THE RELATIONSHIP BETWEEN DELIBERATE PRACTICE AND READING ABILITY Sean Thomas ...

282 Pages·2013·1.63 MB·English

THE RELATIONSHIP BETWEEN DELIBERATE PRACTICE AND READING ABILITY

Sean Thomas Hanlon

A dissertation submitted to the faculty of the University of North Carolina at Chapel Hill in
partial fulfillment of the requirements for the degree of Doctor of Philosophy in the School of
Education.

Chapel Hill
2013

Submitted to:

Jeffrey A. Greene

Gregory J. Cizek

Jill W. Fitzgerald

Judith L. Meece

Carl W. Swartz
©2013
Sean Thomas Hanlon
ALL RIGHTS RESERVED

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ABSTRACT
SEAN T. HANLON: The Relationship between Deliberate Practice and Reading Ability
(Under the direction of Jeffrey A. Greene)

Many students are not prepared to meet the literacy demands of college and career as
defined by the Common Core State Standards (2010). Literacy researchers have struggled to
define the frequency and type of reading practice necessary to nurture the development of
reading ability. The principles of deliberate practice provide a theoretical framework that
could describe the type of practice necessary to develop expertise in reading. The purpose of
this study was to explore the relationship between deliberate practice and reading ability. In
this study, an educational technology, Learning Oasis, was used to deliver deliberate practice
and monitor change in student reading ability over time. The hypotheses were that participants
that engaged in more deliberate practice, as operationalized in this study, would experience
more rapid growth and achieve higher levels of reading ability. Participants in this study (N =
1,369) ranged from grades one through twelve and were from a suburban school district in
Mississippi. Each participant had at least three measurement occasions separated by at least
three months each. The Lexile Framework for Reading was used to estimate participant
reading ability during this research. Given the longitudinal nature of the data, a multilevel
model was used to explore individual change over time. A negative exponential functional
form was determined to best model change in participant reading ability over time. The results
showed that, on average, participants that engaged in more deliberate practice (i.e., targeted
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practice with immediate feedback completed intensely over a long period of time) grew more
rapidly and reached a higher ability level than participants that completed less deliberate
practice. Implications for educators, educational technology designers, and researchers are
discussed along with potential future areas of research.

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ACKNOWLEDGMENTS

Without the contribution and support of many, the tome you now hold in your hands
would never have been completed. While it is impossible to acknowledge everybody who
offered well-timed advice or words of encouragement, certain individuals require special
mention. First and foremost, I extend my gratitude to the students and educators that
participated in this research, particularly superintendent “Dr. C.” It has been a long road since
that rainy morning in 2006 when two guys from North Carolina showed up in the computer
lab at the high school. Thank you for allowing us to be part of the school day; it is a
responsibility we continue to take very seriously.
Throughout my time at the University of North Carolina at Chapel Hill, I have been
fortunate to work with brilliant scholars. To Lara Jean Costa, I offer my thanks for years of
friendship through all manner of shared experiences. To my various committees, I offer my
thanks for your scholarship, honesty, and thoughtful feedback. My thanks to Dr. Jeffrey A.
Greene, Dr. Gregory J. Cizek, Dr. Jill W. Fitzgerald, Dr. Judith L. Meece, Dr. A. Jackson
Stenner, and Dr. Carl W. Swartz.
It is also important to recognize the support I have received from my colleagues at
MetaMetrics. In particular, I offer my deepest thanks to Dr. Gary L. Williamson and Juee
Tendulkar for their technical expertise, timely feedback, and ongoing support. I hope this
research reflects the time, talent, and effort the entire Learning Science and Technology group
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(i.e., the group formerly known as New Technologies) devoted to bringing our educational
technology to students. To past and present members of the Learning Science and Technology
team, I offer my thanks. I also offer my gratitude to other members of the MetaMetrics family
that have offered support in a variety of ways through the years. In particular, my thanks to
Harold J. Burdick, Dr. Donald S. Burdick, Dr. Malbert Smith III, Timothy J. Klasson, Patricia
Carideo, and Chris Whyte.
A special thank you to my advisor and dissertation chair Dr. Jeffrey A. Greene. Jeff,
we have come a long way since our collaboration began, and I hope every student receives the
high-quality advising and mentoring that you have provided for me over the years. While we
still might disagree on certain “stylistic features,” I know your attention to detail and
constructive criticism has made me a better thinker and writer. Your guidance on a variety of
topics, both academic and otherwise, has been very much appreciated.
The decision to return to graduate school, particularly to leave computer science and
embark on a Ph.D. program in educational psychology, was not taken lightly. To that end, I
offer a sincere and heartfelt thank you to Dr. A. Jackson Stenner and Dr. Carl W. Swartz for
their support and guidance. Jack, I have always appreciated the unique perspective and
unflappable demeanor that you bring to a discussion, regardless of the topic or venue. Your
honesty and encouragement has always meant a great deal to me. Carl, since our collaboration
began we have shared three different offices, released and supported at least eight different
classroom-based technologies, listened to Freddy C. and the rest of the ESPN radio cast over
incalculable miles in a rental car, racked up enough frequent flyer miles to travel around the
world, visited most of the Outback restaurants around the country, and frequented Starbucks
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enough times that I think we receive mail there. I continue to learn a great deal from you, and
I very much look forward to our continued collaboration.
I offer my appreciation to my family for their love and support throughout the course
of this Ph.D. program. I would like to thank the MacDonell family (Cindy, Dave, Scott, and
Julie) and Karen and Ryan Hanlon for their kind words of support. To Amanda Smith and
Mark Hanlon, I thank them for their true-blue support despite their observation that a 282
page document is really too long to read, let alone write. To my parents, Barbara and Thomas
Hanlon, I extend my thanks for a lifetime of encouragement and guidance. It seems like we
have come a long way from the EPCOT parking lot and my future career in data entry. Last
but certainly not least, I offer my heartfelt thanks to my wife, Kate. J.R.R.Tolkien said “it’s
the job that’s never started that takes the longest to finish,” and your limitless encouragement
to not only begin a Ph.D. program, but to stay the course and finish was very much
appreciated. I think it is safe to say that we can consider this a terminal degree. Thanks for all
you do each and every day.

