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Patterns in Education: Linking Theory to Practice. Theodore Frick Department of Instructional Systems Technology School of Education Indiana University Bloomington. Overview of APT&C. Analysis of Patterns in Time and Configuration: APT&C

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patterns in education linking theory to practice

Patterns in Education: Linking Theory to Practice

Theodore Frick

Department of Instructional Systems Technology

School of Education

Indiana University Bloomington

Patterns in Education, AECT 2006

overview of apt c
Overview of APT&C
  • Analysis of Patterns in Time and Configuration: APT&C
  • Fundamental change in perspective for measurement and analysis
  • Bridges quantitative and qualitative paradigms
  • APT for temporal patterns (both joint and sequential occurrences of events)
  • APC for structural patterns (configurations)

Patterns in Education, AECT 2006

overview cont d
Overview cont’d
  • APT&C based on mathematical theories and general systems theory
  • Value of APT&C is that results can be directly related to practice
  • Through APT&C we have new ways of conducting educational research

Patterns in Education, AECT 2006

outline of this presentation
Outline of this presentation
  • The dilemma: qualitative vs. quantitative methodologies
  • Three examples of empirical studies that used APT&C:
    • Academic learning time (APT joint occurrences)
    • Patterns of mode errors in human-computer interfaces (APT sequential occurrences)
    • Student autonomy structures in a Montessori classroom (APC patterns of student choice of work and guidance of learning)

Patterns in Education, AECT 2006

quantitative vs qualitative paradigms
Quantitative vs. Qualitative Paradigms
  • Research methods in 20th century were largely quantitative.
  • Qualitative and mixed methods are gaining more use in research during past two decades.
  • Main problems:
    • Quantitative methods seldom yield significant results that can be directly linked to educational practice (due to large within-group variances in experiments or treatments)
    • Qualitative methods can provide good insights into practice, but conclusions are often restricted (low generalizability due to sampling strategy, and may or may not transfer to similar situations)

Patterns in Education, AECT 2006

three empirical studies to illustrate value of apt c
Three Empirical Studies to Illustrate Value of APT&C
  • Academic learning time of mildly handicapped children (Frick, 1990)
  • Patterns of mode errors in human-computer interfaces (An, 2003)
  • Student autonomy structures in a Montessori classroom (Koh, 2006)

Patterns in Education, AECT 2006

study 1 academic learning time study
Study # 1:Academic Learning Time Study
  • 25 systems observed in central and southern Indiana
  • Tracked 25 target students in academic activities over several months for 8 -10 hours each
  • Trained observers coded types of academic learning contexts, task difficulty and task success
  • Observers also coded student and instructor behaviors in math and reading (about 500 time samples at one-minute intervals for each target student)
  • Nearly 15,000 time moments sampled overall.

Patterns in Education, AECT 2006

what observers coded in math and reading activities each minute
What observers coded in math and reading activities each minute
  • Types of student engagement: written, oral, and covert on-task; off-task behaviors (later recoded as engagement, EN, and non-engagement, NE)
  • Types of instructor behaviors: structuring, explaining, demonstrating, questioning, feedback (later recoded as direct instruction, DI), and monitoring academic seatwork (non-direct instruction, ND).
  • Observer comments to elaborate what was happening

Patterns in Education, AECT 2006

observer coding form
Observer coding form

Patterns in Education, AECT 2006

codes for target student moves
Codes for target student moves

Patterns in Education, AECT 2006

codes for instructor moves and focus
Codes for instructor moves and focus

Patterns in Education, AECT 2006

standard analysis columns 1 and 2 independent measures of di and of en were correlated n 25
Standard analysis: columns 1 and 2: independent measures of DI and of EN were correlated (n = 25)

Patterns in Education, AECT 2006

linear models approach
Linear Models Approach
  • Linear models approach (quantitative method):
    • Relates independent measures through a mathematical function
    • Treats deviation from model as error variance

Patterns in Education, AECT 2006

linear models approach cont d
Linear Models Approach cont’d

Patterns in Education, AECT 2006

linear models results
Linear models results:
  • Means and standard deviations
    • Mean p(DI) = 0.432 s.d. = 0.144
    • Mean p(EN) = 0.741 s.d. = 0.101
  • Regression equation
    • EN = 0.57 + 0.40DI
    • R2 = 0.33
    • DI “explains” 33 percent of the variance in student engagement; 67 percent unexplained

Patterns in Education, AECT 2006

analysis of patterns in time
Analysis of Patterns in Time
  • APT measures a relation directly by counting occurrences of when a temporal pattern is true or false in observational data
  • Probability of joint or sequential occurrence can be estimated for a pattern from the counts

Patterns in Education, AECT 2006

apt results for same 25 systems includes measures of joint and conditional occurrences
APT Results for same 25 systems: includes measures of joint and conditional occurrences

Patterns in Education, AECT 2006

apt results
APT Results
  • Means and standard deviations for the relations
    • Mean p(EN | DI) = 0.967 s.d. = 0.029
    • Mean p(EN | ND) = 0.573 s.d. = 0.142
  • When direct instruction is occurring, students are highly engaged.
  • When non-direct instruction is occurring they are less engaged.
  • Students were 13 times more likely to be off-task during non-direct instruction compared with direct instruction: (1 - 0.573) / (1 – 0.967) = 12.94.

