Top Lang Disorders
Vol. 25, No. 1, pp. 65-83
© 2005 Uppincott Williams & Wilkins, Inc.
Deep-Level Comprehension
of Science Texts
The Role of the Reader and the Text
Rachel M. Best, PhD; Michael Rowe, MS;
Yasuhiro Ozuru, PhD; Danielle S. McNamara, PhD
Many students from elementary school through college encounter difficulty understanding their
science textbooks, regardless of whether they have language disorders. This article discusses some
of the particular difficulties associated with science text comprehension and possible remedies for
facilitating and enhancing comprehension of challenging expository text materials. Specifically,
we focus on the difficulties associated with generating inferences needed to comprehend science
texts. The successful generation of inferences is affected by factors such as students' prior knowledge and reading strategies, and the manner in which science texts are written. Many students
lack the necessary prior knowledge and reading strategies to generate inferences and thus comprehend science texts only poorly. Further, science texts are typically "low<ohesion" texts, which
means that they require readers to generate many inferences andfillin conceptual gaps. Remedies
for overcoming comprehension difficulties include matching texts to students' knowledge level
and providing explicit instruction aimed at teaching students to use reading comprehension strategies for comprehension monitoring, paraphrasing, and elaborations. The computer-supported tool
iSTART Onteractive Strategy Training for Active Reading and Thinking) is introduced as a technological support to assist students and teachers in the teaching/learning enterprise. Key words:
comprehension, prior knowledge, reading strategies, science texts, text cohesion
A DREADED challenge, even for many stuJLJLdents with typical development, let alone
for those with language disorders, is reading
the science textbook. Even if students can
decode, read, and understand words on the
page, the challenge remains to put the words
together and make sense of them. For some
students, the words remain a string of words,
rather than a coherent, comprehensible, and
learnable message. Many teachers recognize
the problems that students face, as w^ell as
the possibility that students may not actually
read, let alone comprehend their textbook assignments. This problem is particularly pronounced in difficult subject areas such as science (Bowen, 1999; Snow, 2002).
From the University of Memphis, Memphis, Tenn.
Comprehension difficulties arise for a numWe express our thanks to Nickola Nelson and three
ber of reasons, ranging from poor wordanonymous reviewers regarding their helpful comments on an earlier draft of this article. This material
decoding abilities to the inability to use efis based on work supported by grants from the Instifective
reading strategies. In this article, we
tute of Education Sciences (IES R3056020018-02) and
the National Science Foundation (NSF: IERl 0241144) first discuss the cognitive processes involved
awarded to the fourth author, Danielle S. McNamara. in the successful comprehension of science
Any opinions, findings, and conclusions or recommentexts, drawing on a theoretical framework on
dations expressed in this material are those of the authors and do not necessarily reflect the views of IES or the basis of Kintsch's (1998) construction inNSE
tegration model (CI model) to explain the role
Corresponding author: Rachei M. Best, PhD, or Danielle of inference making in deep-level comprehenS. McNamara, PhD, Department of Psychology, 202 sion. Within the framework of the CI model,
Psychology Bldg, The University of Memphis, Memphis, TN 38152 (e-mail: rbest®mail.psyc.memphis.edu we then discuss comprehension problems
commonly observed in child and adolescent
or d. [email protected]. memphis. edu).
65
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TOPICS IN LANGUAGE DISORDERS/JANUARY-MARCH
populations when reading science texts, including problems resulting from one textrelated factor (text cohesion) and two readerrelated factors (domain-relevant knowledge
and reading strategies). Finally, we propose
text-based and reader-based remediation techniques to facilitate and enhance science text
comprehension and thus overcome comprehension problems associated with reading difficult texts.
DNFERENCING AS THE KEY TO DEEP
COMPREHENSION OF SCIENCE TEXTS
Reading comprehension can be defined
as "the ability to obtain meaning from w^ritten text for some purpose" (Vellutino, 2003,
p. 51). To comprehend successfully, the
reader must identify a series of letters as a
word, access the meaning of the word from
the lexicon or mental dictionary (Perfetti,
1985), and integrate individual word meanings into a coherent sentence-level representation (Vellutino, Scanlon, Small, & Tanzman,
1994). Deep comprehension, however, requires more than the mere interpretation of
individual sentences; the reader must also be
able to integrate individual sentence meanings into a coherent text-level representation (Kintsch, 1988, 1998). In other words,
to achieve deep comprehension, the reader
must construct a global meaning that integrates multiple sentences. The primary focus
in this article is on inference generation, a process that leads to text-based and knowledgebased connections to, and across, sentences.
The process of creating connections while
reading by generating inferences underlies
the successful and deep-level comprehension
of science texts.
Making inferences is a critical feature of
understanding the overall meaning of texts
(Kintsch, 1988) because inferences combine
the individual sentence meanings distributed
across the text into a coherent structure
(Gernsbacher, 1997). In other words, the
meaning of a text often remains fragmented
and disconnected without inferences because
texts normally do not (or cannot) state all
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the information relevant to the situations or
events. Therefore, to successfully comprehend a text, the reader must generate inferences to fill in "missing" information and build
a coherent mental model that incorporates all
the information in the text (Zwaan & Singer,
2003). For example, consider the following
sentence pair:
1. Plants lack a nervous system.
2. They carmot make quick responses to
stimuli.
The first inference that must be made after
the first sentence is that the pronominal referent for "they" is "plants." Comprehension
also depends on inferring that "the ability to
make quick responses" is somehow related to
the "nervous system."Building this inferential
connection requires the use of logic, syntactic
knowledge, and the ability to access knowledge from semantic memory, or to recall relevant information cited in earlier parts of the
text. This form of inference is called a "backward causal inference" because it involves attributing the cause of a phenomenon or event
described in a given sentence to a thing or
event described in a previous section of the
text. In the current example, successful, deeplevel understanding of this passage requires
the inference that the nervous system is responsible for quick responses. It illustrates the
point that successful comprehension of texts
requires more than word-decoding, vocabulary, and syntactic skills. Deep-level comprehension also requires the ability to make inferential links across individual sentences in
order to construct a global picture of underlying concepts.
