daniel schwartz stanford university interactivity and learning
TRANSCRIPT
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Daniel Schwartz
Stanford University
Interactivity and Learning
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I don’t mean to be vain, but does this really look like me?
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Interactivity and Learning
Some types of learning do not seem to be the sort of thing that the representations and interactivity of the computer will be particularly helpful for.
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Artificial Intelligence in Education Two main capacities of interest:
Social Interactivity AI techniques create socially inspired interactions
Learning in Formal Domains Math, science, and other things that can be well modeled.
Soon, there will be sufficient models of social interaction that we can help students learn valued social behaviors. (Many attempts are underway.)
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Goal of the talk Present some history of research on valued social
interactions – which is largely about motivation and attitude change.
Describe two dimensions of valued social interactions – which are largely about learning.
Present some suggestive research that my colleagues have done with teachable agents.
Discuss some academic history as to what counts as valuable learning.
Describe how modeling social interactions can yield new possibilities for valuable learning.
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Interactivity and Learning
Goals of social interaction research Enhancing social interaction for learning Teachable Agents Relevant Evidence using the Agents Goals of learning research Sweet Spot of Social Interaction and Learning
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Early research on social interaction For some, the goal of studying or controlling
interaction is to improve human interaction per se.
This includes the research on
cooperation that spawned
cooperative learning.
Interactivity
Valued Interactivity
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How to Create Productive Interactions?
Research in response to WWII The goal was conflict resolution and cooperation
Morton Deutsch, 1973 “I started my graduate career not long after Hiroshima
and Nagasaki, and my work in social psychology has been shadowed by the atomic cloud ever since. The efforts reported in this book reflect my continuing interest in contributing to the understanding of how to prevent destructive conflict and initiate cooperation.”
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Led to Educational Applications About motivation and attitude.
Content learning not the target so much as “attitude change” and “motivation management.”
Still, led to applications for learning math, science, reading.
Begins from assumption of potential conflict or withdrawal. Not such a bad assumption for many school settings (in U.S.).
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Applications to learning. If students work cooperatively, they might improve their learning.
Two key conditions from cooperation research: Mutual Interdependence Individual Accountability
Slavin’s (1996) meta-analysis on cooperative learning: MI or IA = +.07 effect size MI & IA = +.32 effect size
Unfortunately, only 25% of teachers who are trained implement both. (Antil et al., 1998).
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Shift to collaboration More recently, interest in collaboration.
Collaboration is cooperation in the absence of serious conflict.
Collaboration is a valued form of interaction. But, it does not necessarily mean people will learn well.
Barron et al. (1998) study…
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In summary The goal of much research is to promote valued
interactions per se. Exploration of motivations for positive interactions.
When borrowed by education, leads to a model: Motivation Valued Interactions ( Learning) The motivations are for interactions, and not learning
Need to understand valued interactions that directly “motivate” learning.
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OUTLINE HERE
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Valued Interactionsfor Learning
The umbrella of valued interactions (Deutsch) “A cooperative process is characterized by open
and honest communication of relevant information among participants. Each is interested in informing, and being informed by, the other.”
What conditions turn this into learning?
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Two dimensions
Incorporation of Ideas The degree to which participants’ ideas are taken up
by one another.
Initiative in Action The degree to which all participants’ can initiate
actions.
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High motivation when achieved. Take the example of conversation:
People like to talk.
People like to make the effort after shared meaning.
The effort to produce and share meaning. We want others to incorporate the ideas we initiate. We want to incorporate the ideas that others initiate (even if
just to disagree or elaborate).
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Positive Instances Think of an animated conversation…
The world drops away. Try to persuade others to understand and incorporate your
ideas. Listen to how they uptake your ideas and reflect them back. Listen to their ideas and reflect them back, combined with
your own ideas.
A talk is a slow version (stylized turn taking). Writing a journal article is a really slow version. Gossip is a favorite version.