Patterns in Education, AECT 2006

apt joint occurrence calculation example
APT: joint occurrence calculation example

p(DI) = ¾ = 0.75

p(ND) = ¼ = 0.25

p(EN) = ½ = 0.50

p(NE) = ½ = 0.50

p(DI & EN) = 2/4 = 0.50

p(DI & NE) = ¼ = 0.25

p(ND & EN) = 0/4 = 0.0

p(ND & NE) = ¼ = 0.25

p(EN|DI) = 2/3 = 0.67

p(EN|ND) = 0/1 = 0.00

Patterns in Education, AECT 2006

lma vs apt
LMA vs. APT
  • Linear models relate the independent measures by a function for a line:
    • e.g., EN = 0.57 + 0.40DI
  • APT measures the relation in terms of joint, conditional, or sequential occurrence:
    • e.g., p (EN|DI) = 0.967
    • e.g., p (EN|ND) = 0.573DI = direct instruction, EN = student engagement, ND = non-direct instruction

Patterns in Education, AECT 2006

study 2 patterns of mode errors in hci
Study #2:Patterns of Mode Errors in HCI
  • Software mode: when the same action results in two or more outcomes (Raskin, 2000).
  • E.g., In one context, pressing the ‘d’ key results in the letter ‘d’ echoed on the screen
  • In another context, pressing the ‘d’ key results in deleting a file.
  • Mode errors by humans can cause serious problems:
    • Destruction of important work
    • Decreased productivity
    • Not able to complete tasks
  • Modes occur in almost all modern human-computer interfaces (e.g., OS 10, Windows XP, Word, Photoshop, etc.)

Patterns in Education, AECT 2006

an 2003 study of mode errors
An (2003) study of mode errors
  • Mixed methods approach (usability evaluation, qualitative and quantitative)
  • 16 college students performed eight computer tasks with three modern GUI interfaces (word processor, address book, image editor).
  • Participants were videotaped, and stimulated- recall interviews were conducted immediately afterwards to clarify why certain actions were taken, when viewing their videos.

Patterns in Education, AECT 2006

an 2003 study of mode errors cont d
An (2003) study of mode errors (cont’d)
  • Over 280 problematic actions were observed, and 52 were problems due to mode errors
  • 52/280 = .19, or roughly 1 out of 5 problems were due to software modes
  • Three general patterns (conditions) of mode errors emerged from qualitative analyses:
    • Type A: Right action, wrong result
    • Type B: It isn’t there where I need it
    • Type C: It isn’t there at all

Patterns in Education, AECT 2006

an 2003 study of mode errors cont d24
An (2003) study of mode errors (cont’d)
  • Source of error analysis revealed that mode errors appeared to result from 8 types of design incongruity:
    • Unaffordance
    • Invisibility
    • Misled expectation
    • Unmet expectation
    • Mismatched expectation
    • Inconsistency
    • Unmemorability
    • Over-automation

Patterns in Education, AECT 2006

an 2003 study of mode errors cont d25
An (2003) study of mode errors (cont’d)
  • Consequences of mode errors:
    • Can’t find hidden function
    • Can’t find unavailable function
    • False success
    • Stuck performance
    • Inhibited performance
    • Inefficient performance

Patterns in Education, AECT 2006

apt analysis of sequential patterns of mode errors sources and consequences
APT: analysis of sequential patterns of mode errors, sources and consequences

Patterns in Education, AECT 2006

apt analysis of sequential patterns of mode errors sources and consequences27
APT: analysis of sequential patterns of mode errors, sources and consequences

Patterns in Education, AECT 2006

apt analysis of sequential patterns of mode errors sources and consequences28
APT: analysis of sequential patterns of mode errors, sources and consequences
  • APT results have practical implications
  • E.g., if the mode error is ‘right action, wrong result’ and if the source of the error is unaffordance (function not obvious), then 67 percent of the time users could not find a hidden function or thought they did the task correctly when in fact they had not (false success).

