When a topic matters to you, does it matter if you read about it in a second
language?
Bokhee Na, Diane L. Schallert, & Eunjeong Jee
University of Texas at Austin & Pung-am High School
Paper presented at the annual meeting of the Literacy Research Association, Marco Island, FL
December, 2014
Purpose
This study investigated the role of emotional involvement in first and second language
reading comprehension. Readers’ emotions often become engaged while reading, and there is
evidence that aroused emotions can sometimes enhance and sometimes skew text comprehension.
However, except for the rich connection coming from a reader response perspective, research on
the role of emotions while reading is not extensive, especially for reading in a second language,
and many questions remain about how emotional and cognitive processes intertwine during
reading. In this project, we were interested in extending the work of Gaskins (1996) and others to
explore how adolescents’ culturally constructed emotions affected their reading comprehension,
and how this effect varied when reading in their first or second language.
Relevant Literature
Emotions in Classroom
We begin by reviewing the burgeoning literature on emotions in academic settings. From
their recognition of the narrowness of research on emotions and learning, Pekrun, Goetz, Titz,
and Perry (2002) called for a broader perspective, moving from a nearly sole focus on anxiety to
include four distinct categories of emotions: negative activating, negative deactivating, positive
activating, and positive deactivating emotions.
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In response, educational researchers have tried to identify the underlying mechanism of the
reciprocal relationship between emotions and cognitive antecedents, such as goals and
achievement (Pekrun, Frenzel, Goetz, & Perry, 2007; Linnenbrink & Pintrich, 2002).
Linnenbrink et al. (2002) posited that mastery goals, that is, goals to learn more in a domain,
tend to promote positive emotions such as happiness and hope whereas performance goals are
likely to arouse negative emotions such as anxiety and shame in reciprocal ways. Emotions also
influence cognitive engagement and performances like attention, memory, learning strategies,
motivation and self-regulations (Pekrun et al. 2007).
Emotions When Reading in One’s Native Language
In the general literacy field, research on emotions began in reaction to a preoccupation with
the cognitive aspects of reading. This research on readers’ emotions bloomed in the 1980s and
90s with an assumed distinction between research with literary texts and with expository texts.
Both camps conducted their research within the same cognitive traditions but differed in their
focus, approaches, and findings on emotions. As for literary texts, which are most likely among
text genres to arouse emotional response, Miall and Kuiken (1994, 1999) investigated how
emotions functioned during a reading experience and reported that stylistic features such as
foregrounding evoked readers’ feelings and guided readers’ experience. In a similar vein,
Dijkstra, Zwaan, Graesser, and Magliano (1994) categorized readers’ emotions into fiction and
artefact emotions and reported that fiction emotions induced by story structure such as suspense
affected the comprehension process. Furthermore, Sadoski and Goetz and their colleagues (1988,
1990, 1993) argued for the convergence of cognitive and affective response in readers, and
reported a consistently high correlation between mental imagery and affective response. Based
on empirical evidence, Kneepkens and Zwaan (1995) proposed a framework to encompass
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cognitive and emotional aspects of reading within contemporary models of comprehension and
memory, where emotions guide and support the reading process, particularly when there are
fewer textual cues.
A different stream of emotion research was conducted with expository texts. This line of
research started as an extended branch of attitude and interest research in psychology.
Nevertheless, the concern was arguably about the effect of emotions on text processing.
However, research with expository texts differed from the research with literary texts in the
following ways. First, the focus was on the emotions that readers brought to the text; in contrast,
research with literary texts was more concerned about emotions aroused by the text. For instance,
Gaskins (1996) investigated the effect of issue-related emotions and reported that readers
interpreted a passage about a basketball game to be favorable to their team affiliations. Baldwin,
Peleg-Gruckner, and McClintock (1985) studied whether topic interest affects reading
comprehension and found that readers understood better high interest materials rather than low
interest materials. Also, research with expository texts tried to control the effect of prior
knowledge in assessing affective effects. For example, Henk and Holmes (1988) statistically held
prior knowledge constant and found no significant effect of topic-related attitude on text
comprehension. Reutzel and Hollingsworth (1991) also treated prior knowledge as a
confounding variable in their study of attitude effects on text recalls. Thus, given the different
foci and approaches, research using expository texts reached a different conclusion about
emotions than did research using literary texts. Research with expository texts considered
emotional involvement to be an interfering variable in text comprehension whereas research with
literary texts regarded it as evidence of deep engagement.
