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Advancing Theory and Research in the Sciences of

Learning: Innovative Technologies and

Pedagogies

Michael J. Jacobson

Co-director, Centre for Research on Computer Supported Learning and Cognition (CoCo)

Deputy Director, Institute for Innovation in Science and Mathematics EducationThe University of Sydney

Overview

• Innovative learning technologies and the sciences of learning

• Agent augmented virtual world for learning science inquiry

• “Productive Failure” and learning the physics of electricity

• “Reflections” on future theory and research directions in the learning sciences

School should be less about preparation for life and more like life itself. John Dewey (1859-1952)

An Intelligent Agent Augmented Virtual Environment for Learning

Science Inquiry: Preliminary Research

Michael J. JacobsonCentre for Research on Computer Supported Learning and Cognition,

University of Sydney

Chunyan Miao, Beaumie Kim, Zhiqi Shen, Mark Chavez

Learning Sciences Laboratory, National Institute of Education, Nanyang Technological University Singapore

A Glimpse of Future Learning?

6

Literature Review of Learning in 3D MUVE & Gaming Environments

• Widely held assumption: 3D virtual and game environments will greatly enhance student learning (“like standing next to a fire...”)

• Literature review in 2006: Less than 5 studies documenting significant learning gains

• Harvard’s Chris Dede (VS collaborator):

• “Content acquired typically is neither related to national standards for academic content nor useful if applied to real world contexts,

• and no studies have yet established the transfer of skills mastered in gaming to life situations.”

7

Quick & Dirty Guide to Developing Virtual Singapura

8

Intelligent Agents and “Goals”

• Infuse intelligent agents into new media (virtual, mixed, mobile, and pervasive) to create new experiences and dimensions in game design and interactive narratives

• Agents represent virtual characters and players with human-like behaviors

• Intent to bring new dimensions of engaging experiences into virtual space and life

• Emerging Research Lab group: Second place finish in agent competition at 2006 AAMAS (International Joint Conference on Autonomous Agents and Multi-agent Systems)

9

Features of Agent Scripts (I)

• Location awareness

• Agents initiated conversationsExample 1

Student’s question: Where am I? Mdm Tan Kim Gek (lady proprietor of the Chinese Medical Hall): You are at the Chinese Medical Hall. There are maps around in the city that shows your current location. You can click on any of the labeled locations in the map to teleport to that place in the city.

Example 2Student’s question: Where is the hospital? Mdm Tan Kim Gek: You can refer to the map of the city for help. You can ALSO click on any of the labeled locations in the map to teleport to that place in the city.

VS Pilot Research 2007

• Grade 7 (Secondary 1) students

• 3 classes (105) used VS with science inquiry module

• 6 classes (222) used a PBL paper-based version

• Bandwidth limitations: only 10 working computers

VS Pilot Findings

• Naturally changing role of teachers

• Paper based comparison group scored higher on posttest than VS group

• Significant but weak group effect

• VS students and teachers very positive

• “...so we don’t feel bored...because the teachers just like (gave) us all the notes...then we just copy down and all that, but in “Virtual Singapura”, we get to carry out experiments ourselves, so we understand better. “

• “...it (“Virtual Singapura”) was very interesting as we not only get information from our teachers... we get to conduct experiments by ourselves, and there were different outcomes from the others...”

• “...the process of learning while you’re playing is quite fun...you can learn better.

Student Views of Learning with VS

VS2 Study

• School requested we do follow-up study with all classes in new Secondary 1 January 2008 cohort

• Research interest: Transfer of learning about science inquiry from virtual environment

• Analogical encoding theory (Gentner et al.)

• Three post VS treatments with science inquiry cases

• Contrast and compare

• Sequential individual problem solving

• Reading comprehension

Quantitative Findings

• Students in all classes performed at comparable levels on measures paper-based measures of understanding of inquiry, attitudes towards science, content knowledge

• Significant differences were found on the posttest transfer problem solving inquiry question

• Contrast and compare group significantly higher

• Consistent with analogical encoding theory

• Provisional since a small effect size

• Potentially important finding suggesting that non-computer based individual learning activities might enhance learning gains from collaborative virtual experiences

Things to Notice

• Virtual environment merely a way to allow students to experience inquiry, problem, and project types of learning experiences

• Changing from teacher centered to learning centered pedagogies can be enabled with several types of ICT (e.g., Wikis, blogs)

• Important factor is the pedagogy not just the technology

Conclusion: Part 1

• Potential of learning technologies to enable new ways of teaching and learning

• New virtual and “serious game” technologies for new types of learning environments: learn by virtually doing

• Value of learning sciences theory such as analogical encoding to inform learning activities related to virtual worlds

Learning the Physics of Electricity with Agent-based

Models: Fail First and Structure Later?

Michael J. Jacobson

Centre for Research on Computer Supported Learning and Cognition (CoCo)The University of Sydney

Beaumie Kim, Suneeta Pathak, BaoHui Zhang

Learning Sciences Laboratory, National Institute of Education, Nanyang Technological University, Singapore

Failure and Learning

• Is this an oxymoron?

• Short discussions of 2-3

• Consider your own learning: did experiences of failure help or hurt?

• Consider teaching: would experiences of failure help or hurt students?

• Short group reports

Productive Failure?