Patterns in Education, AECT 2006

apt methodology sequential occurrence
APT Methodology: sequential occurrence
  • When one event precedes another, and when observers code the order in which events occur:
    • APT can estimate the probability of the consequent following the antecedent event.
    • APT can estimate likelihoods of sequences longer than two (unlike Markov chains).
    • APT can estimate both joint and sequential event occurrences in complex combinations.

Patterns in Education, AECT 2006

apt coding temporal configuration
APT Coding (temporal configuration)

Patterns in Education, AECT 2006

apt classifications and categories
APT Classifications and Categories
  • Each column is a classification
  • Classifications co-exist in time
  • Categories of events within a classification cannot co-exist in time (since they are mutually exclusive, by definition)
  • An observer codes event changes within each classification in the order that they occur.
  • Date/time is always a classification and is recorded whenever there is an event change.

Patterns in Education, AECT 2006

slide33

APT Query: IF target student IS Mona?

Patterns in Education, AECT 2006

apt query and results
APT Query and Results

Query

IF target student IS Mona?

Results

Cumulative duration = (9:13 – 9:01) = 12 minutes

Cumulative frequency = 1 event

Likelihood = 1 out of 1 relevant event changes = 1.00

Proportion time = 12 minutes out of 12 = 1.00

Patterns in Education, AECT 2006

apt query if target student is mona and instruction is direct
APT Query: IF target student is Mona AND instruction is direct?

Patterns in Education, AECT 2006

apt query results
APT Query Results

Query

IF target student IS Mona

AND instruction IS direct?

Results

Cumulative duration = (9:08 – 9:01) = 7 minutes

Cumulative frequency = 1 event

Likelihood = 1 out of 2 relevant event changes = 0.50

Proportion time = 7 minutes out of 12 = 0.583

Patterns in Education, AECT 2006

apt query if target student is mona and instruction is direct then student engagement is on task
APT Query: IF target student IS Mona AND instruction IS direct, THEN student engagement IS on-task?

Patterns in Education, AECT 2006

apt query results38
APT Query Results

Query

IF target student IS Mona

AND instruction IS direct,

THEN student engagement IS on-task?

Results

Cumulative duration = (9:06 – 9:03) + (9:08 – 9:07) = 4 minutes

Cumulative frequency = 2

Likelihood = 2 out of 4 = 0.50

Proportion time = 4 minutes out of 6 = 0.667

Patterns in Education, AECT 2006

apt query syntax
APT Query Syntax

Patterns in Education, AECT 2006

apt syntax cont d
APT Syntax (cont’d)

Patterns in Education, AECT 2006

apt syntax cont d41
APT Syntax (cont’d)

Patterns in Education, AECT 2006

apt query syntax42
APT Query Syntax
  • Thus, simple to very complex temporal patterns can be specified within APT queries.
  • Joint and/or sequential occurrences of events can be specified.
  • Results include frequency counts, likelihood estimates, durations and proportions of total time.

Patterns in Education, AECT 2006

theoretical foundations of apt
Theoretical Foundationsof APT
  • Mathematical theory
    • Set theory
    • Probability theory
  • Information theory
    • Classifications (more than one, non-exclusive)
    • Categories within each classification must be mutually exclusive and exhaustive
  • General systems theory
    • SIGGS Theory Model

Patterns in Education, AECT 2006

advantages of apt
Advantages of APT
  • APT brings theoretical rigor to pattern identification in qualitative research.
  • APT measures relations not possible in quantitative methods such as the linear models approach.
  • APT requires a different kind of conceptual framework for measurement and analysis than those for qualitative and quantitative approaches.

Patterns in Education, AECT 2006

apc analysis of patterns in configuration
APC: Analysis of Patterns in Configuration
  • Thompson (2005) realized that APT could be extended to measure and analyze structure of systems.
  • Structure pertains to relationships among parts.

Patterns in Education, AECT 2006

familiar patterns structural
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

Patterns in Education, AECT 2006

familiar patterns structural47
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
  • Instructional relation:
    • During fall semester, 2005,T. Frick was the R690 instructor of:
      • Andrew, Omer, Shyamasri, Nichole, Jamison, Sunnie, Emmanuel, Uvsh, Chris, Theano

Patterns in Education, AECT 2006

a pattern is a relation

A rel B

A pattern is a relation
  • General form of a relation:

Patterns in Education, AECT 2006

temporal structural patterns logical relations
Temporal & Structural Patterns & Logical Relations
  • Temporal Patterns
    • A precedes B
    • A co-occurs with B
  • Structural Patterns or Configurations
    • A affect relation B
  • Logical Relations
    • A implies B
    • A is equivalent to B