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Although they used different texts and perspectives on emotions, several consistent themes
emerged from both camps. First, emotions play an active role in the reading process. Miall et al.
(1994, 1999, & 2002) argued that emotions have self-modifying powers that support selftransformation in readers. Kneepkens et al. (1994) posited that emotions direct readers’ attention
and guide comprehension process. A similar position was found with expository texts: attitude
and interest function as “a frame of reference” in reading (Baldwin at al., 1985; Reutzel et al.,
1991). Second, emotion is a multifaceted construct. Research with literary texts has distinguished
reader emotions into fiction emotion and artefact emotion: the former is aroused by the events in
a fictional world whereas the latter is the consequence of the author’s stylistic writing (e.g.
Dijkstra, et al., 1994; Kneepkens et al., 1994). Research with expository texts also has made
distinctions among emotions aroused by attitude, interest, and ego involvement (e.g., Baldwin et
al., 1985; Henk et al., 1991; Gaskins, 1996). Lastly, emotions are deeply associated with prior
knowledge. Research with literary texts has shown that readers’ affective responses are
inherently embedded in their prior experiences (Miall et al., 1994; Oatley, 1999), and research
with expository texts has also noted that attitude, interest, and ego-involvement are connected to
prior knowledge (e.g. Baldwin et al., 1985; Henk et al., 1991; Gaskins, 1996).
Much of the earlier work on readers’ affective responses has regarded readers’ emotions as
an individual phenomenon, but, in the 2000s, researchers have increasingly used socio-cultural
perspectives and posited that readers’ emotions are culturally situated and socially constructed
(Gee, 2001, 2002; Jimenez, Garcia & Pearson, 1996; Trainor, 2008). These views hold that
readers construct situated meanings from the text using their own cultural models and become
emotionally involved through representations of cultural experiences. Thus, readers’ emotions
are not solely individual but originate from institutional practices and social relations invited by
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the text. The implication from research on emotions in reading is that readers’ emotions are
multifaceted, playing an active role in the reading process intertwined with prior knowledge and
readers’ socio-cultural backgrounds.
Emotions When Reading in a Second Language
In research on reading in a second language, there have been few studies on readers’
emotions except for anxiety (Brantmeier, 2005; Saito, Horwitz & Garza, 1999; Sellers, 2000).
The result of reading anxiety research showed that second language readers’ emotional
experience varies across proficiency levels: Saito et al. (1999) and Sellers (2000) reported that
anxiety was found among foreign language learners of beginner and intermediate levels, and it
had a debilitating effect on reading comprehension, whereas Brantimeier (2005) reported that
minimal reading anxiety was found among advanced level learners, with no effect on reading
comprehension. These results suggest that second language readers’ affective responses can be
influenced by their proficiency levels. In addition, several researchers noted that emotions in
second language situations are also socio-culturally constructed, and that individuals experience
transactions in a second language as having lower emotionality than a first language (Dewaele,
2010; Pavelenko, 2007). Benesch (2012) connected this lack of emotional resources in a second
language with the lack of agency in second language learning and insisted that emotional literacy
(emotional lives) should be emphasized in second language learning.
Another issue with respect to second language readers’ emotions, as implied by the research
on reading in one’s native language, is that emotions aroused when reading in a second language
are associated with prior knowledge. Here, there are conflicting results about prior knowledge
and its effect on second language reading (Bernhardt, 2011; Grabe, 2009). Some studies (AbuRabia, 1998; Jimenez et al., 1996) showed that readers comprehend better when they were
5
reading culturally familiar text, whereas others found that prior knowledge played a weak or
debilitating role in reading in a second language (Bernhard, 1991; Elder, Golombeck, & Stott,
2004). As for this conflicting issue, Grabe (2009) and Bernhardt (2010) argued for the need for
more research on how background knowledge works in L2 reading.
In addressing these contradictory findings, Nassaji (2007) suggested that Kintsch’s (1988)
“construction-integration model” can help bring some coherence to the role of prior knowledge
in second language reading. In the construction-integration model, comprehension is explained
with the distinction between the “textbase model” and the “situation model.” The textbase model
emerges through the construction phase of text information (e.g., word recognition, syntactic
parsing), and the situation model emerges through the integration phase with prior knowledge
(e.g., inferences, elaborations). Nassaji (2007) suggested that individuals reading second
language texts use prior knowledge to create the textbase model and the situation model through
a parallel and synthetic, not linear or additive, process. In our study, we used the Kintsch model
to design our measures of reading comprehension and posited that, by examining adolescent
readers’ emotions as they read a text in their first language (Korean) and in their second language
(English), we could contribute to the debate about how emotions participate in meaning making.