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Negative Instances
No chance to take the initiative blocked from entering conversation told what to do or say listening with no prospect of action
No chance to incorporate your ideas talking to people who do not understand talking to people who cannot respond ignored during conversation
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Interactivity Space for Novice Learningin Motivating Collaborations
Self OtherMixed
Initiative(actions)
Self
Other
Merged
Inco
rpora
tion
(ideas)
Showing
WatchingCopying
Being Imitated
OptimalLearning
ForNovices
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Not just low motivation…also, low learning.
No mutual incorporation of ideas: Neither teacher nor student learns.
No sharing of the initiative: Neither teacher nor student learns.
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Examples of optimal region
Self OtherMixed
Initiative(actions)
Self
Other
Merged
Inco
rpora
tion
(ideas)
OptimalLearning
ForNovices
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Computer Technologies
Present some research relevant to the two dimensions of interactivity.
I’ll look at teaching as the example As last movie clip showed, not all teaching yields
ideal social interaction and learning. We use computers to help optimize the balance of
incorporation and initiative.
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Teachable Agents Learning By Teaching
Common wisdom people “really” learn when they teach.
Empirical findings Students who prepare to teach learn more than students who
prepare to take a test. (Bargh & Schul,1980; Biswas, et al., 2001)
Built computer agents that students teach A natural social interaction students know well
Teach – Test – Remediate
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Basic Teaching Interaction
Not machine induction; students must explicitly teach.
Students teach agent. Students uses visual representations to teach the agent.
Agent performs based on teaching. Generic AI algorithms draw inferences based on student teaching.
Students revise agent to do better. Based on agent performance student revises agent and own
knowledge.
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Betty: A Teachable Agent
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Extensions to TA paradigm
Students know they are not real people. We are more interested in enabling social
interactions that facilitate learning. The well-known teaching schema works well. Plus, once the basic interaction is developed,
they can be extended in numerous ways.
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Videogames(Kristen Blair)
Students teach agent to perform in game. Besides motivation and game leveling,
it enables a number of learning resources
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On-Line Homework Game Show(Paula Wellings)
Students can log on, chat, and do homework with whomever is on-line.
Teach agent, who performs in a gameshow.
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Front of the Class System(Joan Davis)
Students create models that can answer all questions, instead of memorizing a few answers to select problems.
Present results at front of class.
Gain Scores in Correct Answers for Concept Maps after Feedback and Revision
0%
5%
10%
15%
20%
25%
One (n=26) Two (n=16) Three-Four(n=18)
Seven-Eight(n=8)
Links Involved in Question
Incr
ease
in %
Cor
rect
Front-of-Class
Solitary
Feedback Condition
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A Suite of Homely Teachable Agent “Engines”
Betty Qualitative Reasoning
Orbo Reasoning by Assumption
Milo Reasoning by Model
Moby Hypothetico-Deductive Reasoning
J-Mole Reasoning by Discrepancy
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Also, a suite of homely collaborators
In order of homeliness: Gautam Biswas John Bransford Krittaya Leelawong, Thomas Katzleberger, Ying Bin Joan Davis, Kristen Blair, Paula Wellings, George Chang Girija Mittagunta, Elliot Castillo, Anh Huynh, Nancy Vye
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Return to proposal about valued interactions that promote learning.
Self OtherMixed
Initiative(actions)
Self
Other
Merged
Inco
rpora
tion
(ideas)
OptimalLearning
ForNovices
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Incorporation Agents, by design, merge ideas with students.
Students provide facts of the matter. Agent provides spatial representation and reasoning.
Not just learning the brute facts, learning how the “expert” thinks with those facts. Literally making thinking visible.
Hope is that merging with Betty’s representations and reasoning will lead student to learn and adopt those representations.
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Interactivity Space for Novice Learning
Student AgentMixed
Initiative(actions)
Student
Agent
Merged
Inco
rpora
tion
(ideas) Would students learn causal structure
when ideas get merged with agent?
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Merging Ideas
Undergraduates read exercise physiology text.
