IE
W
EV
PR
IE
W
EV
PR
IE
W
EV
PR
Predicting Elementary English Learners’ Inferences From A Background Knowledge
Threshold
IE
W
A Thesis
Presented to
The Department of TESOL
EV
Emporia State University
PR
In Partial Fulfillment
Of the Requirements for the Degree
Master of Arts
by
Annette Putnam
2021
_________________________
Jim Persinger, Ph.D.
PR
EV
IE
W
Approved by the Department Chair
Cate Grundleger, Ph.D.
Committee Chair
Teddy Roop, Ph.D.
Chair Member
_________________________________________
Jerald W.Spotswood, Ph.D.
Dean of the Graduate School and Distance Education
ii
Acknowledgements
Since I am notoriously not a touchy-feely person, I shall keep this brief. I sincerely thank
everyone who has played a role in my academic journey. This thesis would not have been
possible without God, my parents, Dr. Cate Grundleger, Dr. Jim Persinger, Dr. Teddy Roop, Dr.
Rajarshi Dey, Dr. Elizabeth Moss, and Dr. Gerry Coffman.
PR
EV
IE
W
Thank you.
iii
TABLE OF CONTENTS
ACKNOWLEDGMENTS……………………………………………………………...………………….iii
TABLE OF CONTENTS…………………………………………………………………..………………iv
CHAPTER I - INTRODUCTION………………………………………………………….…….1
Statement of the Problem………...………………………………………………………..2
Purpose of the Study………...…………………………………………………………….3
Research Questions and Hypotheses……………...………………………………………4
IE
W
Significance of the Study………………………………………………………………….5
CHAPTER II - LITERATURE REVIEW…………………………………………………..…7
Influential Theory…………………………………………...………………………….....7
L1 Reading Comprehension………………...………………………………….....7
EV
Resonance Model………………………………………………………………...10
Predictive Modeling, Latent Semantic Analysis, and Knowledge Thresholds…..11
PR
Reading Reconsidered: The Case for Knowledge…………………………………….....13
Qualities of Background Knowledge………………………………….................15
Background Knowledge as a Prerequisite to Inferences……………………...….16
Background Knowledge, Inferences, and Informational Text…………………...18
Group Differences………………………………………………………………………..20
L2 Reading Comprehension…...………………………………………………...20
English Learners, Background Knowledge, and Inference…………...…………22
iv
CHAPTER II - METHOD……………………………………………………..……………....25
Research Design and Rationale…………………………………...…..………………....25
Participants and Setting…………………………………………………………..……...27
Instrumentation………………………………………………………………....………...……...27
Procedure…………………………………………………………………….…………..30
Data Analysis…………………………………………………………………………….31
Ethics……………………………………………………………………………………..32
CHAPTER III - RESULTS………………………………………………………………….....33
IE
W
CHAPTER IV - DISCUSSION AND CONCLUSION…………………………………….....39
A Background Knowledge Threshold……………….…………………………..………39
Predicting English Learner Success……………………………………………………...40
EV
Implications for Instruction……………………………………………...……………….40
Limitations of the Study………………………...……………….………….……………42
Suggestions for Future Research……………..………………………………………….43
PR
References………………………………………………………………………………………..44
Appendix A…………………………………………………………………...……………….…60
Appendix B……………………………………………………………………………………....61
Appendix C………………………………………………………………………………………63
v
1
CHAPTER 1
INTRODUCTION
“If the topic...is outside [students’] experience or base of knowledge, they are adrift to an
unknown sea.” (Aebersold & Field, 1997, p. 41)
There is mounting evidence that K-12 English learners (ELs) are performing lower than
their monolingual peers in reading on both high-stakes and classroom assessments (Koo et al.,
2014). The National Assessment of Education Progress (2017) reports multilingual students read
IE
W
37 points below their non-EL peers. Considering there are almost 5 million ELs in the United
States (Sanchez, 2017), this is an unacceptable gap. The current reality expects students to
understand grade-level language, grade-level content, and meet rigorous Common Core State
ELs (Wilson et al., 2016).
EV
Standards/English Language Arts academic standards, thus creating a challenge for teachers and
The first nonfiction CCSS/ELA standard, 4.RI.1, is fundamental because it requires
PR
readers to independently make inferences (National Governors Association (NGA) Center for
Best Practices & Council of Chief State School Officers (CCSSO, 2010). Years of overwhelming
evidence suggest that strong inferences lead to comprehension (Oakhill & Cain, 2018; Shapiro,
2004), but this is a challenging skill for children (Omanson et al., 1978). Although a variety of
factors influence inference-making, Yuill and Oakhill (1991) proposed a general knowledge
deficit as one plausible explanation for variation in children's inference-making, and subsequent
studies point in the same direction: proficient readers leverage sufficient prior knowledge to
make accurate inferences regarding background information not outright stated by the author
(Fisher et al., 2009; Graesser & Bertus, 1998). Unfortunately, this reality has not caught the
2
attention of reading practitioners and researchers in a way that is useful for teaching and
learning.
