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Lecture 7: Building and Exploring Domain Tools

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Title: Lecture 7: Building and Exploring Domain Tools


1
Lecture 7 Building and Exploring Domain Tools
2
Contents
  • Building Domain Models
  • LOGO experience - model building
  • Intermodeller
  • Conception
  • Simulation Authoring Tools RIDES
  • Pedagogical Agents

3
1. Building Domain Models
4
Learning through Model Building
  • Assumes learner is active and seeking stimulation
  • Making knowledge explicit
  • get learner to communicate beliefs
  • get learner to model theories and test them
  • get learner to reflect on learning
  • Learning through confrontation
  • student has belief of what happens in environment
  • tests belief -gt consequences in environment
  • if consequences don't match belief, then (hope)
  • cause student to review belief (learn)
  • But, are all confrontations beneficial?

5
Quote from Mark Elsom-Cook
  • r.e. discovery learning environments in general
  • The process of externalisation making explicit
    the learner's model of the educational setting
    makes a concrete representation of the internal
    processes of the learner.
  • By seeing the external form of the predictions
    made by these processes, the pupil realises the
    limitations of the external representation,
    repairs it and, in the process, repairs his/her
    internal representation.

6
Some Advantages
  • Student sets goals, agenda, and structures
    learning themselves - own v. imposed goals
  • Structure provided by environment has to be
    non-intrusive, but supportive
  • Allows learner to directly experience behaviour
    inaccessible in real world
  • Self-directed learner --gt interaction --gt
    stimulation
  • Learner is actively engaged in developing and
    testing theories (echoes child-centred/progressive
    education claims)

7
A distinction
  • A. Simulations, Exploratory Environments,
    Micro-worlds
  • the investigation of views of a given domain,
    which may differ from the learner's own
  • B. Expressive or Modelling Environments
  • the modelling of user's own beliefs, and
    reflecting on and exploring own models

8
2. The LOGO Experience
9
Model Building in LOGO
  • Allows student to explore their own models by
    building programs written in the programming
    language LOGO
  • LOGO - Feurzeig and Papert (1969)
  • - based on LISP
  • - provides language for describing procedures
  • Piaget
  • "If you want a child to learn, begin by taking
    something he already knows, and use it as a
    framework within which new learning takes place."

10
Turtles and Button Boxes
  • So, start with body awareness and relate body
    movements to those of a small robot.
  • Control the robot (turtle) with commands - see
    effect of drawing device attached.
  • If doesn't do what expect, try to act out what it
    does do and correct it.
  • slides of Turtle and Button Box, figures 1.1 and
    1.2 from O'Shea and Self

11
LOGO uses
  • Subjects explored through problem solving
    include
  • programming
  • maths
  • Language
  • physics
  • Child learns the language and also learns
    problem-solving skills such as problem
    decomposition in the course of writing
    procedures.

12
Claims and Problems
  • Claims
  • student gets a problem solving methodology
  • generalisable to other problems.
  • Problems
  • Syntax of LOGO - language may be harder than
  • domain to be taught
  • What is the goal?
  • Does it transfer?
  • Evaluation
  • no clear evidence of generalisable problem
    solving skills through LOGO
  • some evidence of improved learning in the domains
    where LOGO used

13
Issues about how used
  • PAPERT
  • if integrate in existing class this may
    prejudice any success
  • take whole new approach to curriculum with it
  • only need student and environment
  • V. EDINBURGH
  • need for some structure to support it - can't
    just say "explore, learn!"
  • need additional guidance too

14
However.
  • Not all environments equally suited for all
    learning
  • a design may facilitate one model
  • but be incompatible with another
  • (e.g. physics models, Newtonian v. Relativity),
  • therefore make different predictions for
    different models
  • Should environment suit all users?
  • Build so that can hold onto bits of the
    environment and develop better models from them.
  • Tools must be meaningful to learner
  • turtle geometry assumes can relate movements to
    motor skills.

15
3. Intermodeller
16
Expert System Shells
  • Can be used to build knowledge models
  • But commercial ones not suitable for schools
  • Various rule based shells developed for schools
  • But used mostly in technology classes - not as
    modelling tools in the wider curriculum
  • Led to research by Conlon on why, and how tools
    might be improved to make them both useful and
    usable

17
INTERMODELLER
  • a computer program intended to support children
    in building classification models
  • can be in many domains a child who is learning
    about spiders, dinosaurs or planets can build a
    model of his or her developing knowledge of the
    domain
  • models once constructed can be run as small-scale
    expert systems that perform interactive
    classification
  • models can be demonstrated and discussed, shared
    as files across networks, pasted into word
    processors and graphics programs, printed out to
    create classroom display material.

18
Conlon, 1999
  • .. effective support for constructive thinking
    in classification can be provided by a
    model-building environment in which learners
    create software representations (or models) of
    classification structures. The environment
    provides editors for building these models and an
    interpreter which can run any model as an
    interactive classifier.

