jan. 20, 2006 patterns in education 1 theodore frick department of instructional systems technology...
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Jan. 20, 2006 Patterns in Education 1
Patterns in Education
Theodore Frick
Department of Instructional Systems TechnologySchool of EducationIndiana University Bloomington
Jan. 20, 2006 Patterns in Education 2
Familiar Patterns: Temporal
Darkness at night, stars shiningDawnSunriseDaytime, sun moves east to westSunsetDuskDarkness…
Jan. 20, 2006 Patterns in Education 3
Familiar Patterns: Temporal
SpringSummerFallWinter
Jan. 20, 2006 Patterns in Education 4
Familiar Patterns: Structural
Geographical relation: Bloomington is located in southern Indiana on the
North American continent. Bloomington is south of Indianapolis.
Organizational relation: Gerardo Gonzalez is University Dean of the School
of Education who directs and supervises: Peter Kloosterman, Executive Associate Dean, SoE, IUB
campus Khaula Murtahda, Executive Associate Dean, SoE, IUPUI
campus (see org chart)
Jan. 20, 2006 Patterns in Education 5
Familiar Patterns: Structural
Familial relation: Philip and Irma Frick are the parents of Theodore
Frick William and Helen Brophy are the parents of
Kathleen Brophy Kathleen Brophy Frick and Theodore Frick are the
parents of Benjamin Brophy Frick Instructional relation:
During fall semester, 2005,T. Frick was the R690 instructor of:
Andrew Barrett, Omer Delialioglu, Shyamasri Gosh, Nicole Harlin, Jamison Judd, Sunnie Lee, Emmanuel Okafor, Uvsh Purev, Chris Ryan, Theano Yerasimou
Jan. 20, 2006 Patterns in Education 6
A pattern is a relation
General form of a relation:
Jan. 20, 2006 Patterns in Education 7
Temporal & Structural Patterns & Logical Relations
Temporal PatternsA precedes B A co-occurs with B
Structural Patterns or ConfigurationsA affect relation B
Logical RelationsA implies BA is equivalent to B
Jan. 20, 2006 Patterns in Education 8
Pattern Examples: Temporal
Solicit > Respond > React (Bellack, et al., 1966)
Mildly handicapped students are 13 times more likely to be non-engaged during non-direct instruction than during direct instruction (Frick, 1990)
Heavy cigarette smokers are 5-10 times more likely to have lung cancer later in their lives than non-smokers (Kumar, Abbas & Fausto, 2005)
Jan. 20, 2006 Patterns in Education 9
Pattern Examples: Structural
Affect relation: guides research of
Faculty Person 1
Faculty Person 2
Student 1Student 2
Student 3
Student 4 Student 5
Old IST Ph.D. structure
Jan. 20, 2006 Patterns in Education 10
Pattern Examples: Structural
Affect relation: guides research of
Faculty Person 1
Faculty Person 2
Student 1Student 2
Student 3
Student 4 Student 5
New IST Ph.D. structure
Jan. 20, 2006 Patterns in Education 11
Logical Implications in a Formal Theory
Thompson (2005): Axiomatic Theory of Intentional Systems (ATIS), examples of axioms and theorems (logical implications):
If system input decreases, then filtration increases. If system filtration increases, then adaptability increases. If system strongness increases, then hierarchical order
decreases. If system strongness increases, then flexibility increases. If system strongness increases, then input increases. If system strongness increases, then filtration decreases.
Jan. 20, 2006 Patterns in Education 12
Verifying Systems Theory
The systems theory consists axioms and theorems for making predictions
Axioms and theorems consist of dynamic and structural properties
APT&C can be used as a verification methodology: Analysis of Patterns in Time & Configuration
Jan. 20, 2006 Patterns in Education 13
Using Theoretical Predictions
We can use theoretical predictions to make practical decisions, e.g., Not smoke, to reduce chances of lung
cancer later in life. Provide direct instruction to increase
chances of elementary student engagement in learning activities.
Take umbrella if rain is predicted to be highly likely.
We can use predictions without cause-and-effect explanations.