Patterns in Education, AECT 2006

affect relation guides research of
Affect relation: guides research of

Faculty Person 2

Faculty Person 1

Student 3

Student 1

Student 4

Student 5

Student 2

Old IST Ph.D. structure

Patterns in Education, AECT 2006

affect relation guides research of51
Affect relation: guides research of

Faculty Person 2

Faculty Person 1

Student 3

Student 1

Student 4

Student 5

Student 2

New IST Ph.D. structure

Patterns in Education, AECT 2006

study 3 autonomy structures in a montessori classroom koh 2006
Study #3: Autonomy structures in a Montessori classroom (Koh, 2006)
  • Case study to explore Montessori classroom structures that support student autonomy
  • Observed on 10 occasions for about an hour at different times of morning session (1 head teacher, 2 assistant teachers, 28 students ages 10-12)
  • Ethnographic approach initially

Patterns in Education, AECT 2006

koh 2006 study cont d
Koh (2006) study cont’d
  • Class activities were built around two different activity structures:
    • Head problems
    • Morning work period
  • Koh was interested in two kinds of affect relations:
    • schooses worky
    • yguides learning ofs

Patterns in Education, AECT 2006

koh 2006 study cont d55
Koh (2006) study cont’d
  • Digraphs were drawn for affect relation structures during Head Problems and during Morning Work Period
  • APC software was used to calculate structure measures of these digraphs (Frick & Thompson, 2006)

Patterns in Education, AECT 2006

koh 2006 study cont d56
Koh (2006) study cont’d
  • Structures measured:
    • Active dependence
    • Centrality
    • Complexity
    • Independence
    • Interdependence
    • Complete connectivity

Patterns in Education, AECT 2006

active dependence definition and measure
Active Dependence: definition and measure

Patterns in Education, AECT 2006

apc results from koh 2006 study
APC Results fromKoh (2006) study

PropertyValue

Structural Property

Patterns in Education, AECT 2006

apc results from koh 2006 study cont d
APC Results fromKoh (2006) study (cont’d)
  • Active dependence higher in Head Problems vs. Morning Work Period
  • Centrality higher in Head Problems vs. Morning Work Period
  • Interdependence lower in Head Problems vs. Morning Work Period
  • Complexity lower in Head Problems vs. Morning Work Period

Patterns in Education, AECT 2006

apc results from koh 2006 study cont d60
APC Results fromKoh (2006) study (cont’d)
  • The structure of the Morning Work Period supported student autonomy
  • During the Morning Work period there was:
    • Less active dependence
    • No centrality
    • Greater complexity
    • Greater interdependence

Patterns in Education, AECT 2006

apc results from koh 2006 study cont d61
APC Results fromKoh (2006) study (cont’d)
  • The 3 teachers’ responses to the Problems in Schools Questionnaire (SDT, 2006) showed them to be “highly autonomy supportive”.
  • Student responses to the Academic Self-Regulation Questionnaire (SDT, 2006) indicated a greater tendency to undertake learning activities because they perceived some personal value and identification with the learning goals, rather than because they felt compelled by external factors.

Patterns in Education, AECT 2006

apc results from koh 2006 study cont d62
APC Results fromKoh (2006) study (cont’d)
  • The structural configuration of the Morning Work Period, where students chose learning activities and worked at their own pace is characteristic of Montessori classrooms.
  • The structural configuration of the Head Problems activity chosen by the head teacher with all students working on the same problems, is more typical of traditional K-12 classrooms in the U.S.
  • APC allowed analysis and comparison of structural properties of those two configurations of affect relations.

Patterns in Education, AECT 2006

summary
Summary
  • APT allows measurement and analysis of temporal properties
    • Joint occurrences
    • Sequential occurrences
    • Combinations of joint and sequential occurrences

Patterns in Education, AECT 2006

apt joint occurrence example
APT: joint occurrence example

Patterns in Education, AECT 2006

apt joint and sequential occurrence example
APT joint and sequential occurrence example

Patterns in Education, AECT 2006

summary66
Summary
  • APC allows measurement and analysis of structural properties

Patterns in Education, AECT 2006

apc allows measures of structural properties of an affect relation e g guides research of
APC allows measures of structural properties of an affect relation (e.g., guides research of)

Faculty Person 2

Faculty Person 1

Student 3

Student 1

Student 4

Student 5

Student 2

New IST Ph.D. structure

Patterns in Education, AECT 2006

apc property measures and values
APC property measures and values

Patterns in Education, AECT 2006

summary apt c
Summary: APT&C
  • Analysis of Patterns in Time and Configuration permits measurement and analysis of human learning and work environments.
  • The value of APT&C methodology was illustrated by clear results from three empirical studies.
  • These results have direct implications for practice. APT&C is a way to link theory to practice.
  • Software is under development to do APT&C.

Patterns in Education, AECT 2006

questions
Questions

For more information on APT&C:

http://www.indiana.edu/~aptfrick

Patterns in Education, AECT 2006