According to Kintsch's (1988, 1998) CI
model, to accomplish this, readers must first
use their language-related know^ledge (e.g.,
morphology, syntax, semantics) to construct a
representation on the basis of the information
explicitly stated in the text. This initial level
of representation is often far removed from
a coherent model regarding the situations or
events that the text aims to describe; rather
the model reflects the information that is explicitly stated in the text. Thus, as illustrated
with the plant example, the meaning of a text
may remain fragmented and disconnected.
Deep-Level Comprehension of Science Texts
To go beyond a purely text-level comprehension, the CI model then posits that readers
must draw on additional information, such as
prior domain knowledge, or information cited
in previous parts ofthe text, and integrate this
information into a more complete mental representation of the events that the text aims
to describe. In this sense, the representation
that readers must construct to comprehend a
text's deeper meanings does not necessarily
correspond w^ith the representation that they
can construct directly from information provided explicitly in the text. The related implication of the CI model is that the quality of the
text determines the amount and type of inferences that the reader needs to generate in order to comprehend the events or situations in
a coherent manner.
TEXT COHESION AND INFERENCING
The CI model suggests that comprehension
can be improved when text cohesion is improved, thus reducing the need to make inferences. This improvement would be particularly true for readers who are unable to
generate inferences from difficult texts. Text
cohesion refers to properties of the text that
determine the degree to which readers need
to generate inferences to construct a coherent mental representation from the information explicitly stated in the text. Texts are
considered to be low cohesion w^hen constructing a coherent representation from the
text requires many inferences. Texts are considered high cohesion when elements within
the text provide more explicit clues to relations within and across sentences.
Cohesion is important at both the global
and local levels of text. The term global cohesion refers to the overall cohesion of the
text. Signals for global cohesion include introductory paragraphs, headers, summary paragraphs, and the semantic overlap between
paragraphs. Local cohesion concerns the relations between adjacent sentences. Signals for
local cohesion include semantic overlap between the sentences, connectives that explicitly describe the relation between sentences.
67
and explanations for difficulty terms or concepts. Texts are considered to be locally cohesive to the extent that the relationship between adjacent sentences (or clauses) is made
explicit through linguistic cues. For example, argument overlap can be increased by
repeating a referent in a sentence and using
fewer pronominal referents or synonyms. Below are examples of sentence pairs that do
and do not repeat the target referent "sexual
reproduction":
1. We tend to take the existence of sexual reproduction for granted. From an
evolutionary standpoint, this is a serious
puzzle.
2. We tend to take the existence of sexual reproduction for granted. However,
from an evolutionary standpoint, sexual
reproduction is a serious puzzle.
In this example, the second sentence pair is
presumed easier to process because the author has explicitly stated that sexual reproduction is "the puzzle." Also, by adding the
connective, however (other examples include
therefore, because, etc.), the author has provided more explicit cues for readers on the nature ofthe link between the sentences, generating the expectation among readers that the
nature of the puzzle is about to be explained.
A second aspect of cohesion is termed explanatory cohesion. This aspect concerns
the degree to w^hich the background information necessary to make connections among
relevant sentences is provided explicitly by
the text. Specifically, the presence of domainspecific background information necessary
for the coherent linking of sentences has a
large impact on explanatory cohesion. Returning to the previous example about plants,
readers would be more likely to recognize the
relationship between the two sentences, and
to construct a representation that integrated
their meanings if the sentences "Plants lack
a nervous system" and "They cannot make
quick response to stimuli" were preceded
by the explanatory sentence "The nervous
system is critical for animal's ability to produce quick response to stimuli." Adding explanatory information so that readers do not
have to rely solely on background knowledge
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TOPICS IN LANGUAGE DISORDERS/JANUARY-MARCH
helps the reader construct inferences which,
in turn, facilitate deeper comprehension.
A common problem of many science
textbooks, however, is that authors often
leave out information they assume to be
prior knowledge for target readers. Textbook
analyses have revealed that such omissions
even occur for information that is critical to
the construction of situations or events described by the text (Beck, McKeown, Sinatra,
& Loxterman, 1991). Studies also have shown
that improving text quality by increasing local
and/or global cohesion facilitates comprehension as assessed with a variety of comprehension measures, including recall, open-ended
questions, multiple-choice questions, and
card-sorting tasks. For example, the positive
influence of increasing text cohesion on comprehension is observed with both narrative
texts (e.g.. Beck, McKeown, Omanson, &
Pople, 1984) and expository texts (e.g.. Beck
et al., 1991; Britton & Gulgoz, 1991; Britton,
Gulgoz, & Glynn, 1993; Linderholm et al.,
2000), especially when readers do not have
sufficient domain knowledge to generate
inferences necessary for comprehension
(McNamara, 2001; McNamara, Kintsch,
Songer, & Kintsch, 1996; McNamara &
Kitsch, 1996).
READER ABBLITY AND INFERENCING
Revising a text to increase cohesion can reduce the need for readers to make inferences
about relationships among ideas in texts, but
there w^ill always be a need for students to generate inferences, if they are to build a mental model of the global concepts conveyed
by the text. Several theoretical accounts have
been posited for why some readers are able
to generate inferences better than others (see
McNamara & O'Reilly, 2004, for a review).
Some researchers have proposed that readers' ability to generate inferences is a function of their working memory capacity Gust &
Carpenter, 1992; Rosen & Engle, 1997; cf.
McNamara & Scott, 2001). According to these
theories, individuals who can hold and process more information in working memory
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are better able to make inferences, because
they can hold and process more of the text
at the same time (Engle & Marshall, 1983). In
this article, we focus on two other important
factors that influence readers' abilities to generate inferences. One factor concerns readers' preexisting domain-specific knowledge;
the other concerns their degree of competency in using reading strategies. We emphasize roles of domain knowledge and reading
strategies because, unlike working memory
capacity, they are subject to intervention.
