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Engineering active
learning: LEGO robots and
3D virtual worlds
Dr. Michael Vallance
Future University Hakodate, Japan
about FUN
research
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.
“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
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)
context
http://spectrum.ieee.org/energy/nuclear/24-hours-at-fukushima
context motivated
by 3/11
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?
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)
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.
implementation
(virtual spaces)
implementation
(robots)
implementation
(real world circuits)
reactor
off switchradioactive bins
control
station
implementation
(students)
movie demo time
http://tinyurl.com/m34wpr9
data
Task flow
chart for
simulation
Lesson outline for UK &
USA teachers
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
Cognitive)process
Knowledge$
dimension
factual conceptual procedural metaG$
cogniHve
remember
understand
apply
analyze
evaluate
create
Bloom’s revised taxonomy of learning objectives
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
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
based on Pearce et. al., 2005
Immersivity ( or Flow)
The ‘optimal zone’ of immersivity
observations
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
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.
robots or not …
what canYOU take away from this talk?
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)
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
(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
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

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Engineering active learning: LEGO robots & 3D virtual worlds

  • 1. Engineering active learning: LEGO robots and 3D virtual worlds Dr. Michael Vallance Future University Hakodate, Japan
  • 3.
  • 4.
  • 5.
  • 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)
  • 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.
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  • 38. data
  • 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
  • 44.
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  • 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
  • 47.
  • 48. based on Pearce et. al., 2005 Immersivity ( or Flow)
  • 49. The ‘optimal zone’ of immersivity
  • 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