8 Taught Betty on cell metabolism. 8 Wrote Summary on cell metabolism.
Would students adopt Betty’s knowledge structure?
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Direction of Causality
During activity:
Betty students discovered
they had confused
causation and correlation.Mitochondria <-> ATP synthesis
Summary students tended to
focus on outlining0%
25%
50%
75%
100%
Betty Summary
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Multiple Causality
0
1
2
3
4
Betty Summary
Learning TaskC
ompl
ex L
inks
Given a metabolism word, list entities related to it.
Simple Link: Mitochondria increase ATP synthesis.
Complex Link: Mitochondria with glycogen or free fatty acids increase ATP synthesis.
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Dynamic aspect of thinking Merging is not just of representations, but also of
reasoning. Wanted to examine if the AI-reasoning component was
important for merging ideas ideas. 4th-grade students learned about pond ecology over
three days. Animation condition:
Taught Betty and she could answer their questions. No Animation condition:
Created concept map using Betty (reasoning turned off)
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Adoption of Causal Structure
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5
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Day 1 Day 2 Day 3
Nu
mb
er
of
Ca
us
al L
ink
s
No Animation
Animation
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Opportunities to commingle thoughts with agent helped students learn/adopt causal structure.
How about initiative?
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Interactivity Space for Novice Learning
Student AgentMixed
Initiative(actions)
Student
Agent
Merged
Inco
rpora
tion
(ideas)
Does mixing initiative with agent improve student learning?
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Independent Performance Conversation is often taken as model of social interaction
Mixed-initiative involves shared lead. A broader view extends interaction over time.
Mixed-initiative can include independent performance. Teaching and then watching one’s student perform.
Student incorporates the teacher’s ideas, but also has to have abilities to do an independent performance.
Swedish dissertation defenses? A very powerful form of mixed initiative.
Motivating Excellent opportunity for teacher to learn.
Examine value of mixed-initiative as independent performance with a second agent.
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Moby(Anh Huynh)
To teach science content using hypothetico-deductive reasoning.
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Green and Not Pink are Necessary for a Flower(Shade and ~ Sun are Necessary for a Flower)
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Significance of Independent Performance Study with 100+ high school seniors
Control Never used game
Play Played game themselves without teaching feature But did fill in “teaching rule” after each “win”
Teach Used game, filled in “teaching rule,” watched Moby play
Students reached same level in same time. Posttest of inductive-deductive reasoning
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0%
25%
50%
75%
100%
Induce Imply Translate
Reasoning Task
Acc
urac
yControl
Play
Teach
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25%
50%
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100%
Induce Imply Translate
Reasoning Task
Acc
urac
yControl
Play
Teach
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Induce Imply Translate
Reasoning Task
Acc
urac
yControl
Play
Teach
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Summary
Incorporation seems to help. Mixed-Initiative (as far as we went) helps.
Next is the question of what is valuable learning.
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Problem 1: Narrow definitions
Detterman from Transfer on Trial. “…most studies fail to find transfer …and those
studies claiming transfer can only be said to have found transfer by the most generous of criteria and would not meet the classical definition of transfer.”
Classic “stimulus generalization” view – efficient replication of old behavior in a new situation.
(Bransford & Schwartz, 1999, Review of Research in Education)
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The History of the Transfer Problem
Much of the psychological literature on learning has emphasized efficiency Faster and more accurate retrieval and
application of previously learned behaviors.
Efficiency’s long, dominant history in psychology and the USA…
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Efficiency is important
99.9% = failure for orchestral musician.
Improved efficiency frees up cognitive resources.
Important for routine tasks.
Most learning assessments are about efficiency Speed, accuracy, consistency, 1st-try positive transfer
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Issues with Efficiency Businesses worry that too much emphasis on efficiency
reduces innovation.
For novel learning, efficiency can interfere Assimilate to efficient schemas and miss what is new.
Novick finding of expert “letting go” at transfer.
