The University of Sydney Page 1
Learning to work
across boundaries -
opportunities for
research and
innovationLina Markaus...
The University of Sydney Page 2
Interdisciplinary
skilfulness
Harnessing
“soft” and
“generic” skills
Understanding and
cre...
The University of Sydney Page 3
Facing a methodological challenge of the
science of learning interdisciplinary science(s)
...
The University of Sydney Page 4
Design, implementation and development
Design-based research
Design-based implementation r...
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Developing and researching interdisciplinary skillfulness

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Ideas presented at the Closing plenary: Learning to work across boundaries - opportunities for research and innovation (Panel: Tim Shaw, Peter Reimann, Lina Markauskaite, Tina Hinton, Phil Poronnik)

Closing session – BRIEF
This (‘Sciences & Technologies of Learning’) Research Fest is being hosted by the ‘Science of Learning Science’ node of the Charles Perkins Centre (CPC), in the CPC Hub building.
The CPC is the University’s largest and most ambitious commitment to interdisciplinary research and teaching, aimed at combining many talents around the challenges of obesity, diabetes and cardiovascular disease.
For the CPC to be successful, effective ways have to be found of working across disciplinary boundaries, and up and down the paths that translate between basic research, design of interventions, policy, changes in human behaviour, and health outcomes. We also have to maximise synergies between teaching and research. We have to help students learn to work in ways that the CPC embodies.
The Science of Learning Science node brings together a number of CPC researchers who are interested in understanding how to improve learning and teaching (broadly defined) in the CPC’s areas of operation.
Given this context – the challenges facing CPC and us all:

1. What do you see as the most worthwhile things for the Science of Learning Science node to be doing over the next 2-3 years?
2. What have you learned today that excites you about the scope for progress and/or the challenges here?
I’d like each of you to prepare an informal 5 minute response to the two questions in the paragraph just above.
Peter Goodyear

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  • Closing plenary: Learning to work across boundaries - opportunities for research and innovation (Panel: Tim Shaw, Peter Reimann, Lina Markauskaite, Tina Hinton, Phil Poronnik)

    Closing session – brief
    This (‘Sciences & Technologies of Learning’) Research Fest is being hosted by the ‘Science of Learning Science’ node of the Charles Perkins Centre (CPC), in the CPC Hub building.
    The CPC is the University’s largest and most ambitious commitment to interdisciplinary research and teaching, aimed at combining many talents around the challenges of obesity, diabetes and cardiovascular disease.
    For the CPC to be successful, effective ways have to be found of working across disciplinary boundaries, and up and down the paths that translate between basic research, design of interventions, policy, changes in human behaviour, and health outcomes. We also have to maximise synergies between teaching and research. We have to help students learn to work in ways that the CPC embodies.
    The Science of Learning Science node brings together a number of CPC researchers who are interested in understanding how to improve learning and teaching (broadly defined) in the CPC’s areas of operation.
    Given this context – the challenges facing CPC and us all:

    What do you see as the most worthwhile things for the Science of Learning Science node to be doing over the next 2-3 years?
    What have you learned today that excites you about the scope for progress and/or the challenges here?
    The closing session is 3-4 on Thursday 5th Nov in the CPC Auditorium.

    I’d like each of you to prepare an informal 5 minute response to the two questions in the paragraph just above.
    If you think you would find it useful to pre-prepare a slide for this (JUST ONE) make sure David Ashe gets it before Thursday 5th.
    After the 5 minute responses, we’ll open up to questions & comments from the floor and we’ll conclude with the ‘best poster’ awards.
     
    Thanks
    Peter Goodyear, 27-Oct-15.
     
  • What do you see as the most worthwhile things for the Science of Learning Science node to be doing over the next 2-3 years?

    Response: Getting grips with interdisciplinary skilfulness: understanding and creating better curriculum and better environments for learning

    Key idea is how to help students to:
    Act more flexibly with disciplinary knowledge
    Learn forms of knowledge and knowing that are essential for creativity, innovativeness, in saturated with technologies and data environments, but hard to articulate and/or hard to teach.