Domain-specific knowledge refers to the
degree to which readers possess knowledge
that is specifically related to the text content. Reading strategies refer to a general
set of abilities that facilitate active processing of a text's content, and are considered
to be closely related to metacognitive abilities, such as knowledge of cognition and
the ability to monitor and regulate ongoing
cognitive processes (Hacker, 1998). Domainspecific knowledge and the ability to use
reading strategies are not completely independent. Competent readers are able to integrate them and use them in concert. On the
other hand, our research suggests that the
two factors are distinguishable because they
tend to be related to different aspects of
the reading comprehension process. Whereas
domain-specific knowledge is closely related
to how easily one can comprehend material
at a given level by accessing relevant knowledge, the ability to use reading strategies is
more closely related to the effort and use of
active processing techniques, such as elaborative and bridging inferences (Best, Ozuru, &
McNamara, 2004; Ozuru, Best, & McNamara,
2004).
Contributions of domain-specific
knowledge to inferencing
With respect to the benefits of domainspecific knowledge, readers with rich and organized topic-relevant knowledge structures
(i.e., schema) are at an advantage in making
inferences because they can gain quick access to relevant knowledge structures with
minimal reliance on explicit text-based input.
Deep-Level Comprehension of Science Texts
For example, readers are known to generate backward causal inferences (discussed
earlier) routinely when the topic of the text
is generally familiar, as they are in narrative texts that tend to regard familiar topics such as relationships between individuals and common problems encountered
in everyday life (e.g., Graesser, Singer, &
Trabasso, 1994). On the other hand, readers
are less likely to draw backward causal inferences when reading unfamiliar expository
texts (Noordman, Vonk, & Kempff, 1992).
These findings suggest that readers typically
do not generate backward causal inferences
when they have less knowledge about the
text topic. Also, a wealth of empirical evidence demonstrates that a reader's background knowledge facilitates and enhances
comprehension and learning from expository materials (e.g., Afflerbach, 1986; Chi,
Feltovich, & Glaser, 1981; Chiesi, Spilich, &
Voss, 1979; Lundeberg, 1987; McNamara &
Kintsch, 1996; Means & Voss, 1985; O'Reilly
& McNamara, 2002).
Having rich domain-specific knowledge
seems to be an important factor supporting inference generation, but it is not an essential.
Studies have show^n that some aspects of inference generation are observed even w^hen
knowledge about a topic is limited. For example, Noordman et al. (1992) conducted two
experiments in w^hich they showed that readers are able to draw^ backward causal inferences even when reading expository texts on
unfamiliar topics. In a similar vein. Singer,
Harkness, and Stewart (1997) found evidence
of inference generation for unfamiliar expository materials, that is, as long as the readers were not rushed in their examination
of the texts. Together, these studies show
that readers sometimes generate inferences
even when they possess little domain-specific
knowledge, indicating that inference generation depends not only on the reader's knowledge level, but also on other factors. Likely
candidates include discourse cues that signal
the nature of texts, time constraints on tasks,
and readers' perceptions of their reading goals
(McKoon & Ratcliff, 1992).
69
Rich domain-specific knowledge also
interacts in interesting ways with text-based
factors. McNamara and colleagues (e.g.,
McNamara, 2001; McNamara et al., 1996;
McNamara & Kintsch, 1996) investigated
the interactions of text cohesion and prior
domain-specific knowledge in text comprehension by manipulating text cohesion and
measuring the effects on comprehension
by low- and high-knowledge readers at
the middle-school and college levels. Interestingly, whereas low-knowledge readers
demonstrated better comprehension when
reading high-cohesion texts, high-knowledge
readers experienced better comprehension
when reading low-cohesion texts. One interpretation of this finding is that the redundancy between information in high-cohesion
texts and background knowledge led highknowledge readers to engage in passive
processing, thus keeping their comprehension at a surface level (see also Gilabert,
Martinez, & Vidal-Abarca, 2005). According
to this explanation, increased text-based
cohesion may interfere with high-know^ledge
readers' ability to actively process texts by
decreasing the need for spontaneously generated inferences. In contrast, low-cohesion
texts require readers to generate many
inferences. Thus, high-knowledge readers
are induced by low-cohesion text to work
more actively to integrate information in the
text with their prior know^ledge. This active
processing allows high-know^ledge readers
to achieve deeper levels of comprehension.
However, low-knowledge readers do not
possess the knowledge necessary to generate
the inferences required by low-cohesion text.
As a consequence, low-know^ledge readers
are disadvantaged by a combination of
low text-cohesion and low domain-specific
knowledge.
There is also evidence that readers with
high reading ability (as measured with the
Nelson-Denny test; Brown, Fishco, & Hanna,
1993) and high domain knowledge perform equally well when reading either lowcohesion or high-cohesion versions of texts
(O'Reilly & McNamara, 2004). They found
70
TOPICS IN LANGUAGE
DisoRDERS,(fANUARY-MARCH 2005
that only high-knowledge readers with low
scores on the Nelson-Denny Reading Test
benefited from reading low cohesion texts. In
contrast, high-knowledge readers with higher
scores on the Nelson-Denny Reading Test
benefited from reading high-cohesion texts.
This pattern of results indicates that whereas
high-knowledge readers with lower reading
abilities tend to process texts actively only
when the low-cohesion version of the text
demands the active generation of inferences,
high-know^ledge readers with good reading
abilities tend to actively process texts regardless of the text cohesion level or task demands. This research suggests that how actively a reader processes text by generating
inferences may be influenced not only by the
reader's domain-specific knowledge but also
by an ability to use knowledge actively and
strategically when reading science texts. In
the next section, we consider the role of reading strategy use in generation of inferences
that facilitate deep-level comprehension of
science texts.
Contributions of reading strategies
to inferencing
Solely having domain-specific knowledge
is often not enough; to be used effectively,
readers must deliberately activate their knowledge or it may remain somew^hat "inert" and
fail to contribute to deeper understanding.
That is, students may have some domain and
general knowledge that can be used to generate inferences and thus understand a text
at a deeper level, but they may not realize that their knowledge is applicable to a
particular reading situation. Indeed, several
researchers have found that readers sometimes encounter difficulty invoking preexisting knowledge in novel situations (e.g.,
Bransford, 1979; Hasher & Zacks, 1979;
Nitsch, 1977).