19
Interchangeable Representations
  • Classification trees organise classes or
    categories hierarchically, with arcs representing
    subclass relationships
  • Decision trees flowchart-like representations
    which use a branching structure of questions and
    answers to distinguish between categories
  • Decision (factor) tables category named in the
    right column is defined by a row specifying
    features as values of attributes named on the
    header row
  • Rules statements of if/then relationships
  • Graphical tools for Knowledge Base development

20
Screenshot of Intermodeller
21
Model building methods
  • The methodology comprises the following seven
    steps
  • Decide on the purpose of the model
  • Identify decision factors (factors used to
    distinguish different categories)
  • Select a form of representation
  • Review the design
  • Start the model
  • Develop the model
  • Reflect and evaluate

22
Functionality
  • Learner can switch between representations
  • a knowledge base can be switched between
    representational forms automatically.
  • In-built machine-learning
  • models can be automatically slimmed to improve
    efficiency (uses ACLS induction).
  • Full expert-system style runtime features
  • how and why explanation
  • certainty handling
  • consultation review

23
Evaluation of modelling course
  • 82 children, 15 years old, c. 8 hours of class
    time.
  • 632 models - found that rule models not as high
    in quality (by indices of correctness,
    efficiency, and conciseness) as those built using
    the alternative factor table, decision tree and
    classification tree representations.
  • Questionnaire responses
  • children least enjoyed working with rules.
  • Children's ability to construct representations
    of classification improved significantly as a
    result of the modelling course.
  • (Say more in relation to methodology later)

24
4. Conception
25
Using Multiple Representations
  • Users with a repertoire of representational
    skills can describe, reason with and build models
    of information.
  • Constructivist learning theory stresses
    relationship between building internal mental
    models and building external information models
    (Cox Brna 1995).
  • Cox Brna showed that when people reach an
    impasse or become 'stuck' while solving a
    problem, the ability to reformulate the problem
    using a different type of representation can be
    an effective way of making progress.
  • By making thinking visible, the process of
    constructing an information map can help to
    understand, refine and communicate ideas.

26
Information Maps as Classroom Tools
  • Used by teachers to communicate information
  • Used by pupils to learn subject matter.
  • Building information maps improves generic
    representational techniques and transferable
    thinking skills.
  • eg.1 creating an argument map about transport
    policy - learn that argument can be understood as
    a hierarchical structure of claims,
    justifications and objections.
  • eg.2 creating a decision map about the budget -
  • learn that decision-making process can be
    understood in terms of options, factors and
    evaluations.

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Argument mapping
  • Informal discussion this involves learners
    working in twos or threes around a computer,
    debating a main claim while simultaneously
    constructing an argument map.
  • Preparation for formal classroom debates
  • Planning an essay
  • Reporting on research

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Map types and applications
32
Concept mapping by learners
  • Concept mapping activities by learners can be
    placed into three categories
  • Tabula rasa ('blank slate') mapping involves the
    creation of a map from scratch
  • Scaffolded mapping tasks elements of the map are
    provided by the teacher, leaving the learner to
    supply the rest.
  • Buggy map correction tasks present learners with
    concept maps containing deliberately introduced
    bugs (errors). Learners task is to locate and
    correct them.

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5. Simulation Authoring Tools RIDES
37
IMTS, RAPIDS and RIDES
  • Munro et al, 1997 University of Southern
    California
  • IMTS (Towne and Munro, 1988, 1992)
  • tools for authoring interactive graphical
    simulations of electrical and hydraulic systems
  • supported troubleshooting assistance but could
    not deliver other kinds of instruction
  • RAPIDS (Towne and Munro, 1991) and RAPIDS II
  • (Coller, Pizzini, Wogulis, Munro, and Towne,
    1991)
  • direct manipulation authoring of instructional
    content in the context of graphical simulations
  • no low level control over instructional
    presentations
  • Both only available on specialized AI
    workstations
  • RIDES more robust, less constrained, simulation
    authoring tools and instructional editing
    facilities

38
RIDES
  • Integrated software environment for computer
    based tutorial instruction and practice
  • Uses interactive graphical simulations of devices
    or other complex systems
  • Students explore graphical simulations.
  • Build graphical models by fetching library
    objects and pasting them into scenes.
  • Draw objects directly onto the scenes
  • Behavior of objects specified by authoring rules
    that control the value of object attributes.

39
RIDES Course
  • A course a set of learning objectives that must
    be realized by a student
  • each associated with a lesson for teaching it
  • student model based on course objectives
  • what to present next controlled by relationships
    between course objectives and student model
  • Instruction author can 'record' a procedure that
    students must learn, by carrying out the
    procedure.
  • During training, the student is prompted to carry
    out the correct sequence of actions.

40
Claimed advantages
  • For the student
  • More robust, more realistic simulations
  • Support for free play immersive learning
  • For the developer and the manager
  • Potential for the reuse of objects
  • Ease in developing more robust, flexible, and
    realistic simulations

41
  • Graphical tutor, simulates behaviour of human
    circulatory system, by Carol Horwitz at
    Armstrong Laboratory, USAF.

42
Control panels for ground support unit for
maintenance of the B-2 bomber, by Stephanie
Perdomo, Northrop Grumman Corporation
43
Pulse oximeter, for monitoring patient's pulse
and blood oxygen, Carol Horwitz, Armstrong
Laboratory, USAF.
44
Diesel engine, Michael Crumm, Armstrong Lab,
USAF.
45
Pedagogical Agents
  • Steve agent cohabits a 3D simulated mockup of a
    student's (complex) real work environment (US
    Navy ship).
  • The agent can demonstrate physical tasks, such as
    operation and repair of equipment.
  • Students can learn and practice skills in a
    virtual world
  • (Rickel and Johnson, 99)

46
Steve and another agent interacting
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Trends in Domain Modelling
  • Multiple representations of Domain Knowledge
  • Simulations
  • Simulation Authoring Tools
  • Further Use of Qualitative Reasoning Methods
  • Empirically Informed Design
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