Method
Participants and Context
Participants were 477 students (268 girls, 209 boys; age range: 15-17 years) learning English
as a foreign language at a public high school in a southern city of South Korea. These students
had received school-based English instruction starting in the third grade, with lessons lasting 1 to
2 hours per week in elementary school and 3 to 4 hours per week in middle school. At the time
of study (2013, summer), they were taking English for 4 hours per week, with most students
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receiving supplementary after-school instruction for 2 hours per week. Note that only students
whose parents had consented to their participation in the study were included.
As is typical for Korean academic high schools, English instruction was focused on the
receptive skills of listening and reading rather than the productive skills of speaking and writing.
This instructional focus was partly explained by the logistics of large class sizes (35 to 40
students), the reluctance on the part of the non-native English teachers to teach English in
English, and the strong “wash back” effect of the Korean college entrance exam that assessed
only the receptive skills of English. Although participants were familiar with reading tasks from
their previous English instruction, they were not as familiar with free recall or open-ended
questions as with multiple choice and short answer questions. Regarding their computer literacy,
these students were skillful and comfortable with reading texts on screen, a mode of reading that
had often been a part of learning activities in regular content classes from elementary grades on.
Materials
Reading passages. All texts were excerpted from articles found on internet news websites
such as BBC (http://www.bbc.com/), Korea Herald (http://www.koreaherald.com/), HITC sport
(http://hereisthecity.com/en-gb/sport/), and Mail online (http://www.dailymail.co.uk/news/).
These news articles were selected because they addressed issues deemed controversial in South
Korea at the time of the study and that had evoked diverse emotions among Korean high school
students in a pilot study. The topics included: (a) a dispute between South Korea and Japan about
an island, (b) a comparison between a Korean and a Japanese soccer player in the British Premier
league, (c) the potential harm of social networking websites for children’s development, and (d)
plagiarism in Korean education. These texts were expected to evoke topic-related emotions
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among our participants when they were read in their emotional version (see Appendix A for a
sample reading text, the island dispute between South Korea and Japan).
Beginning with these English-language authentic articles, we modified them to be equivalent
in length (253-256 words), lexical density, and syntactic complexity as measured using a webbased text analyzer (Ai & Lu, 2010; Lu, 2010). Guided by Gaskins (1996), we created the neutral
version of each text by changing only the names of locations, people, and years so as to distance
the text from the students. Once the emotional and neutral versions of each text were completed,
the texts were translated into Korean and assessed by two high school language arts teachers for
Korean accuracy and fluency. There were some readability gap differences between the English
and Korean versions: the English versions ranged from 10th to 12th grade for English language
learners but the Korean versions ranged from 8th to 10th grade for Korean students reading in
Korean, their native language.
Attitude and prior knowledge assessment. Guided by previous research (Fortner et al., 1991;
Reutzel et al., 1991), we used 5-point Likert scales to measure attitude (or interest) and selfperceived prior knowledge of the four topics. Students were asked to indicate their attitude and
prior knowledge on 5-point scales, with 1 representing low attitude or knowledge and 5 high
attitude or knowledge. In this attitude and prior knowledge assessment, they were asked about
eight topics in total, with the four target topics intermixed among four other distraction topics.
(See Appendix B for attitude and prior knowledge scales).
Emotional involvement assessment. Guided by Pekrun et al. (2002), we used four categories
of emotions to measure emotional involvement in the four texts: positive activating (pride, hope,
excitement), positive deactivating (gratitude, satisfaction, relief), negative activating (anger,
worry, shame), and negative deactivating (boredom, sadness, disappointment). Immediately after
8
reading each text, students were asked to indicate the degree to which they felt each emotion,
again on 5-point scales.
Comprehension assessment. Guided by Kintsch (1988) and Zwaan and Radvansky (1998), we
used two sets of questions to measure students’ comprehension of the texts: five true or false
questions to measure their textbase level of understanding and one open-ended question to
measure their situation model level of understanding. The five textbase model questions
consisted of three to four microstructure questions and one to two macrostructure questions. An
example of a microstructure question is “Korea stationed a coast-guard detachment on the island.”