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TABLE OF CONTENTS
LIST OF TABLES………………………………………………………………………… x
LIST OF FIGURES..………………………………………………………………………. xii
Chapter
I. INTRODUCTION………………………………………………………….. 1
II. REVIEW OF THE LITERATURE……………………………..………….. 11
The development of expertise………………………………….…………… 11
Theories of reading……………………...…………………………………... 39
Technology and 21st century learning and research………………………... 77
Deliberate practice, theories of reading, and educational
technology…………………………………………………………………... 84
III. METHODS………………………………………………………….……… 89
Participants…………………………………………………...…...………… 89
Data sources……………………………………………………………..….. 91
Procedures……………………………………………………………..……. 92
Instrumentation………………………………………………………...……. 97
Data analysis plan…………………………………………………………… 111
IV. RESULTS…………………………………………………………………… 131
Exploratory analysis………………………………………………………… 131
Fitting a taxonomy of models……………………………………………….. 138
Hypotheses revisited………………………………………………………… 158
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Deliberate practice as a whole………………………………………………. 168
Examination of assumptions………………………………..…….………… 168
Summary of results………………………………………………………….. 172
V. DISCUSSION…………………………………………………………...….. 173
Deliberate practice and reading ability……………………………………… 174
The role of educational technology…………………………………………. 183
Limitations…………………………………………………………………... 185
Future research…………………………………………………………….. 190
Implications for educators, educational technology designers, and
researchers………………………………………………….……………… 195

Next steps…………………………………………………………………… 198
Conclusion………………………………………………………………….. 202
APPENDICES……………………………………………………………………………… 203
REFERENCES……………………………………………………………………………. 225
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LIST OF TABLES

Table

1. Time spent reading daily and words read annually in relation to percentile rank on
standardized reading tests…………………………………………………………….... 3

2. Operational definition of deliberate practice…………………….…………………….. 8

3. Qualifying participants by grade…………………………………………………….. 90

4. Demographic information for all participants participating in study (N = 1,369)….…91

5. Test length of a sample of high-stakes reading assessments……………………….. 101

6. Operational definition of deliberate practice………………………………………... 103

7. Grade-based standard-length words per minute (Wpm) ranges…………………….. 109

8. Values in person-period data set……………………………………………………. 118

9. Descriptive statistics for the individual growth parameters obtained by fitting
separate within-person OLS regression models (N = 1,369)……………………..… 133

10. Descriptive statistics for amount of reading and components of deliberate practice.. 134

11. Correlations between predictors of interest…………………………………………. 137

12. Taxonomy of multilevel models for change fitted to participant reading data……... 139

13. Unstandardized fixed effects: Results of fitting a taxonomy of multilevel models
for change to participant reading data (N = 1,360)…………………………………. 142

14. Variance components and goodness-of-fit indices: Results of fitting a taxonomy of
multilevel models for change to participant reading data (N = 1,360)…………….... 144

15. Model comparisons made during fitting taxonomy of models to participant reading
data using both Likelihood Ratio Test (LRT) and Wald statistics…………………. 149

16. VIF statistics for potentially collinear predictors and goodness-of-fit indices for
select
models………………………………………………………………………………. 152

17. Fixed effects and VIF values for Model G, Model H, and Model I (N = 1,360)…… 155

x