Statement of the Problem
A knowledge deficit is present in our country. Background knowledge’s presence in
instruction, curriculum, professional development, and scholarship does not reflect its significant
role in making inferences during reading. Students in today’s classrooms are asked to conjure
prior knowledge while being withheld from systematic or planned instruction of background
IE
W
knowledge. Before-reading background knowledge routines are typically spontaneous with a
sampling of students sharing personal experience or incomplete information about the topic
(Karpov, 2018), and rarely are individual marks collected on prior knowledge. Whole-group
EV
reading then swiftly moves into reading the selected text and students must immediately begin
inferring complex concepts and ideas. Background knowledge is needed for inferences but is
assumed to be acquired outside the school’s parameters and there is a push for reading skills and
PR
strategies instead (Neuman et al., 2014). However, it is “mistaken dogma”, leader-in-the-field
Hirsch asserts (2006), to leave the acquisition of background knowledge to the rare field trip or
hands-on activity. Without adequate background knowledge, teaching reading skills and
strategies, like summarizing or visualizing, have limited effectiveness for the reader (Hirsch,
2006).
A lack of knowledge building and measurement has resulted in a dependency on skill and
strategy to increase reading scores while ignoring the return-on-investment of knowledge input
(Cervetti & Hiebert, 2015; Wexler, 2019). Further, there remains a lack of quantitative evidence
as to what renders a student’s background knowledge deficient or sufficient. Who or what
3
establishes such a consequential threshold?
The generalizability of published research on background knowledge and inferences is
problematic because it captures the experiences of monolingual learners and excludes the diverse
public school classrooms that include ELs. Over time, however, the achievement gap widens and
these traditionally underserved students are inadvertently sent to the sidelines in schools, work,
and society (August et al., 2009; Hirsch, 2006). The logical question, then, is if the knowledge
deficiency is more significant among students whose first language (L1) is not English? To help
IE
W
ELs gain the most out of reading instruction, researchers and educators must understand how
much background knowledge is needed for inference.
Given the sine qua non of proficient reading, detailed empirical accounts of young ELs
EV
are vital to student learning and achievement. O’Reilly et. al. (2019) looked at how background
knowledge and inference-making work in concert, but not specifically in elementary-aged
students or English learners. To help elementary ELs gain the most out of reading instruction,
PR
researchers and educators need cognizance of how much background knowledge is needed for
inference, especially since ELs have different background knowledge that should not be
confused with a deficit of background knowledge (Vogt et al., 2010). If the aim is to provide a
firm direction for K-12 educational settings, reading studies need to be updated and include ELs,
as this thesis is designed to do. To remedy this, I will run a piecewise linear regression to reveal
a knowledge threshold for expository text to shed light on the cruciality of measuring individual
student levels of topic knowledge.
4
Purpose of the Study
Despite the importance of literacy skills and strategies, ELs depend on background
knowledge. The lack of background knowledge negatively impacts an EL’s willingness to
communicate, comprehend, and grasp new learning (Shen & Byfield, 2018). A possible cause is
that teachers do not assess or press enough on eliciting and then measuring how much
knowledge students come to a topic with (Ambrose et al., 2010). This author’s experience in the
classroom has been that activating background knowledge is a quick discussion rather than a preassessment that adjusts instruction for optimal learning. Nor has this author seen the systematic
IE
W
building of background knowledge that is needed to deepen student understanding of new topics
that will support their comprehension (Fugnitto, n.d.) In empirical research, prior knowledge is
largely controlled, so it is neither an option to turn to the literature for guidance on how prior
EV
knowledge interacts or influences comprehension (Witherby & Carpenter, 2021).
ELs require two knowledge sources for successful interpretation of a text: linguistic and
background knowledge (Grabe & Stoller, 2019). The proposed quantitative non-experimental
PR
study using archival data is set up to investigate the unanswered question of whether there is a
numerical value, a percentage point, at which the facilitating effect of background knowledge
secures a good inference score. To control for cultural influence as much as possible, the
purposeful decision was made to use the common experience of sleep. Specific sleep terms that
most likely predict high inference scores will also be evaluated.
Research Questions and Hypotheses
Building on the studies above and the reality of a growing English Language Learner
achievement gap, the purpose of this investigation is to predict inference correctness by
determining whether or not a background knowledge threshold exists in children ages 9-10.
