Transcript lesson meta-cognition for learning

Approximate Plan of the Course
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21.4. Introduction
28.4. ActiveMath Vorstellung /Introduction to ActiveMath
12.5. Benutzermodellierung/user modeling
19.5. .instructional design
2.6. support of meta-cognition
9.6. XML knowledge representation, adaptive hypermedia
16.6. collaborative learning/ Lernen in Gruppen
23.6. diagnosis
30.6. action analysis
7.7 further topics (tutorial dialogues, mobile learning..)
14.7. student project reports
Source: Erica Melis
Learning by Constructing Knowledge
Interpreting information rather than recoding
KNOWLEDGE CONSTRUCTION
Make sense of information
Relate to preexisting knowledge
relate to personal experience
elaborate new information
restructure existing schemas
internal process -> self-regulate, -inspect
when, why, how?
Source: Erica Melis
What are Meta-Cognitive Activities?
When, why, how (cognitive, strategies and self)
• self-regulation
– planning and monitoring, evaluating problem solving
– planning and monitoring of learning
– Role in collaborative learning
• constructing relationships between concepts
• reflection
– Self-explanation of examples
– self-explanation of exercises
– analysis
• actively seeking help or information
Source: Erica Melis
How to Stimulate Meta-Cognition
• PROVIDE structure(s)
• REQUEST articulation of strategies and knowledge
• for self-explaining examples, exercises
– why is this step done?
– how does the step correspond to the plan?
• Is this solution finished? (for monitoring)
• compare the solutions!
• MAKE AWARE: erroneous examples
– Web!
• More in Wizard of Oz experiments
Source: Erica Melis
Problem solving: How to Solve it, George Polya
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Understand the problem
Plan a solution
Execute the plan, keep track of solution
Analyze whether it worked
Generalize solution
Bild von Buch und Polya
Source: Erica Melis
Open Learner Model
Source: Erica Melis
Andes: Sample instructional material
Problem
Statement
Worked out
solution
Situation
Diagram
Free Body
Diagram
Source: Erica Melis
SE-Coach of Andes
• User interface:
– workbench presents examples (PME, latency data)
– incrementally promts SE
– has tools for SE (browser, templates)
• student model (BN) for plan recognition and mastery
from reading, menu selection, template filling...
• BN includes model of correct SE with rules, action nodes
• adequate minimal help when?: if SE not performed and
– due to lack of attention, guide to example parts.
– If due to lack of knowledge, request SE
Source: Erica Melis
The Interface: Masking User Interface
• Helps students focus attention and SE-Coach monitor it
Source: Erica Melis
SE-Coach of Andes, Help
Self-explanation of examples
• Records time of reading steps of worked-out
example (latency data)
• If time too short and prediction of prerequisite
low then promt explanation menu (+support)
– Menus contain either prerequisite Newtonian
Physics laws or plan steps
Source: Erica Melis
SE-Coach of Andes: Help, promts
Solution step xxx
Self-explain:
This fact is true because...
The role of this fact in the solution plan is to ...
This choice is correct because...
The role of this choice in the solution plan is to...
Using the rule browser and plan browser
Source: Erica Melis
Prompts to Self-Explain
• Stimulate self-questioning on relevant explanations
Source: Erica Melis
SE-Coach Hints
Source: Erica Melis
Justify Solution Steps: Rule Browser
Source: Erica Melis
Identify Goal Structure - Plan Browser
• Encodes abstract solution plan
Source: Erica Melis
SE exercises in Geometry Explanation Tutor
self-explanation of exercises
• students explain steps in own words or name of
principle
• evaluate student‘s restricted input (correct? category?)
• helps through restricted dialogue to arrive at
mathematically precise explanation
• knowledge-based: hierarchy of 149 explanation
categories. For each rule
– one or more correct and complete ways to state
each geometry rule
– numerous incomplete or incorrect ways
Source: Erica Melis
Geometry Explanation Tutor,
example explanations
complete and correct
• Category:
complem-angles-sum-180
– the sum of the measures of
compl angles is 180 degrees
Source: Erica Melis
incomplete or incorrect
• Category:
complem-angles-sum-180
– compl angles are 180
degrees
Geometry Explanation Tutor, example
Source: Erica Melis
SciWise and ThinkerTools *
Meta-cognitive activities:
hypothesize
investigate
Formulate Question
Inquiry cycle
analyze
patterns
transfer
model
Source: Erica Melis
ThinkerTools: Form for Inquiry Learning
• Question
– which general topic chosen, Why?