This situation is where reading strategy contributions come into play. Competency in
reading strategy use provides an additional
factor that influences inference generation
while reading science texts. The notion that
reading strategy use facilitates inference gen-
eration (and hence, deep comprehension) is
bolstered by several sources of evidence. According to McNamara and Scott (2001), the
use of reading strategies, such as the careful monitoring of text contents at the time of
reading, is an important component of reading comprehension ability. Good comprehenders (e.g., as assessed using the Nelson-Denny
Reading Comprehension Test) are more likely
to generate inferences that repair conceptual gaps between clauses, sentences, and
paraphrases (Cain & Oakhill, 1999; Long,
Oppy, & Seely, 1994; Magliano & Millis, 2003;
Magliano, Wiemer-Hastings, Millis, Munoz, &
McNamara, 2002; OakhiU, 1982,1984; Oakhill
& Yuill, 1996). Furthermore, good comprehenders have more metacognitive knowledge
(Baker, 1982; Wong, 1985) and are more likely
to use reading strategies to repair gaps in
their understanding than poor comprehenders (e.g.. Garner, 1987; Long & Golding,
1993; Oakhill, 1982, 1983). Finally, interventions that promote the active and strategic
use of knowledge has been show^n to improve
reading comprehension (e.g., Bereiter & Bird,
1985; Bulgren, Deshler, Schumaker, & Lenz,
2000; Chi, de Leeuw, Chiu, & La Vancher,
1994; Cornoldi & Oakhill, 1996; Kucan &
Beck, 1997; McNamara, 2004, McNamara
& Scott, 1999; O'Reilly, Best, & McNamara,
2004; O'Reilly, Sinclair, & McNamara, 2004a,
2004b). Collectively, these studies indicate
that reading strategies are important to successful comprehension, and that readers
can be taught strategies to improve their
comprehension.
CHALLENGES OF COMPREHENDING
SCIENCE TEXTS
Students of all ages have been found
to experience difficulty comprehending and
learning from science texts (Brand-Gruwel,
Aarnoutse, & Van den Bos, 1998; Nichols,
Rupley, & Willson, 1997). The problems with
science textbook comprehension, however,
are particularly pertinent among students at
the elementary-school and middle-school levels, when children are first exposed to them.
Deep-Level Comprehension of Science Texts
Science texts contain difficult vocabulary and
syntax, and also place greater emphasis on
inferential thinking and the use of prior
knowledge (Allington, 2002). The increased
exposure to challenging expository materials, including science textbooks, at a time
when domain knowledge is still developing
places greater cognitive demands on young
readers, and may account for some of the reading comprehension difficulties experienced
by children in the third to fifth grades, which
is sometimes called the fourth-grade slump
(McNamara, Floyd, Best, & Louwerse, 2004;
Meichenbaum & Biemiller, 1998; Sweet &
Snow, 2003).
In this section of the article, we discuss factors that account for why the comprehension
of science text is particularly difficult for many
students. We focus on three factors of reading comprehension identified earlier in the
discussion (text quality, background knowledge, and reading skill). Our goal is to provide
a detailed picture of the nature of problems
commonly experienced by students in this
particular curricular area to assist clinicians in
devising possible remedies.
KNOWLEDGE DEFICITS AND
MISCONCEPTIONS
As noted previously, the CI model of text
comprehension emphasizes domain-specific
knowledge as an important factor driving text
comprehension. Further, a number of studies
indicate that knowledge is, arguably, the most
important factor determining expository text
comprehension (e.g., Afflerbach, 1986; Chi,
Feltovich, & Glaser, 1981; Chiesi, Spilich, &
Voss, 1979; Lundeberg, 1987; Means & Voss,
1985). According to this view, it is inevitable
that many students experience significant difficulty comprehending expository texts and,
in particular, texts dealing with scientific concepts because their existing knowledge is limited. Science textbooks, along with other expository texts, are used in the classroom for
the purpose of providing ne^v information
to students. Thus, it is not surprising that
most students in the classroom have a little to
71
no background knowledge about the domainspecific expository text content when they
first encounter it.
Students' knowledge deficits may take, at
least, two different forms. First, many students
may lack the knowledge of specific concepts
outlined by the text (e.g., osmosis, gravity,
etc.). As our earlier plant example indicated,
w^hen students do not have a sufficient understanding of a particular concept, they often
have a problem generating inferences to link
concepts within or across sentences. As a result, their understanding of the text remains
fragmented and isolated, causing a failure to
form a coherent mental representation of the
overall text content.
Second, students' knowledge deficits may
take the form of preexisting misconceptions based on common knowledge or personal experience, rather than scientific concepts. Understanding scientific phenomena
often requires adopting a completely different
perspective from that acquired from everyday perceptual experiences. Indeed, Vygotsky
(1978) pointed out the problem of integrating spontaneously developed knowledge with
"scientific" or "theoretical" concepts, which
are abstract in nature and cannot be acquired
from direct perceptual experience. For example, children's direct perceptual experience
of the movement of the sun in the sky can create the misunderstanding that the sun moves
across the sky. This perceptual experience
can make it difficult for children to understand that the sun moves as a function of the
earth rotating and revolving around the sun,
and not the sun's movement around the earth.
Children with misconceptions about the
movement of the sun may fail to comprehend a text about the solar system accurately
because they cannot reconcile information
stated in the text with preexisting background
knowledge gained from everyday perceptual
experiences. According to the CI model,
the utilization of preexisting misconceptions
(e.g., incorrect background knowledge) in
reading comprehension can give rise to the
construction of an inaccurate mental model
of the situation (by making inappropriate
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TOPICS IN LANGLIAGE DISORDERS/JANUARY-MARCH
inferences), which contrasts with the scientific model of the situation intended by the
authors of the text. Such misconceptions may
give rise to what Piaget (1985) described
as the "assimilation" of the incoming information to the preexisting knowledge structure, instead of "accommodation" of the student's current knowledge structure to the
text. Thus, for students with misconceptions,
deep, or accurate, text comprehension requires not only accessing and using prior
background knowledge, but also recognizing
misconceptions, or contradictions between
their prior knowledge and the text content.