5
Thus, the specific research questions and null hypotheses addressed in this study are:
Research Question 1: Can a background knowledge threshold be identified below which
correct inferences will be limited and not predicted by background knowledge but above which
there is a correlation?
Null hypothesis 1: There will not be a background knowledge threshold that affects
accurate inferencing.
Research Question 2: Using a latent semantic analysis hybrid, can a background
IE
W
knowledge score of topic-specific words determine which ELs will have difficulty with making
correct inferences about an informational text?
Null hypothesis 2: Background knowledge activation via topic-specific words cannot
Significance of the Study
EV
predict whether a student will be able to make correct inferences about a specific topic.
Background knowledge is too often discussed as a have-or-have-not entity, but this
PR
undermines the depth of academic and topic-specific background knowledge (Ambrose et al.,
2010). Previous studies have demonstrated the qualitative advantages of having ample academic
background knowledge, but there remains limited evidence pointing to how much background
knowledge is needed to ensure successful inferencing. By quantifiably finding a threshold, at
which a lack of topic background knowledge will severely limit the inference ability of readers,
this study could be of importance to the following:
Mainstream and English as a Second Language (ESL) teachers: When background
knowledge is externally and individually elicited, teachers are given a glimpse into the depth,
breadth, and organization of the knowledge that will be used for higher-order reading processes,
such as inferencing (Kim & Clariana, 2015). There may be a strong possibility of practical use
for teachers across many content-areas to identify ELs who may struggle to generate correct
inferences based on insufficient background knowledge. The outcomes of this study will help
them make decisions about pre-reading strategies and interventions for science topics.
Additionally, data given will help ESL specialists better know how to evaluate students for
conceptual understandings and vocabulary knowledge.
TESOL research: The researcher intends that this small study will improve the
knowledge base of teacher educators in best practices for assessing and teaching background
PR
EV
IE
W
knowledge. A key takeaway will be fostering the next generation of teachers of ELs.
6
CHAPTER II
7
LITERATURE REVIEW
Reading studies have looked at preconditions affecting inference outcomes, from reading
purpose (Nahatame, 2014) to vocabulary knowledge (Beck et al., 1982; Neuman et al., 2014) to
decoding skills (Oakhill & Cain, 2018). There is also a close connection between a reader’s
activated background knowledge, inference, and comprehension (Anderson & Pearson, 1984;
Hirsch, 2006; Neuman et al., 2014), an assertion which is supported by the classic ConstructionIntegration Model (Kintsch, 1988) and the resonance model (O’Brien & Cook, 2016). However,
IE
W
few studies have looked at the quantity of background knowledge needed for inferencing during
reading. This review synthesizes relevant background knowledge and inference studies to
provide the context as to what led to this study’s research questions. An overview of theoretical
EV
reading perspectives is considered first followed by an examination of the characteristics of
background knowledge and its effect on inferencing in informational texts. The chapter proceeds
PR
to highlight intra-reader variation, including difficulties of the EL.
Influential Theory
L1 reading comprehension
One primary goal of reading research is to understand how readers represent a text after
they process it. In education, this representation is commonly referred to as reading
comprehension and, according to the RAND Reading Study Group (RRSG) and Snow (2002),
consists of three interrelated parts: the reading purpose, the text, and the reader. Interactive
reading models are based on Bartlett’s schema model (1932) which puts forth a theoretical
structure of how knowledge is processed, modified, and retrieved from a reader’s long-term
memory (Al-Issa, 2006; Duke et al., 2011; Rumelhart, 1980). Schema is not organized linearly,
like printed text, but in a combined network of knowledge. A founding principle of schema
8
theory is that of bottom-up and top-down (knowledge-driven) processes working interactively as
opposed to serially. Conceptual-level processing does not lie in the text, as the text itself does not
carry meaning but instead guides the reader as to what to retrieve from schemata (Carrell &
Eisterhold, 1983); essentially, more information is supplied by the reader than the text (Clark &
Silberstein, 1977). Accordingly, comprehension is achieved when a schema that “provides a
coherent explanation of the relations between the objects and events mentioned in a discourse” is
constructed. (Espinosa, 1996, p. 240)
IE
W
One view of successful comprehension at the discourse level is the well-developed
construction-integration (CI) model (Carlson, van den Broek, McMaster, Rapp, Bohn-Gettler,
Kendeou, White, 2014). As can be seen in Figure 1, the comprehension process is born from two
EV
levels of representation: integrating concepts across sentences, the textbase, and the subsequent
act of supplementing that textbase with background knowledge and inferences, the situation
PR
model (Cain & Oakhill, 2018; Duke, 2011; Wolfe & Woodwyk, 2010).
Reproduced with permission of copyright owner. Further reproduction prohibited without permission.