Assessments of efficiency can miss early forms of knowledge that prepare students to learn.
Harvard students and seasons
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Innovation Innovation involves generation of new ideas
Rather than refinement of pre-existing ones.
Efficiency & innovation often seen as opposites. Myth of creative person versus drudge.
Adaptive experts are presumably high on both. A strong set of efficient schemas to draw upon.
Ericsson’s 10-year latency to innovation. Ability to “let go,” adapt, and learn new ideas when needed.
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Efficiency & Innovation
Efficiency
Innovation
Novice
AdaptiveExpert
Routine Expert
EternalNovice?
(Schwartz, Bransford, & Sears, in press)
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Optimal Trajectory for Learning?In
nova
tion
Efficiency
AdaptiveExpertise
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Problem 3: Restricted methods for assessing transfer
Sequestered problem solving assessments (SPS) Harvard students on the seasons.
Harvard(Treatment A)
High School(Treatment B)
Sequestered Transfer Assessments(Seasons)
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Teaching Method A(discovery-based)
Teaching Method B(direct instruction)
Target Transfer Problem
Learning Resource in Test(worked example)
CorrectSolutions
67%
33%
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Hypothesize that both are necessary and there is an optimal balance.
Inno
vati
on
Efficiency
AdaptiveExpertise
NoviceRoutineExpertise
FrustratedNovice?
Optimal A
daptabilit
y Corri
dor
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- Help students learn to use resources for learning.
- Put Betty in larger instructional model.
- Develop intelligent tutor and coach.
Value of Teaching Agents for Future Learning
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The mentor provided tips on:(i) How to be a better teacher,(ii) How to be a better learner, (iii) Help on domain concepts.
The Mentor Agent
Here, the mentor responds to a student query about decomposition. Rather than give a direct answer, the mentor suggests what material the student should look up.
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Preparation for Future Learning 5th-graders
Taught Betty about the Oxygen Cycle. Tutored how to create Oxygen Cycle.
On posttest on Oxygen Cycle, the groups looked the same.
Returned 7 weeks later. Asked children to learn about Nitrogen Cycle and create a concept map.
Quality of Learning about Nitrogen Cycle,Weeks after Completing Intervention
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Good Nodes Good Links
Teach
Tutored
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A more nuanced view
Interactivity Learning
Valued Interactivity
Valued Learning
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OUTLINE HERE
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Limitations Limited value in feedback:
Betty simply answers. Need some way to evaluate whether an answer makes sense.
Limited resources for learning: Typically text resources on the side. Outside resources, not directly linked to agent interaction
Limited expressiveness: Betty does not use conditional logic, temporal cycles
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Most motivation theories do not entail learning with understanding
Extrinsic Reward External reinforcement
Achievement motivation Motivates any sort of change
Intrinsic Reward “Desirable” internal state
Flow Motivates to maintain state
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Motivation to Learn
Want a more specific motivation -- to continue learning with understanding
Some common proposals: Curiosity
Depends too much on environment Disequilibrium
Not something many people strive for
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An Example of Motivation to Learn with Understanding
w/ Jay Pfaffman (at UT Knoxville)
Surveyed 100’s of hobbyists Motor cycle racers, beer brewers, musicians, etc.
People self-select into hobbies so motivating
They rated importance of different aspects of their hobby.
Five classes of questions (with 5 items each)
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Sample Survey Item Types
How important is X for why you do your hobby?
Extrinsic Motivators – increasing success
Intrinsic Motivators – losing a sense of time
Social Motivators – belonging to a group
Learning Motivators – learning the history of my hobby
Production Motivators – being creative
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Three Lowest Motivations
(1 is low, 7 is high)
2.7 – To Increase Success
2.8 – To Improve Stature
3.2 – To Be Liked
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Highest Motivations
6.0 – See the “fruits” of their labor6.0 – Learn new methods to create products5.7 – Just manageable challenges5.6 – End in itself5.5 – Share artifacts/performances (products)
Asked high school students about their favorite classes and found similar results.