Perhaps most importantly, successful comprehension requires that readers subsequently repair any erroneous aspects of their mental
models (Chi et al., 1994).
TEXT COHESION INFLUENCES
REVISITED
As discussed earlier, several studies have
shown text with high cohesion to be particularly beneficial to readers with less knowledge about the text domain (Beck et al., 1991;
Britton et al., 1993; Britton & Gulgoz, 1991;
McNamara, 2001; McNamara et al., 1996;
Vidal-Abarca, Martinez, & Gilabert, 2000). It
is important to bear in mind that there is not
an optimal level of explicitness or cohesion
for all readers. Rather, comprehension success also depends on the knowledge level and
reading strategies that the reader brings into
the reading situation. As mentioned earlier,
high-cohesion text is not necessarily helpful
to high-know^ledge readers with a low level of
competency in reading strategy use. It is theorized that this reverse cohesion effect occurs
because high-knowledge readers are more
likely to actively engage with and comprehend texts that contain fewer cohesion cues.
However, many, if not most, students lack the
necessary domain-specific knowledge to generate inferences when reading their science
textbooks, particularly if the books are w^ritten with low cohesion.
The point here is that students' knowledge deficits often are compounded by a general lack of cohesion in science textbooks.
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Moreover, there is reason to believe that the
selection of science texts may be carried
out by textbook selection committees without sufficient attention to all of the textual
features that influence the understandabUity
of the texts. First, texts are often classified
and selected on the basis of traditional readability formulas that rely on simple indices
such as word frequency, word length, and
sentence length (Beck et al., 1991; Britton
et al., 1993). According to these formulas,
texts that comprise short words and sentences are considered less difficult, and thus
more suitable for novice readers. Short words
tend to be more frequently used and encountered, and thus are considered to be de facto
more familiar (Zipf, 1932). Short sentences
place fewer processing demands pertaining to
lower level cognitive process (e.g., syntactic
processing), but short sentences do not necessarily facilitate deep-level comprehension
because they offer fewer cohesion supports
for low-knowledge readers. That is, texts characterized by short words and short sentences
are likely to lack adequate cohesion because
the sentences are not likely to contain connectives. In addition, a focus on readability in
terms of short words and sentences is likely
to diminish the focus on elements of cohesion such as referential and explanatory cohesion. The lack of cohesion supports built into
the text places other demands on the reader,
such as the need to make inferences about
the nature of the links within and between
sentences, which will tend to interfere with
higher level processing. In sum, a reliance on
readability formulas is likely to result in a lack
of attention to other textual properties that
have an important bearing on deep-level text
comprehension.
A second complicating factor is that textbooks are written by experts on the topic.
Experts' high-level knowledge of a subject
matter can interfere w^ith writing for less
knowledgeable readers such as school students (Britton et al., 1993). A large body of
evidence indicates that people have difficulty
taking a perspective of others who are dissimilar to themselves (for a review, see Nickerson,
Deep-Level Comprehension of Science Texts
1999). In other words, vast domain knowledge, which is obvious and natural to domain
experts, can lead them to assume an unrealistic amount of prior knowledge on the part of
readers (Beck et al., 1991). As a consequence,
textbooks often lack sufficient background information for non-domain-expert readers to
understand the content, impeding the formation of a coherent situation model in these
readers' minds.
READING STRATEGY EVELUENCES
REVISITED
As reported earlier, the use of strategic reading practices, such as the active generation of
inferences using backward causal inferencing,
plays a critical role in the deep-level comprehension of science texts. However, there are
reasons to believe that it is difficult to attain
sufficient competency in reading strategy use
by the time children begin to read challenging science texts. We discuss two possible reasons why students struggle to acquire and apply reading strategies.
First, students, particularly those at the
elementary school level, may not have mastered efficient and accurate lower level comprehension skills (e.g., efficient word decoding), which are necessary for the execution of
higher level comprehension processes (e.g.,
strategic reading practices, metacomprehension, backward causal inferences). Slow or
inaccurate word recognition skills may affect comprehension by consuming too much
working memory capacity, thus constraining
resources that could be used for deep comprehension processes involving inference generation (Cain, Oakhill, & Bryant, 2004; Curtis,
1980; Hannon & Daneman, 2001; Perfetti,
1985). Many studies have indicated that children may not have mastered word decoding
skills by the time they are introduced to expository materials (Brand- Gruwel, Aarnoutse,
& Van den Bos, 1988; Mommers, 1987; Roth,
Speece, Cooper, & De La Paz, 1996; Taschow,
1969; Vellutino, 2003) and, thus, are stUl struggling with reading fluency w^hen confronted
with texts that introduce challenging new
concepts.
73
Second, even if students have acquired sufficient word decoding ability, they still may
not have attained sufficient competency in using reading strategies necessary for successful comprehension and learning from science
texts. For many students, strategic reading
practices needed to support inference generation are unlikely to develop automatically
just by virtue of reading narrative texts in
classroom reading activities. Instead, knowledge of reading strategies and effective use of
these strategies in comprehending challenging expository texts may need to be explicitly
taught.
A report published by Educational Testing
Service (ETS, 2004) on current fourth-grade
reading instruction indicated that teachers do
provide students with instructions on techniques such as "predicting contents of the
material they are reading" and "making generalizations and inferences about reading contents"(p. 24). However, there is little evidence
that deep-level reading strategies are explicitly taught as a part of the normal curriculum. It seems that the majority of reading comprehension training administered at school
to children focuses on lower level reading
skills such as efficient word decoding skills
(Mommers, 1987; Nichols et al., 1997; Pressley & Wharton-McDonald, 1997; Roth et al.,
1996; Wilson & Rupley, 1997). It is a concern if such instruction occurs in the absence,
or at the expense, of the explicit teaching of
higher level processes, such as strategic reading practices. Investigations regarding the use
of metacognitive strategies, in fact, have indicated that teachers spend little time giving
metacognitive and strategy-oriented instruction pertaining to reading comprehension
(Baker, 1996; Graesser, Person, & Magliano,
1995; Kurtz, Schneider, Carr, Borkowski, &
Rellinger, 1990; Moley et al, 1992). Furthermore, reading problems for children who
have learned higher level reading skills such
as inference generation strategies in the context of texts with a familiar narrative structure
may only become apparent when they are
faced with the additional demands of expository texts about unfamiliar topics. The crux
74
TOPICS IN LANGUAGE DISORDERS/JANUARY-MARCH
of the problem is that students are less likely
to generate inferences when dealing with unfamiliar materials, such as expository science
texts (Noordman et al., 1992), even though
this is exactly the situation in which they
need to generate inferences for understanding and learning new information from the
texts. In sum, it is important not to ignore
the instruction of comprehension strategies
at early stages of reading development. Young
readers need explicit instruction in decoding
words, but also need explicit instruction in
how to strategically comprehend complex information.