• Most “traditional” instruction and many used in activities with learning technologies start with structured experiences before introducing students to low structure activities

• Start simple or “small” and build gradually in terms of conceptual complexity, knowledge, and skills

• Current learning theories are similar:

• Cognitive Apprenticeship of Allan Collins

• Expert models activity, learners are scaffolded doing activity, and gradually fade scaffolding

• Good goal: minimize frustration if learners fail

• But does this mean learning failures in low structure activities might not, under the right conditions, also lead to learning?

NetLogo Model of Ohm’s Law (V = I * R)

Method

• Secondary 4 students at all boys school in Singapore

• Productive Failure (PF) group: 16 dyads

• Non-Productive Failure (Non-PF): 16 dyads

• Used four different NetLogo models of electricity developed at Northwestern University, one per day for one hour sessions

Examples of PF vs. NPF Activities

PF Low Structure Initial Learning Activity

Run the NetLogo Model 3 to know the effect of voltage on current two wires joined in a particular pattern.

Answer the following question.

1. What is your observation about current in both the wires? Explain why it is so.

Non-PF High Structure Initial Learning Activity

Run the Model 3 NetLogo to know the effect of voltage on current in two wires joined in a particular pattern.

1. Move the sliders to the following values. i. Total electrons= 300ii. Collision rate with nuclei left =0.5iii. Collision rate with nuclei right=0.5

2. Fill in the following table.

Answer the following question.

1. What is your observation about current in both the wires? Explain why it is so.

Voltage Current in Left Wire

Current in Right Wire

0.5

1.0

1.5

Experimental Design

Treatment Group

Problem 1 Problem 2 Problem 3

Non-productive Failure (NPF)

High structure

Dyads

High structure

Dyads

Low structure Individuals

Productive Failure (PF)

Low structure

Dyads

High structure

Dyads

Low structure Individuals

• Significant difference on pretest with NPF > PF

• PF group significantly higher on assessments of declarative knowledge and open ended conceptual

Results

35

43.75

52.5

61.25

70

Pretest

PosttestPF: DeclarativeNPF: DeclarativePF: ConceptualNPF: Conceptual

Qualitative Findings

• Both groups liked using the NetLogo agent-based models

• Appreciated the representational value of the visualizations in the NetLogo models over textbook pictures and PowerPoint lectures

• However, very different subjective impressions of their actual learning by treatment groups

Interview with PF Student

Because some worksheets was not very specific-la. So we were discussing whether which of variable should be kept constant and which one is should be changing so that we can answer the question-la. So before we shift the sliders my partner and me will try to hypothesize, like oh this is directly related to that or in equal-proportion-to-that, so-we-try-to-slide. But in the end all our assumptions were wrong and then we couldn't find any relations so we keep shifting the sliders and then we were discussing, ah, why are the results are inconsistent with our assumptions and stuff.

Interview with Non-PF Student

We, ah, me and my partner asked [the teacher] to clarify some, clarify, some stuff about the model in question. I prefer that she teach. It's much more... It will clarify more things ... if she teaches.

Discussion - PF Group

• Highly significant learning gains for PF

• Initial failure in PF group resulted in relatively large activation of prior knowledge (including misconceptions)

• Subsequent structured problem solving with agent-based models provided feedback that helped students link to relevant prior knowledge and to construct enhanced understandings

• Preparation for future problem solving

Discussion - NPF Group

• NPF students failed to activate broad range of prior knowledge

• Worked rather “mechanically”

• Observed “learned helplessness”

• Did not observe preparation for future problem solving and learning

• Small scale initial study, but one consistent with other PF research by Kapur and earlier work by Bransford & Schwartz and VanLehn

Questions and Implications

• Argue activity trajectories of high-to-low structure followed by assessment are ubiquitous in education at all levels

• Missing opportunities to enhance learning by not providing high after low structure experiences?

• Discussion:

• Do we even need initially high structure experiences?

• Do we instead need an initial “zone of proximal failure?”

• Implications: potential for significant impact on curricula, assessment, and school practices generally

Advancing the Sciences of Learning Research: Reflections on the Future

• Need to advance theoretical perspectives on the pedagogical trajectories for learning

• Current presumptions in the field of the learning sciences:

• High-to-low structure trajectories

• Scaffold or structure learning first, then allow “doing” of problems or projects

• Extensive learning sciences research using such as approaches such as guided inquiry or cognitive apprenticeship

Theoretical and Pedagogical Challenge• Low-to-high structure pedagogical trajectories

• AERA 2010 symposium:

• To Structure or not to Structure? New Perspectives on Success and Failure in Learning

• Allan Collins as Chair and John Bransford as Discussant

• Believe theoretical and empirical progress in this area of fundamental practical significance as well as to advance theory

Conclusion

• Exciting time to be involved with research into the sciences of learning

• Innovative technologies have great potential to enable innovative pedagogical approaches

• Task for the field: advance theory and research into the why, what, and how of innovative pedagogies

Thanks you!

   Michael  J.  JacobsonProfessor  and  Chair  of  Educa1on

Co-­‐director,  Centre  for  Reseach  on  Computer  Supported  Learning  and  Cogni1on  (CoCo)

Deputy  Director,  Ins1tute  for  Innova1on  in  Science  and  Mathema1cs  Educa1on

Email:  mjacobson@usyd.edu.auWeb:  hIp://coco.edfac.usyd.edu.au/