    Such initial skill sets could be...
    1. System thinking – to equip with tools that help see phenomena from multiple (disciplinary) perspectives.
    But then going beyond analytical insight
    2. Design thinking - learning to combine different kinds of (disciplinary) knowledge and ways of knowing into practical solutions
    And then go beyond “hypothetical” and learning
    3. Situative, grounded ways of thinking needed for skilful, attuned to the physical and social environment action. Learning to work in physical hybrid and heterogeneous multi-disciplinary environments, developing sensitivity to context. Developing fine-tuned sensitivity to specific situations and being able to adjust disciplinary ways of thinking to the material realities, ability to answer knowledge questions in the world (interact with a patient, understand what he/she is saying and carry out a job). And learn to do this, not just wait until practice “teach” students to do this.

    Harnessing and hardening soft
    1. Collaboration science – I mean science to collaborate. We should find better ways to teach students to work collaboratively. An obvious way is to help students harness knowledge and methods of “science of team science”
    2. Data thinking/science – I mainly mean that we should go beyond simple statistical literacy and help students to learn data-driven ways of thinking that underpin data science. Understanding capacity to extract knowledge from data by using not only statistics, but also data mining, knowledge discovery and visualisation tools.
    3. Computational thinking – I think we need to go beyond digital or ICT literacy and pay much more attention to computational thinking. Here I primarily have in mind two main features of computational thinking: 1) way of knowing that draws on fundamental principles of abstraction and articulation that are fundamental for general capability to create shareable knowledge; and 2) a way of thinking that id concerned with answering question “how could I solve this problem effectively by embracing the distributed capacity of networks, humans and machines”.
  • One of key challenges that we will face is a deep a methodological challenge of the science of learning interdisciplinary science(s)
    Lets start from when we can call something a “science”.

    1. The trouble is that we don’t have yet a well articulated phenomenon of “learning interdisciplinary science”
    2. In fact we are called to create concepts that could be implemented rather than accumulated.
    3. If we follow this logic then we should teach students to create such concepts that should be implemented rather than just how to accumulate them

    1. Do we have a phenomenon of “productive interdisciplinary work”?
    2. Do we have a methodology for creating concepts and phenomena simultaneously?
    3. Do we know how to teach a methodology that we yet need to create? (aka pedagogy)


    “science begins when a body of phenomena is available which shows some coherence and regularities,
    [and]
    science consists in assimilating these regularities and in creating concepts which permit expressing these regularities in a natural way.”
    <...>
    “it is this method of science rather than the concepts themselves (such as energy) which should be applied to other fields of learning.” (Eugene Wigner, 1963)

    URL: www.nobelprize.org/nobel_prizes/physics/laureates/1963/wigner-speech.html

  • Could Design-based developmental implementation research be a productive direction?

    Design based research partly addressed the first challenge
    Now we saw a small expansion of DBR to the DIBR (i.e. it expands the focus from the pedagogical innovation, narrower defined, to a larger activity system in which this innovation is implemented)
    My argument that this does not solve the methodological problem. In fact, people who do interdisciplinary science need to continuously to learn and create very phenomenon of “interdisciplinary science”.
    We most likely need to draw much more heavily on the methodological approaches that have been emerging in the developmental evaluation domain (e.g., Patton, 2011)
  • Developing and researching interdisciplinary skillfulness

    1. 1. The University of Sydney Page 1 Learning to work across boundaries - opportunities for research and innovationLina Markauskaite STL Research Fest @ CPC 5 November 2015
    2. 2. The University of Sydney Page 2 Interdisciplinary skilfulness Harnessing “soft” and “generic” skills Understanding and creating better environments for learning... Crossing disciplinary boundaries • Collaboration science • Data thinking • Computational thinking • Systems thinking • Design thinking • Grounded thinking & responsive action
    3. 3. The University of Sydney Page 3 Facing a methodological challenge of the science of learning interdisciplinary science(s) “science begins when a body of phenomena is available which shows some coherence and regularities, <then> science consists in assimilating these regularities and in creating concepts which permit expressing these regularities in a natural way, <and> it is this method of science rather than the concepts themselves (such as energy) which should be applied to other fields of learning.” (Eugene Wigner, 1963)
    4. 4. The University of Sydney Page 4 Design, implementation and development Design-based research Design-based implementation research Design-based developmental implementation research? Design ImplementationDevelopment

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