REMEDIATION STRATEGIES
Thus far, we have emphasized the importance of inference generation in the comprehension of science texts and the difficulties
that many readers encounter generating inferences when reading science texts. The final goal of this article is to discuss possible remediation strategies to enhance science
text comprehension. The discussion focuses
on two lines of research currently being pursued in our research laboratory at the University of Memphis. One involves text difficulty,
specifically the ways in which we can assess
text difficulty and select texts most likely to facilitate comprehension among different kinds
of readers. The other involves designing instructional programs to teach readers active
reading practices based on metacognition and
reading strategies that facilitate the deep-level
comprehension of science texts.
Text-based modiflcations: Identiiying
texts appropriate for the reader
As the prior discussion has indicated, text
characteristics have an important impact on
the readers' deep-level comprehension of science texts, particularly for readers who have
less domain knowledge, which, we believe,
comprise a majority of school students reading science texts for the purpose of acquiring knowledge. Reviews of existing research
indicate that many of the expository texts
2005
used in actual classrooms are too demanding
for students who lack sufficient background
knowledge because they are "low cohesion"
texts that require the reader to generate many
inferences, •written by experts who overestimate students' background knowledge. Given
this situation, it is critically important for
researchers and educational practitioners to
find means to identify and select texts that are
more appropriate for students, especially for
those who lack necessary domain knowledge
or who have language disorders that interfere
with their ability tofillin the gaps.
As Britton et al. (1993) and others (e.g..
Beck et al., 1991) have pointed out, current
text classification systems and selection criteria (e.g., reading grade level formulas) appear
to be oversimplistic, focusing on surface attributes of the text (e.g., word and sentence
length) that affect relatively low levels of processing. One of the reasons for the use of
simplistic methods is there has been no alternative method to analyze and classify text difficulty in a systematic manner (Britton et al.,
1993). Readability measures do not take into
account local cohesion (i.e., argument overlap between adjacent sentences) or explanatory cohesion, which contribute critically to
how easily a reader processes a text. Thus, a
more adequate assessment of text difficulty
would include measures of local and global
cohesion.
Work being conducted in our lab has
led to the development of a computerized
tool, called Coh-Metrix (Graesser, McNamara,
Louwerse, & Cai, 2004), which is designed
to analyze various features of texts, including cohesion. The tool automatically measures
text characteristics predictive of text difficulty using the latest techniques available in
computer science and computational linguistics. Coh-Metrix provides measures of text cohesion as well as other text difficulty measures (e.g., word frequency, average sentence
length, and type token ratio).
The cohesion measures focus on the number of connectives, pronouns, degree of
argument overlap between sentences, and
conceptual overlap between sentences across
Deep-Level Comprehension of Science Texts
adjacent and distant sentences using Latent Semantic Analysis (Landauer & Dumais,
1997), which was developed by researchers at
the University of Colorado. As discussed earlier, the number of connectives contained in
a text is an important measure of cohesion.
Connectives provide explicit cues for readers
when generating inferences. Similarly, argument overlap, which is measured by the degree to which adjacent sentences share content words, is known to be related to local
cohesion because having lexical overlap helps
readers relate sentences and construct higher
text level representation. However, simple
word matching may not provide a comprehensive measure of overlap.
As described by Landauer and Dumais
(1997), Latent Semantic Analysis (LSA) bolsters argument overlap analysis by computing the co-occurrence of two or more Unguistic units (w^ords, phrases, sentence, etc.)
with respect to a given reference point within
the LSA space. The LSA space is a mathematical, dimensional space for plotting vectors
derived from the linguistic units (e.g., sentences) being compared. Landauer and Dumais showed that LSA betw^een two linguistic
units correlates w^ith the conceptual similarity
among linguistic units, not just the identical
words each unit possesses. The LSA is therefore useful for measuring the approximate cohesion between two sentences in terms of
the overlap of conceptually related w^ords, not
just the words themselves. Such a conceptual
overlap between words would not be captured in a simple analysis of argument overlap
(i.e., word matching).
Ultimately, the Coh-Metrix tool is designed
to be used by researchers and educational
practitioners to measure aspects of texts that
contribute to reading comprehension difficulties. Although the tool does not yet include established criteria for distinguishing
low-cohesion texts from high-cohesion texts,
it does offer information about differences in
levels of cohesion measures. Researchers and
educators can then use these as a guideline
for selecting texts that are more or less cohesive and thus more appropriate for read-
75
ers with more or less domain knowledge. A
Coh-Metrix analysis of texts from published
studies of text cohesion (McNamara, Ozuru,
Louwerse, & Graesser, 2005) indicates that
the tool reliably captures critical aspects of
text manipulations (argument overlap, increase of explanatory cohesion) such as those
used in experimental research of text revision
(e.g.. Beck et al., 1991; McNamara et al., 1996;
Voss & Silfies, 1996).
Overall, we believe that research investigating text features will provide a more systematic and reliable method of measuring and
classifying text difficulty. Furthermore, new^
measures of text difficulty should offer an
important alternative to the simplistic measures currently used (e.g., reading grade level
formulas). Consequently, tools such as CohMetrix are anticipated to positively contribute
to the process of assigning more appropriate texts to students who may lack sufficient
background knowledge.
Reader-focused interventions: Teaching
reading strategies
If problems that students encounter understanding their science textbooks stem largely
from deficits in, or erroneous, background
knowledge, it is important to find remedies that compensate for "poor knowledge"
by teaching them comprehension strategies.