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Learning is “built-in” to the hobby motivation
Personal feedback from the fruits of their labor. Reflect on externalized products and improve.
Social feedback and new possibilities through sharing Other people’s responses, variations, and products.
Consult resources to make new artifacts/performances. What we typically think of as learning behaviors.
Increasing challenges create more needs for learning
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When do we choose collaboration to learn?
Education often misses this question. Students are told to collaborate We orchestrate various structures:
Rewards Scripts and norms Interdependency
Consider when collaborative learning intent naturally appears.
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Outline Where learning is integral to motivation.
An example from hobbyists Is learning integral to collaborative motivations?
A production by efficiency task space.
Sample of research in the task space.
Addressing the assigned questions.
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Outline Where learning is integral to motivation.
Is learning integral to collaborative motivations? When people make the effort to share meaning.
A production by efficiency task space.
Sample of research in the task space.
Addressing the assigned questions.
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Motivation to share ideas something like hobbies
See the products of their labor See own ideas appear in other person
Sharing Learn from other’s responses and additions
Consult resources to make new products Recruit evidence (sometimes) to make a point
Increasing challenges Reply of other person can create a learning challenge
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Outline Where learning is integral to motivation.
Is learning integral to collaborative motivations?
A production by efficiency task space. Tasks with productive agency lead to sharing.
Sample of research in the task space.
Addressing the assigned questions.
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Motivations to Collaborate:Production Productive Agency: The opportunity to produce own ideas
plus share meaning with those ideas. Hobby productions often in material form Social productions often in verbal form
Not a welfare model of learning. Not about just taking in the wealth of knowledge from an expert.
About the means of production. Do learners get to produce ideas (not just construct)? Do they have material and intellectual resources?
Plus opportunity to share the productions (not ownership). Do ideas get taken up and reflected back with variation?
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Motivations to Collaborate:Efficient Performance Standard answers often involve efficiency.
Too much for one person. Error checking. More ideas to sample from.
Research often examines efficiency. Are two heads better (more efficient) than one? Measures of the efficiency of information exchange.
School often emphasizes efficiency motivations Proximal task: solve a problem without error and for a grade. Learning is a side effect, not a motivation.
Over emphasis on efficiency may interfere with effort to share meaning because there is little original production.
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Collaborative Task Space
Efficiency Tasks(speed, accuracy)
Production Tasks(invention, authoring)
Brain Storming
Invent/DesignSolutions
ImplementProcedure
InsightProblems
Which task regions naturally lead people to want to share meaning and learn through collaboration?
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Individuals & Dyads on Production Task
Received Passage:
When either the Spotted Halluck or the Black Froling live in a lake, the lake has weeds. However, the Spotted Halluck needs a lake with trees around it, while the Black Froling needs a lake with minnows and a sandy bottom….
Told to invent a representation that would help answer questions like:
What fish need weeds and trees?
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A Surprising Outcome of Effort to Share Meaningin a Production Task
Individual rate of abstract production = 6%
Actual rate of dyad abstract production = 67%
Emergence of abstraction replicated for several production tasks. Abstraction not the goal of collaboration – it naturally emerged. Shared meanings yield an abstraction of common structure.
MOST COMPETENT MEMBER MODEL A & B: .06 x .06 = .0036 A & ~B: .06 x .94 = .0564~A & B: .94 x .06 = .0564 Dyad solution odds = .1164 = 11.6%
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Hard to Imagine for an Efficiency-only Task
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Effects of Collaboration on Weight Pulling
Number of People Pulling Together
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FoundIdeal
Effects of Collaboration on Weight Pulling
Number of People Jointly Pulling
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Number of People Jointly Pulling
+100 (each person)
-20 (each link)
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Collaborative Task Space
Efficiency Tasks(speed, accuracy)
Production Tasks(invention, authoring)
Brain Storming
Invent/DesignSolutions
ImplementProcedure
InsightProblems
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Outline Where learning is integral to motivation.