Jan. 20, 2006 Patterns in Education 14
Imagine for the moment…
We have a valid educational systems theory that:Can predict education systems outcomes
based on current conditions (PESO), and Is based on empirically verified temporal
patterns and configurations in systems
Jan. 20, 2006 Patterns in Education 15
NCLB Example
To make this more concrete, consider the following scenario:
Smithtown School #9 failed to achieve state standards for No Child Left Behind (NCLB)
Jan. 20, 2006 Patterns in Education 16
SMITHTOWN SCHOOL #9
Parents start transferring children to other schools
Scenario
Jan. 20, 2006 Patterns in Education 17
Predictions – Axiom 13
Then filtration increases
NCLB rating deters enrollmentEnrollment falls
If input decreases
Year 1 Year 2 Year 3
SMITHTOWN SCHOOL #9
This is a FAILING school. Tommy shouldn’t enroll here!
Jan. 20, 2006 Patterns in Education 18
Predictions – Axiom 11
Then storeput decreases
Fewer students attending classesEnrollment falls
If input decreases
Year 1 Year 2 Year 3
Jan. 20, 2006 Patterns in Education 19
Predictions – Axiom 10
Then fromput decreases
Fewer students to graduate
ADMINISTRATIONOFFICE
Hmm…there aren’t as many diplomas to print this year!
Enrollment falls
If input decreases
Year 1 Year 2 Year 3
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Predictions – Axiom 16
Then feedout decreases
Fewer graduatesEnrollment falls
If input decreases
Year 1 Year 2 Year 3
Jan. 20, 2006 Patterns in Education 21
SMITHTOWN SCHOOL #9 BOARD MEETING AGENDA:
How to improve achievement scores?
Predictions – Axiom 28
If filtration increases Then adaptability increases
Smithtown adapts tomaintain system stability
SMITHTOWN SCHOOL #9
NCLB rating deters enrollment
This is a FAILING school. Tommy shouldn’t enroll here!
Jan. 20, 2006 Patterns in Education 22
Using PESO with Smithtown’s adaptation strategies
How can Smithtown adapt? Change the structure – i.e., the affect relations.
What if Smithtown increases STRONGNESS of affect relations that are of type: guidance of learning?
Jan. 20, 2006 Patterns in Education 23
Smithtown’s proposed strategy
Increase avenues of instruction through:Teaching aidesPeer tutoring Instructional technology e.g. using e-
Learning software
Increase strongness
Jan. 20, 2006 Patterns in Education 24
Predictions – Axiom 56
If strongness increases Then hierarchical order decreases
After: Less focus on teacher as guide of learning.
GUIDE
GU
IDE
GU
IDE
Teaching aides
Teachers
E-learningsoftware
Peer tutoring
More ‘guidance of learning’ connections for students
GUIDE
GUIDEGUIDE
Before: Teacher is main guide.
Jan. 20, 2006 Patterns in Education 25
Predictions – Axiom 55
Then flexibility increases
More different ways for guidinglearning of students
Peer tutoring
Teaching aides
E-learningsoftware
Teachers
If strongness increases
Teaching aides
Teachers
E-learningsoftware
Peer tutoring
More ‘guidance of learning’ connections for students
Jan. 20, 2006 Patterns in Education 26
SMITHTOWN SCHOOL #9
Predictions – Axiom 108
Then filtration decreases
Smithtown #9 makes NCLB rating. This encourages enrollment.
They’ve made AYP. Tommy can enroll here!
FAILURESUCCESS
If strongness increases
Teaching aides
Teachers
E-learningsoftware
Peer tutoring
More ‘guidance of learning’ connections for students
AYP = Annual Yearly Progress (part of NCLB law)
Jan. 20, 2006 Patterns in Education 27
Predictions – Axiom 144
Then isomorphism increases
Smithtown replicates successstrategy for more schools
SMITHTOWN SCHOOL #9
SMITHTOWN SCHOOL #1
SMITHTOWN SCHOOL #12SMITHTOWN
SCHOOL #25
SMITHTOWN SCHOOL #5
SUCCESS
Smithtown #9 makes NCLB rating.This raises enrollment.
They’ve improvedachievement scores and made AYP. Tommy canenroll here!
SMITHTOWN SCHOOL #9
If strongness increases
Increasestrongness
Increasestrongness
Increasestrongness
Increasestrongness
AYP = Annual Yearly Progress (part of NCLB law)
Jan. 20, 2006 Patterns in Education 28
Summary
If we have a valid educational systems theory,
Based on predictable temporal patterns and configurations,
Then we can change an education system with a reasonable expectation that it will actually be improved.
Jan. 20, 2006 Patterns in Education 29
Summary
This leads to an inquiry-based systems change strategy:
Get Ready >> SET >> Go!
