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 ComplexityRTC$=$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

controlstation

implementation (students)

movie demo timehttp://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/

Collaboration

STEM/ anticipated

Essential Skills (Wales Baccalaureate)/ 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 = 8UK 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 = 9Japan 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 tasks60 hours of data

CTC$=$Σ$(d$+$m$+$s+$o)

RTC$=$Σ$Mv1$+$Σ$Sv2$+$Σ$SW$+$Σ$Lv3$ 

Task CTC RTCT2 0.56 0.22T3 0.5 0.42T4 0.81 0.22T5 0.81 0.57T6 1 0.85T7 0.69 1T8 0.25 0.39T9 0.31 0.33T10 0.19 0.2T11 0.63 0.76T12 0.63 0.84T16 0.56 0.83T17 0.25 0.22T18 0.31 0.65T19 0.31 0.65T20 0.69 0.48T21 0.31 0.65T28 0.25 0.17

Circuit$Task$Complexity$Robot$Task$Complexity

Task

Task

Com

plex

ity

based on Pearce et. al., 2005

Immersivity ( or Flow)

The ‘optim

al zone’ of im

mersivity

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 can YOU 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 VallanceFuture University Hakodate, Japan

http://www.mvallance.net

This PDF is at http://tinyurl.com/mnmx3kx