Even if a tool such as Coh-Metrix (Graesser
et al., 2004) becomes capable of providing information that helps improve text cohesion,
it is impractical or even impossible to provide all the cohesion cues and background information necessary for low-knowledge readers. In addition, such an overly cohesive text
might interfere with the active processing that
is necessary for deep-level comprehension by
higher ability readers (e.g., McNamara et al.,
1996). Thus, reading comprehension difficulties relating to the comprehension of science
texts need to be approached not only from the
perspective of the text, but also from the perspective of the reader.
As we have noted throughout the article,
it is the nature of education that readers of
science texts read them for the purpose of
76
TOPICS IN LANGUAGE
DisoRDERS/jANtJARY-MARCH 2005
learning new information. Thus, most readers approach the task of reading science texts
with low levels of topic-relevant knowledge.
The circularity of this phenomenon is such
that readers cannot comprehend the text contents at a deep level without learning new
concepts or information from the text. This
contrasts w^ith the reading process of narrative texts, where comprehension is supported
by sufficient knowledge about the content so
that inferences necessary for deep-level comprehension may be generated relatively automatically. The comprehension processes necessary for science texts, therefore, require
greater effort because the information relevant to understanding a given sentence, or relations between sentences, is not often readily accessible in long-term memory. Readers
often need to search for relevant information through various forms of linking (e.g.,
association, analogy). Effortful generation of
inferences is psychologically different from
the inferences that readers generate to understand familiar narrative materials (see Kintsch,
1993).
For this reason, not only readers, but also
instructors, need to be aware of the nature of
the difficulties associated with reading comprehension and learning from science texts.
They also need to actively deal with these
problems by (1) explicitly discussing with students the nature of comprehension problems
pertaining to science texts and (2) teaching
and using techniques that help students with
limited prior topic knowledge deal effectively
and strategically with the challenges of science text comprehension.
Numerous reading strategy interventions
have been developed and tested. There is converging evidence to show that the provision
of explicit reading strategy training. In which
readers are taught and trained to actively process a text using specific reading strategies, is
effective for facilitating deeper level comprehension of texts. These intervention programs
vary from those focusing on the training of
lower level skills (Adams, 1990), motivational
aspects of reading (Guthrie, 2003), and higher
level strategies provided to low-achieving
adolescents with comprehension difficulties
and language disorders (Bulgren et al., 2000;
Deshler, & Denton, 1984; Fisher, Schumaker,
& Deshler, 2002).
Our aim here is not to provide an exhaustive list of interventions, but to show the ways
in which reading strategies can help students
overcome knowledge deficits. We focus predominantly on strategy training developed in
our laboratory at the University of Memphis
for typically developing students from the
middle-school level and beyond (i.e., students
most frequently exposed to science texts).
Generally, our efforts have concentrated on
teaching reading strategies to help typical students who lack topic-relevant knowledge for
comprehending science texts. In the remainder of this section, we first describe four specific reading strategies that are known to contribute to active processing of science texts,
and then describe Self-Explanation Reading
Training, a reading strategy training intervention program developed in our lab that provides instruction and practice on the use of
these specific strategies.
Comprehension monitoring
Students' ability to monitor comprehension
critically and accurately is the foundation of
strategic and active reading (Hacker, 1998).
That is, readers need to be aware of not
only whether they are having comprehension
problems, but also the nature of the problem
they encounter (e.g., word meaning, syntax,
or relations between sentences). However,
there is evidence that most readers are rather
poor at monitoring their own comprehension (e.g., Glenberg, Wilkinson, & Epstein,
1982). Thus, providing training to improve
comprehension monitoring is important to
promote the active and strategic processing of
expository texts. Specific methods of teaching comprehension monitoring include selfquestioning and checking texts for content
consistency. Research conducted with elementary school students has indicated that
"poor comprehenders" benefit from comprehension monitoring training in w^hich they are
taught to self-question what they understand
Deep-Level Comprehension of Science Texts
about the material and to check the text for
content consistency (Baker, 1985).
Paraphrasing
Paraphrasing is an important technique
for facilitating the active processing of texts
(Rosenshine & Meisler, 1994). Paraphrasing
requires transforming the surface characteristics of the sentence by replacing the content
words or syntactic structure of the sentence
with equivalent forms, hence forcing readers
to process the information actively by accessing related but different lexical items. Paraphrasing also externalizes one's understating
of the information in the text, which, in turn,
helps readers monitor comprehension more
closely. Difficulty when paraphrasing is a clear
sign of a comprehension problem.
Elaboration
Generating elaborative inferences based on
the reader's personal experience or commonsense knowledge relating to information described by the text is assumed to be useful for facilitating deep-level comprehension
(McNamara, 2004). For one thing, elaborative inferences help readers overcome gaps
in domain-specific knowledge. As our discussion has indicated, understanding texts at a
deep level requires the reader to generate inferences tofillin gaps present in text-based information, such that information distributed
across text can be encoded as coherent and
in an integrated manner. Also, the use of prior
knowledge is important for helping the reader
retain novel information learned from text in
memory; new^ information cannot be learned
and retained without being integrated with
prior knowledge (Pressley et al., 1992). Thus,
when readers do not have sufficient domain
knowledge, they need to forcefully integrate
the new information in the text with existing knowledge by forming a link between the
new information and indirectly related general knowledge or personal experience. As
discussed earlier, however, relying on general
knowledge and/or personal experience alone
may lead to the formation of an inaccurate
situation model (e.g., Vygotsky, 1978). This
11
problem can be minimized if readers combine
elaborations based on general knowledge and
personal experience with inferences using information cited in the prior sections of the
text.
Bridging
The process of generating inferences using
information stated in previous sections of the
text plays an integral role in helping readers
build a global representation ofthe text. More
cohesive texts provide a stifficient amount of
background information for readers to build
a global understanding of the overall text by
continuously adding and integrating newly
introduced information with previously
cited information (Clark & Haviland, 1977;
Gernsbacher & Hargreaves, 1988). Although
many school texts may not contain sufficient
background information and the cohesion
cues that help readers link information
presented in different sentences in an appropriate manner (e.g.. Beck et al., 1991),
students should be taught to maximize the
use of information provided within a text
to understand the meaning of individual
sentences as well as the overall meaning of
the text. When struggling to disambiguate
and/or narrow down the meaning of a given
sentence, readers need to learn that information stated in the previous section can be a
powerful and reliable source of information
that can aid their comprehension.
