & learning (or the heutagogy):
‘how’ & ‘why’ & ‘what’
• Students can design & manipulate the learning
environment.

15. implementation
(virtual spaces)

25. implementation
(robots)

28. implementation
(real world circuits)

31. reactor
off switchradioactive bins
control
station

34. implementation
(students)

37. movie demo time
http://tinyurl.com/m34wpr9

38. data

39. Task flow
chart for
simulation

40. Lesson outline for UK &
USA teachers

41. Learning objectives
Task
Task: robot
actions
CTC/ target CTC only /
objective is to iteratively
increase CTC/
Collabo
ration
STEM/ anticipated
Essential
Skills (Wales
Baccalaureat
e)/
anticipated
RTC/ post
task
calculation
based upon
students’
solution.
T1
Movement:
follow the
line.
Sensors: light
and touch
CTC = Σ (d + m + s+ o)
CTC= 1+2+2+1 = 7
Japan
teach
UK
S: Recognition of light sensor values. What
happens when trigger point increased/ decreased?
T: Learn how to organise NXT program blocks
logically.
E: Construct a robot. Connect software to
hardware.
M: Recognise spatial movements and the
problem of friction. Change surface to see if
robot works the same. Calculate coefficient of
friction.
Identify
Plan/
manage
Explore/
Analyse
(organize)
Evaluate
(checking)
Reflect
T2
Movement:
follow the
line.
Sensors:
colour and
action.
CTC= 1+2+2+2 = 8
UK
teach
Japan
S: Recognition of light sensor values. What
happens when trigger point increased/ decreased?
How does the NXT sensor recognise colour R, G
or B? Try different colour variations and observe
subsequent robot actions.
T: Learn how to organise NXT program blocks
logically.
E: Construct a robot. Connect software to
hardware.
M:
Identify
Plan/
manage
Explore/
Analyse
(organize)
Evaluate
(checking)
Reflect
T3
Movement:
square.
Sensors:
touch and
sound.
CTC = 4+3+1+1 = 9
Japan
teach
UK
S:
T: Learn how to organise NXT program blocks
logically.
E: Construct a robot. Connect software to
hardware.
M: Calculate distance, speed and force (touch).
Identify
Plan/
manage
Explore/
Analyse
(organize)
Evaluate
(checking)
Reflect

42. Cognitive)process
Knowledge$
dimension
factual conceptual procedural metaG$
cogniHve
remember
understand
apply
analyze
evaluate
create
Bloom’s revised taxonomy of learning objectives

43. Data is captured and
coded using neo-
Bloomian descriptors:
virtual screen capture
+ real world video
capture.
Transana s/w
Google Drive
16 tasks
60 hours of data

46. CTC$=$Σ$(d$+$m$+$s+$o)
RTC$=$Σ$Mv1$+$Σ$Sv2$+$Σ$SW$+$Σ$Lv3$
Task CTC RTC
T2 0.56 0.22
T3 0.5 0.42
T4 0.81 0.22
T5 0.81 0.57
T6 1 0.85
T7 0.69 1
T8 0.25 0.39
T9 0.31 0.33
T10 0.19 0.2
T11 0.63 0.76
T12 0.63 0.84
T16 0.56 0.83
T17 0.25 0.22
T18 0.31 0.65
T19 0.31 0.65
T20 0.69 0.48
T21 0.31 0.65
T28 0.25 0.17
Circuit$Task$Complexity$
Robot$Task$Complexity
Task
TaskComplexity

48. based on Pearce et. al., 2005
Immersivity ( or Flow)

49. The ‘optimal zone’ of immersivity

50. observations

51. Procedural knowledge required little remembering but
more applying and evaluating. Active learning.
With increased task complexity, the amount of analyzing,
evaluating and creating also increased. Active learning.
BUT NOT ALWAYS!! Later tasks revealed that making tasks more complex
does not necessarily engage in more occurrences of same components of
the cognitive process.
Why the difference? Immersion. Increased analyzing,
evaluating and creating when students engaged in tasks in zone of optimal
immersivity.
Learning is not linear (we already know that, don’t we!!) as might be
assumed by university metrics for under-graduate and post-graduate
education.
Our RMI data has revealed

52. Robot tasks involving sensors lead to a more immersed experience.
Let students iteratively design, build and utilize modes of communication in 3D
virtual spaces. They will use them.
UK students used mostly procedural language (general) with confirmation
questions.
Japanese students offered mostly instructional language (specific) but with few
instances checking for understanding.
Active learning can be implemented through student- determined design of
learning environments and tasks leading to particular types of thinking that are
sensitive to heutagogy. # Forthcoming paper with Dr. P.A. Towndrow.
Diana Laurillard (2012) calls this the design of learning as practice.
Laurillard, D. (2012). Teaching as a design science. New York: Routledge.