– Which questions to investigate,Why?
• Hypothesize
– write down predictions, 2 different hypotheses
– explaination for hypothesis
• Investigate
– describe how you investigate, justify this way
– show data etc in table, graph,..
• Analyze
– describe patterns
– discuss (poss errors)
• Model
– summarize conclusions, relate to question
– how data support conclusion
Source: Erica Melis
Project outline
SciWise: reflective goal-driven inquiry
Task advisors
inquirer
presenter
questioner
hypothesizer
investigator
General advisors
assessor
analyzer
modeler
edvaluator
cognizer
Inventor
planner
reasoner
representer
socializer
collaborator
debator
mediator
communicator
advisors incl cognitive and social aspects
Source: Erica Melis
A Sequence of interactions with agents
• Student start research by consulting Task Advisor
• Get advice from General Purpose Advisor
• Work with system Developer Advisor (Modifier) to
try to improve General Purpose Advisor
Source: Erica Melis
Advisor Agent: Helena Hypothesizer
• Hi, here are some things I can do for you:
– (1) describe characteristics of a good hypothesis
– (2) suggest strategies for creating hyps and advisors
– (3) evaluate your hyps to see whether they need revision
...good strategy to start with...the Inventor might be asked
Source: Erica Melis
SciWise advisor: Ian Inventor
• So you need help coming up with ideas for your
hypotheses? I‘m the right advisor for that. I
know billions of ways to gernerate ideas. Pick
the strategy that best suits you
– fast and loose
– control freak.
Good choice! Fast and loose is my favorite
Relax and turn your mind loose.Think of as many ideas as
you can find in 5 minutes. The ideas can be crazy or
serious...
Source: Erica Melis
SciWise Agents‘ knowledge
BDI
agent knows: its expertise, goals when useful,
how to get more info, decide what to do, learn, other agents
agent has condition-action rules to control behaviour
IF another advisor recommends you THEN pop up
IF start THEN show examples of how others did this task
agent has knowledge base for advice and assistance
strategies for achieving goals (be inventive...)
which advisors can help for a problem
assessment criteria
examples of good and bad hypotheses
Source: Erica Melis
Mind Maps
Source: Erica Melis
Make Connections (project)
• between concepts in different contexts:
– Fraction: proportion – increase/decrease –
part-of
• between maths and real world problems
– Decimals: 2,50 = 2 euro + 50 cent
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2,5 = 2 hours + 30 minutes
Source: Erica Melis
‚IMPROVE method‘(coll mathematics)
– meta-cognitive prompts:
• what is the problem? Read aloud! Describe concepts
in own words! Which category of problems?
• differences /sim of this and other problems?
• Which strategy/principle is appropriate here? How
can the plan carried out? Why is the strategy
appropriate?
– explain reasoning during problem solving by
answering prompts
– when failed to solve or no agreement , then
show prompts
Source: Erica Melis
Erroneous Examples
Source: Erica Melis
Erroneous Examples, Bruchrechung
Source: Erica Melis
Protocols of Human Dialogs, 69 prompts
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Any thought about this sentence?
Do you want to say something about this?
Could you explain what you are thinking?
Could you explain the concept discussed in the
sentence?
Please explain what this sentence says
what do you think?
What could you learn from the paragraph?
Anything else?
Why?
How‘s that? ....
Source: Erica Melis
Wizard of Oz
• Write meta-cognitive prompts and actions on cards
• group: learner, tutor, protocol
• add actions and prompts later if necessary
• How to foster self-regulation?
• which types of meta-reasoning for problem solving/
for learning /for self-control?
• Which (prompts for) SE and other meta-cognition?
• does explicit meta-cognitive guidance help?
• which knowledge would an inventor agent need?
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• Which functionalities of erroneous examples?
Source: Erica Melis
Woz instructional design:Jörg,…
• Newtonean physics
• Arrage question with dependency
• Make student understand formula: diagams, real
world actions
• Provide more knowledge about gravity, electomagnetics,
• Tried to ask for similarities and differences,
dependencies
• Ask – don‘t tell (basic facts, support by
examples)
Source: Erica Melis