Jan. 20, 2006 Patterns in Education 30
Change Strategy: Get Ready >> SET >> Go!
Phase 1: Get Ready Identify the specific current education system to be improved. Over some interval of time, measure system properties (e.g.,
input, regulation, complexity, strongness) with Analysis of Patterns in Time and Configuration (APT&C)
Use Predicting Educational Systems Outcomes (PESO) software to predict outcomes based on observed system properties under existing conditions (e.g., complexity increases, decreases, or remains constant). These predictions are based on how the system is currently designed and operates under existing conditions, before any new design is implemented.
If these outcomes are what are wanted, then do not modify the system. Otherwise, proceed to Phase 2.
Jan. 20, 2006 Patterns in Education 31
Change Strategy: Get Ready >> SET >> Go!
Phase 2: SET Use PESO software to model newly
envisioned educational system designs – i.e., the changes desired which are feasible.
Run PESO predictions far out enough in time to make sure all the consequences of the newly designed system would be acceptable. Are these the wanted outcomes? If yes, proceed to Phase 3.
Jan. 20, 2006 Patterns in Education 32
Change Strategy: Get Ready >> SET >> Go!
Phase 3: Go! Implement the new design chosen in Phase 2. Over some interval of time, measure system
properties with APT&C. Verify that predicted system outcomes have
occurred. If not, was something important overlooked in the observation and analysis of this particular system? Proceed to Phase 2.
Jan. 20, 2006 Patterns in Education 33
SimEd Technologies
We refer to:
ATIS theory model APT&C software PESO software, and the ‘Get Ready, SET, Go!’ model
as
SimEd Technologies
Jan. 20, 2006 Patterns in Education 34
SimEd Technologies: Student Roles
Literature review: relevant to educational systems, instructional systems, or performance improvement systems
Review measurement and analysis methodologies in comparison or contrast with APT&C (Analysis of Patterns in Time & Configuration):
Qualitative Quantitative Mixed mode
Review empirical studies of temporal patterns and configurations Review empirical studies that support or refute theorems in
PESO (Predicting Education System Outcomes), e.g., Exter, Hur, Koh & Wong (2004): Education systems theory
study
Jan. 20, 2006 Patterns in Education 35
SimEd Technologies: Student Roles
APT&C and PESO software design, development and evaluation
Interface designUsability testing and evaluation of prototypesSoftware development (in XHTML, Flash,
ActionScript, PHP, XML, MySQL, JavaScript, AJAX?, Access?, Visual BASIC?)
Online help systems (dictionary, examples, tutorials)
Jan. 20, 2006 Patterns in Education 36
SimEd Technologies: Student Roles
Research studies that you conduct:
Apply APT&C methodology for measurement and analysis in an empirical study you conduct on a topic of interest, e.g.,
Jaesoon An (2003): Understanding mode errors in modern human-computer interfaces
Christine Fitzpatrick (2006): Instructional strategies for electronic peer review in technical communication
Thomas Plew (1989): An empirical investigation of major adaptive testing methodologies and an expert systems approach
JaeKyung Yi (1995): Analysis of hypermedia using a general systems theory model
Roger Yin (1998): Dynamic learning patterns during individualized instruction
Jan. 20, 2006 Patterns in Education 37
SimEd Technologies: Student Roles
Research studies that you conduct (cont’d):
Empirical validation of a systems relationship in PESO (based on ATIS): e.g., If system strongness increases, then hierarchical order decreases If system input decreases, then filtration increases Etc. (over 200 to choose from)
Note: these implications could be tested in a particular context of interest, e.g., in a(n) classroom system, instructional system, performance improvement system, school system, etc.
Ming Ma is currently studying several structural configurations at Harmony School in Bloomington and in one of our IST classes as an R695 project (legacy)
Theory development utilizing Axiomatic Theories of Intentional Systems, e.g., Joyce Koh (2005): A general systems theory approach for implementing autonomy
support in classrooms
Jan. 20, 2006 Patterns in Education 38
SimEd Technologies: Student Roles
Grant proposal writing to:
Support APT&C software developmentSupport PESO software developmentConduct empirical studies using APT&C
to validate PESOImplement ‘Get Ready >> SET >> Go!’
change model
Jan. 20, 2006 Patterns in Education 39
SimEd Technologies: Student Roles
Questions?
For more information on SimEd Technologies:
http://simedtech.com