53. robots or not …
what canYOU take away from this talk?

54. Learner involvement in
the environment of learning.
Learner generates
contextually
relevant content.
Spontaneous and organic
(structured, organized,
coherent, integrated)
learning experiences.
True collaboration between
teacher and learner,
and learner and learner.
Flexible curricula.
Flexible assessment.
student-directed
active learning
Hase, S. (2011)

55. Anderson, L.W., Krathwohl, D.R., Airasian, P.W., Cruicshank, K.A., Mayer, R.E., Pintrich, P.R., Raths, J.
& Wittrock, M.C. (2001). A taxonomy for learning, teaching and assessing: A revision of Bloom’s
taxonomy of educational objectives. New York: Longman.
Battro, A. M., Fischer, K. W. & Lena, P. J. (2011). The educated brain: essays in neuroscience. UK:
Cambridge University Press.
Bloom, B.S. (Ed.) (1956). Taxonomy of Educational Objectives, the classification of educational goals.
Handbook 1: Cognitive Domain. New York: McKay.
Dewey, J. (1938). Experience & education. New York: Touchstone.
Hase, S. (2011), Learner defined curriculum: heutagogy and action learning in vocational training.
Southern Institute of Technology Journal of Applied Research, Special Edition: Action research and
action learning in vocational education and training. Available from http://sitjar.sit.ac.nz/SITJAR/
Special Accessed August 16, 2014.
Laurillard, D. (2012). Teaching as a design science. New York: Routledge.
Tarricone, P. (2011). The taxonomy of metacognition. New York: Psychology Press.
References

56. (1) T. Morris-Suzuki, D. Boilley, D. McNeill and A. Gundersen. Lessons from Fukushima. Netherlands: Greenpeace
International, February 2012.
(2) J. Watts. “Fukushima parents dish the dirt in protest over radiation levels.” The Guardian, May 2, 2011. [Online].
Available: http://www.guardian.co.uk/world/2011/may/02/parents-revolt-radiation-levels [Accessed August 20, 2012].
(3) L. W. Hixson. “Japan’s nuclear safety agency fights to stay relevant.” Japan Today. [Online]. Available: http://
www.japantoday.com/category/opinions/view/japans-nuclear-safety-agency-Fig.hts-to-stay-relevant [Accessed August
20, 2012].
(4) N. Crumpton. “Severe abnormalities found in Fukushima butterflies.” BBC Science & Environment. [Online].
Available: http://www.bbc.co.uk/news/science-environment-19245818 [Accessed August 20, 2012].
(5) E. Guizzo. “Fukushima Robot Operator Writes Tell-All Blog.” IEEE Spectrum, August 23, 2011. [Online]. Available:
http://spectrum.ieee.org/automaton/robotics/industrial-robots/fukushima-robot-operator-diaries [Accessed August 20,
2012].
(6) M. Vallance and S. Martin. “Assessment and Learning in the Virtual World: Tasks, Taxonomies and Teaching For
Real.” Journal of Virtual Worlds Research Vol. 5, No. 2, 2012.
(7) S. B. Barker and J. Ansorge. “Robotics as means to increase achievement scores in an informal learning environment.”
Journal of Research in Technology and Education, Vol. 39, No. 3, pp. 229-243, 2007.
(8) D.R. Olsen and M.A. Goodrich, “Metrics for evaluating human-robot interactions.” [Online]. Available: http://
icie.cs.byu.edu/Papers/RAD.pdf [Accessed March 14, 2009].
(9) M. Pearce, M. Ainley and S. Howard. “The ebb and flow of online learning.” Computers in Human Behavior, Vol. 21,
pp. 745–771, 2005.
(10) M. Vallance, C. Naamani, M. Thomas and J. Thomas. “Applied Information Science Research in a Virtual World
Simulation to Support Robot Mediated Interaction Following the Fukushima Nuclear Disaster.” Communications in
Information Science and Management Engineering (CISME). Vol. 3 Issue 5, 2013, pp. 222-232.
Additional resources

57. Engineering active
learning: LEGO robots and
3D virtual worlds
Dr. Michael Vallance
Future University Hakodate, Japan
http://www.mvallance.net
This PDF is at http://tinyurl.com/mnmx3kx