Is learning integral to collaborative motivations?
A production by efficiency task space.
Sample of research in the task space. Efficiency vs. sharing interactions: Effects on learning.
Addressing the assigned questions.
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What Conditions Promote aCollaborative Learning Intent?
Requires both efficiency and production. Production and sharing are motivating Need some efficiency of knowledge and constraint to
avoid floundering. People rated “learning new methods” highly in hobby study.
A Hypothesis Degree a task emphasizes efficiency and/or production
determines collaborative intent towards learning.
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An initial example of what research might look like.
Efficiency Tasks(speed, accuracy)
Production Tasks(invention, authoring)
Brain Storming
Invent/DesignSolutions
ImplementProcedure
InsightProblems
versus
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Telling differences in collaborative interactions.
Looked at how pairs used resources
Looked at differences in interactions Dedicated to efficiency
Discussions about how to organize work Partitioning of labor
Dedicated to sharing meaning Evaluating one another’s ideas Constructing shared, “content-based” beliefs
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A Production Pair – Sharing Jointly evaluated each entry into the concept map
“That’s true what you just said, arterial plaque increases risk of heart disease.”
Even jointly challenged experimenter over a specific relation
Started finishing one another’s sentences
Repeatedly consulted passage and compared to map.
Relatively few discussions about roles in task.
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An Efficiency Pair – Partitioning Negotiated separate roles
S1: “Why don’t I write down the links?”S2: “As long as you don’t mind being the scribe and don’t
mind me being the speaker.”
Read out – Copy strategy
When studying in remaining time Worked separately at first Then split passage and quizzed each other
“Shared” meaning typically occurred outside of task
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Our hypothesis for future research Different types of collaborative learning
interactions for production and efficiency tasks. Production tasks generate effort to share meaning.
Good if the goal is new learning. Production pairs seemed to learn better.
Maybe not so good if goal is efficiency. Production pairs took longer.
Efficiency tasks lead to partitioning of work. Good if the goal is increasing efficiency. Maybe not so good if the goal is new learning.
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Outline Where learning is integral to motivation.
An example from hobbyists Is learning integral to collaborative motivations?
When people make the effort to share meaning. A production by efficiency task space.
Tasks with productive agency lead to sharing. Sample of research in the task space.
Efficiency vs. sharing interactions: Effects on learning. Addressing the assigned questions.
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Assigned Questions What has been under studied?
Why people choose to collaborate. How different motivations affect self-organization of learning.
What sorts of research designs? Manipulations of task space (or natural observations). Longer lasting studies that permit mobility among groups.
What sorts of measures? Choice of whether to collaborate throughout. Do different process markers predict successful collaboration?
Effort to share meaning in productive tasks. Partitioning of workload in efficiency tasks.
Subsequent effects on future collaboration and learning.
[email protected]@Stanford.Edu
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Productive Task Space Works for Instruction? Pfaffman tried.
Create a simulation Change values Watch simulation
Measured Learning resource use Spontaneous sharing Overall learning
Results If he recovers from study…
Level of Production
inventiontasks
rhetoricaltasks
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Different forms of collaboration Procedure
Dyads read passage on fat metabolism and heart attack. Received nodes that represent key elements. Made a concept map of relations between elements.
(Olive oil increases HDL; HDL decreases LDL; etc.) Complete a posttest working alone.
Conditions Implement dyads: Received written list of pairwise relations. Produce dyads: Created own way to connect nodes.
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Effects of different collaborative conditions on interactions
Approximately the same number of interactions. Implement dyads partitioned labor
“Handed-off” knowledge to one another Produce dyads discussed ideas.
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# of Turns Taken on a Causal Relation
Fre
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Produce
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Effects of different collaborative conditionson learning
Sears mapped each moment-to-moment interaction with its learning outcome as measured by a posttest.
Examined mutual learning (different from mutual understanding).
Mutual Learning as a Function of Turns Taken
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Number of Turns Taken per Item During Learning
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