Self-Explanation Reading
Training (SERT)
In summary, we postulate that the combination of the above-mentioned reading strategies constitute a powerful combined tool for
helping readers comprehend and learn from
expository texts. The essence ofthe approach
is to teach readers to compensate for both
knowledge and text deficits by learning to
draw deeper inferences based on the active,
constructive processing of connections that
can be found in texts even when the cues are
not immediately obvious.
In our lab we have developed a reading
strategy training program for promoting the
78
TOPICS IN LANGUAGE DISORDERS/JANUARY-MARCH
active and strategic processing of science
texts by combining the above specific reading strategies through a method called selfexplanation. Self-explanation refers to the process of explaining text contents to oneself
while reading (Chi, Bassok, Lewis, Reimann,
& Glazer, 1989). There are a number of benefits to using the self-explanation method, including facilitating inference generation and
repairing erroneous mental models by increasing the extent to which the reader monitors the ongoing comprehension process
(Chi, de Leeuw, Chiu, & LaVancher, 1994).
Our program, called Self-Explanation
Reading Training (SERT; McNamara, 2004;
McNamara & Scott, 1999) uses the selfexplanation technique to teach effective reading comprehension strategies (monitoring
comprehension, paraphrasing, elaborative inferences, and generating bridging inferences).
SERT is divided into three components: (1)
Introduction, in which students are taught
the reading comprehension strategies; (2)
Demonstration, in which the strategies are
demonstrated to students; and (3) Practice,
in which students practice self-explaining
science texts using the reading strategies. In
this way, our program aims to promote both
explicit understanding of reading strategies
(declarative knowledge) and the skills of applying the strategies w^hUe reading challenging science texts (procedural knowledge).
Thus far, research conducted with students
in middle-school, high-school, and college levels has indicated that SERT increases selfexplanation quality and subsequent comprehension of science texts. Eor example, a study
conducted with 38 middle-school students
(O'Reilly & McNamara, 2004) demonstrated
that students trained with SERT performed
better on a comprehension assessment than
untrained students. In a study conducted with
42 college undergraduates, McNamara (2004)
found that SERT improved the quality of
self-explanations (as reflected in students' increased use of elaboration and bridging strategies) and subsequent comprehension of science texts and that low-knowledge students
benefited most from SERT training. Likewise,
2005
O'Reilly, Best, and McNamara (2004) found
that SERT improved science text comprehension among low-know^ledge high-school
readers. In this study, low-knowledge readers
trained by SERT performed better on a comprehension task than low-knowledge readers
trained using a different form of reading strategy training (Previewing) or low-knowledge
readers assigned to a control condition.
Current research in our lab has begun to focus more closely on the effects of individual
differences in reading strategy intervention,
with the aim being to build a studentadaptive reading strategy tutor called Interactive Strategy Training for Active Reading and
Thinking (iSTART; McNamara, Levinstein, &
Boonthum, 2004). In its prototype, iSTART
is an automated, interactive tutor, which currently incorporates SERT but is being expanded to include other reading strategies.
The current iSTART system continuously evaluates what students know about the strategies
and their ability to use the strategies so as to
tailor scaffolding and feedback to the level of
the student. Eor example, students struggling
to produce elaborations are encouraged to
"add more details" to their self-explanations.
Evaluations of iSTART indicate that the system improves the students' use of reading
strategies and comprehension of science texts
at both the middle-school and college level
(Magliano et al., 2005; O'Reilly et al., 2004a,
2004b). Our future research will continue to
explore the relations between individual differences and the benefits of reading strategy
training to develop reading strategy interventions that are increasingly adaptive to each student's needs.
Preliminary findings suggest that automated versions of reading strategy trainers are
useful not only for individual intervention but
also for assisting teachers to implement strategy training in educational settings such as
the classroom. One of the factors impeding
students' ability to acquire and practice effective reading comprehension techniques stems
from the limited explicit instruction regarding strategies for comprehending expository
texts. The low level of explicit instruction
Deep-Level Comprehension of Science Texts
in classroom settings may, in part, be caused
by limitations in teachers' knowledge about
reading strategies, or a limited allocation of
classroom teaching time. Computerized programs may make it easier to implement strategy training in schools because they can reduce the demands on teachers.
CONCLUSIONS
This article has addressed problems students experience in comprehending their science textbooks by focusing on how attributes
of texts (specifically factors involving text cohesion) interface with attributes of readers
(specifically world knowledge and discourse
knowledge and strategies). Our discussion
of remediation strategies has considered the
ways in which researchers and educators can
help students better comprehend the language of their science textbooks. One method
is to match readers to texts, such that lowknowledge readers are given more cohesive texts to help overcome prior knowledge
deficits. Text analysis tools (e.g., Coh-Metrix)
allow us to grade text difficulty appropriately and distinguish low-cohesion from highcohesion texts. Alternatively, research into
79
text cohesion may be used to develop guidelines for future textbook writing, or to open
up the possibility of assigning different textbooks to different individuals.
A second method to overcome comprehension difficulties is to teach students reading
strategies. Many forms of strategy training are
available to help students of various ages and
levels overcome problems regarding the deeplevel comprehension of science texts. From
our perspective, techniques that help students identify problems in their understanding (e.g., comprehension monitoring) should
be combined with instruction aimed at overcoming deficits in prior knowledge (e.g., elaborative inferencing), with the ultimate goal
being deep-level comprehension. To accomplish such individualized and integrated approaches, future research must be designed
to more closely investigate relations between
individual differences and benefits of various
types of reading strategy intervention. Developing computerized versions of reading strategy interventions holds promise not only for
tailoring the training to the needs of the student, but also for making reading strategy
training more widely accessible in classroom
settings by supporting teachers in their instructional practices.
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