into the pedagogical woods david wiley, ph.d. dept. of instructional technology utah state...
TRANSCRIPT
March 12, 2002 David Wiley, Utah State University 2
Overview
LODAS Scope / grain size Sequence / combination
OSOSS
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LODAS
Learning Object Design and Sequencing Theory
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LODAS - Background
Synthesis of van Merriënboer’s 4C/ID Reigeluth’s Elaboration Theory Gibbons et al.’s Work Model Synthesis Bunderson’s et al.’s Domain Theory
Pedagogy Problem and activity centered “Instruction” plays supporting role
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Grain Size
How big is big enough?
How big is too big?
How big should my learning objects be?
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Multigrain
“What’s the right size?” Naïve Misleading
Multiple levels are both practical and ideal
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Synthesize Work Models
Traditional task/job analysis Low-level objectives
Partially recombine objectives Actionable description of valuable
performances
Top level LO is a “Work Model”
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Hollowness on Two Levels
Work Model is only a specification / design construct
Determine simplifying conditions, simplest real-world case (epitome)
Several work model re-statements (SC)
Each mid-level LO is a “Case Type”
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Fundamental Elements
Case Type is another specification
Create several Specific Problems based on each Case Type
“Specific Problems” are the actual learning activities with which learners interact
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Scope Review
Work Model – Dark Green
Case Types – Mint Green
Specific Problems – White
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Sequencing / Combining Objects
What’s the right sequence?
When LO=low-level objective, sequence=prereq hierarchy/tree
But when LO=multigrained, multiple sequences strategies are necessary
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Multigrain Sequencing
Work Models: simple to complex
Case Types: elaboration order
Specific Problems: random
Is it really that simple?
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Multidimensional Multigrain
Simple to complex assumes comparison on a number line
Numeric values assignable to WMs using IRT techniques
Do all WMs belong on the same number line?
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Example
Language learning domain (Strong-Krause, 2001) Vocabulary
Reading Writing Speaking Listening
Assuming uni-d when your data is multi-d makes for yucky analyses
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Charting Domain Dimensionality
First draft Lit reviews and practice-based hunches
Iterative drafts Data driven (factor analysis, smallest
space analysis)
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Splitting Your Multigrains
WMs must be assigned to a uni-d number line
SC ordering along common uni-d
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Catch Your Breath
WMs, Case Types, Specific Problems
Multidimentionality of domain
Each WM associated with appropriate uni-d
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Period.
Without this kind of rigorous domain mapping process our scope and sequence decisions are guesses.
Period.
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A Real World Example
Beginning undergraduate music theory
Wiley & Welch (2001)
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Scope Decisions
Identify or construct scales given key signatures Major minor
Identify or construct key signatures Major minor
Identify or construct triads Major minor diminished augmented
Hear and write intervals Take melodic dictation
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Dimensionality Guesses
Cognitive skills
Aural Skills
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Difficulty Guesses
1. Scales2. Key Signatures3. Chords
Case Types ordered as elaborated
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Dimensionality Results
Three (not two) uni-ds Cognitive
Basic Advanced
Aural
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Practical Results
The traditional sequence does not fit student growth models
An integrative (problem-based) approach would fit better
Britney Spears, Protestant Hymn, Bach Chorale
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LO Taxonomy
Single Combined-closed Combined-open Generative-presentation Generative-instructional
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<whine>It’s Complicated!</whine>
and expensive and will never fit into our ID process!
Data gathering: 10 minutes Data entry & scrubbing: 2 hours Data analysis: 4 hours
Id est, about one day.
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“It’s the context, stupid.”
Individual LOs are decontextualized
To do ID is to contextualize
Is juxtaposition/sequencing of LOs enough?
If not, where do we get context?
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1. Integrative Info at the WM Level
Design additional context-linked SCOs to Intro the Work Model
Design additional context-linked SCOs to Summarize the Work Model with assessments
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2. Cross uni-d Work Models
Most models (Cisco) assume domain uni-d-ness
Additional integrative WMs will have to “vertically integrate” across several uni-ds
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It’s Possible Now!
Work Model as Aggregation Case Type as SCO Specific Problem (CT/GI) as Asset
SCORM 1.2 already gives us:1. Tree-based sequencing2. Mastery-determined advancement
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Daves’ Directions
Merrill Cognitive Psychology paradigm Information Processing Model Single-processor!
Wiley Social Constructivist paradigm Grid / Distributed Processing Model
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Distributed Computing (P2P)
Cycle sharing / distributed computation SETI@home Genome@home Distributed.net
File sharing / distributed storage Napster GNUtella/LimeWire/BearShare Morpheus/Kazaa/FastTrack
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Distributed Comparison
Cycle sharing / distributed comp
File sharing / distributed storage
Collaborative learning strategies
Distributed expertise / resource sharing
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OSOSS
Online self-organizing social system
“Osu” -- “What’s up?” in Japanese
Wiley & Edwards (2002)
NSF CAREER grant
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Characteristics
Very large (30,000+) Increasingly (fully) decentralized
No omniscient expert Increasingly democratic
All voices initially equal Peer feedback (review) critical role
Bio self-org (pheromonal, stygmergy)
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OSOSS Pedagogy / SCORM?
Collaborative Problem-Solving (Nelson, 1999)
Reusable instructional resources1. Catalyze/crystalize CPS process2. Slurping OSOSS exchanges as SCO
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6 Degrees of Separation
Church & State/Strategy & Content?
Content is generally inert
Human beings and authentic probs Strategies Contextual glue
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Innovation?
Resource queries without metadata
Resource reuse without digital libraries
Scalable learning support rich with human interaction
Collaborative problem solving around authentic problems