Jan. 20, 2006 Patterns in Education 1 Theodore Frick Department of Instructional Systems Technology...

Jan. 20, 2006 Patterns in Education 1

Patterns in Education

Theodore Frick

Department of Instructional Systems TechnologySchool of EducationIndiana University Bloomington


Familiar Patterns: Temporal

Darkness at night, stars shiningDawnSunriseDaytime, sun moves east to westSunsetDuskDarkness…


Familiar Patterns: Temporal

SpringSummerFallWinter


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)


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


A pattern is a relation

General form of a relation:


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


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)


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


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


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.


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


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.


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


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)


SMITHTOWN SCHOOL #9

Parents start transferring children to other schools

Scenario


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!



Then storeput decreases

Fewer students attending classesEnrollment falls

If input decreases




Then fromput decreases

Fewer students to graduate

ADMINISTRATIONOFFICE

Hmm…there aren’t as many diplomas to print this year!

Enrollment falls

If input decreases




Then feedout decreases

Fewer graduatesEnrollment falls

If input decreases



SMITHTOWN SCHOOL #9 BOARD MEETING AGENDA:

How to improve achievement scores?


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!


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?


Smithtown’s proposed strategy

Increase avenues of instruction through:Teaching aidesPeer tutoring Instructional technology e.g. using e-

Learning software

Increase strongness



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.



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



SMITHTOWN SCHOOL #9


Then filtration decreases

Smithtown #9 makes NCLB rating. This encourages enrollment.

They’ve made AYP. Tommy can enroll here!

FAILURESUCCESS


Teaching aides

Teachers

E-learningsoftware

Peer tutoring


AYP = Annual Yearly Progress (part of NCLB law)



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


Increasestrongness

Increasestrongness

Increasestrongness

Increasestrongness

AYP = Annual Yearly Progress (part of NCLB law)


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.


Summary

This leads to an inquiry-based systems change strategy:

Get Ready >> SET >> Go!


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.



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.



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.


SimEd Technologies

We refer to:

ATIS theory model APT&C software PESO software, and the ‘Get Ready, SET, Go!’ model

as

SimEd Technologies


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



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)



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



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



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



Questions?

For more information on SimEd Technologies:

http://simedtech.com

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