Macrostructure questions were aimed at discourse-level ideas, as in, “Korea insists on their
sovereignty over the island for historical reasons.” The open-ended questions asked students to
use what they had learned from the text to solve a given problem, such as, “Complete the
following debate between Korea and Japan based on what you read in the text.”
Reading motivation. We also measured students’ reading motivation about first and second
language reading, adapting items from the Motivation for Reading Questionnaire (MRQ:
Wigfield & Guthrie, 1997). Two 10-item motivation questionnaires were developed to measure
the participants’ reading motivation in the first and second language, separately. For each
language, five items measured extrinsic motivation, and another five items measured intrinsic
motivation (See Appendix C for ten items for first language reading motivation).
Procedures
Data were gathered in a computer lab, with materials delivered by computer. In a first session,
students responded to the demographic questions asking them background knowledge and
interest questions and reading motivation questions. After one to three day intervals, participants
read four texts on four different topics; two topics were read in Korean and the other two topics
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were read in English; also, two topics were read in the emotional version (one in English and one
in Korean) and the other two topics were read in their neutral version (one in English and one in
Korean). Table 1 below summarizes how each topic was presented to different groups of
students. Every student experienced all four text conditions in counterbalanced order, and across
students, all four topics appeared approximately the same number of times in all four orders.
Table 1. Distribution of text versions (language X emotion) across groups of participants.
Topics
Group A (n=134)
Group B (n=108)
Group C (n=121)
Group D (n=114)
Soccer players
English /Emotional
Korean/Neutral
English/Neutral
Korean/Emotional
Social networking
Korean/Neutral
English/ Neutral
Korean/Emotional
English/Emotional
Island dispute
English/Neutral
Korean/Emotional
English/Emotional
Korean/Neutral
Plagiarism
Korean/Emotional
English/Emotional
Korean/Neutral
English/Neutral
Participants within the same English achievement level, as determined by their semester
grades, were randomly assigned to one of four groups. However, Group A attracted more
participants accidentally because it was located at the top of the menu page, and some
participants who had been assigned to a different group carelessly clicked on it. Still, each group
had equivalent Korean and English achievement levels as shown by one-way ANOVAs (F
(3,456 ) = .603, p = .614 for Korean, F (3,456) =.329, p = .805 for English). Table 2 below
shows the means of each group in Korean and English proficiency.
Table 2. Language proficiency scores across groups.
Language
proficiency
Group A (n=128)
Mean (SD)
Group B (n=105)
Mean (SD)
Group C (n=118)
Mean (SD)
Group D (n=112)
Mean (SD)
Korean
achievement
5.08 (1.97)
4.84 (1.80)
5.16 (1.90)
4.98 (1.94)
English
achievement
5.10 (1.87)
4.92 (1.91)
5.10 (1.82)
4.92 (1.87)
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Students were told they could read the text at their own pace and that there would be some
comprehension questions to check their understanding. After reading the first text, participants
were asked to rate the degree to which they felt each of 12 emotions (see Pekrun et al., 2002).
After indicating their emotional response, they answered the five textbase model question and
the one open-ended question assessing their situation model understanding. They then went on to
read and respond to questions about each of the other three texts in turn.
All measures, including the background questionnaire and the comprehension tests, were
presented in Korean, the participants’ first language. Although students were told they could
respond in English to the post reading questions, nearly all typed their responses in Korean.
Data Analysis
Students’ responses to the textbase model questions were totaled, resulting in scores ranging
from 0 to 5. Compared to the simplicity of scoring the textbase model questions, scoring
situation model understanding as measured by the open-ended questions involved a long process
of trial and error. We first created a scoring rubric based on the propositions in the text, guided
by Kintsch (1988). Before attempting to score all protocols based on our rubric, we tested its
viability by scoring a random sample of 30 participants’ responses. We found that sometimes we
had to condense propositions and other times we had to separate them. For example, we had
initially counted as one idea unit the sentence “Dokdo is an integral part of Korean territory
historically, geographically, and under international law…” However, we found that many
students recognized several idea units in the sentence and used them separately and
independently, as in, “Dokdo is Korean land for geographical reason. We can see the island in
our naked eyes.” Thus, we decided to separate the sentence into three propositions.
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Another revision was made when we encountered many inferences and elaborations students
made about the propositions in the text. For instance, at initial scoring, we scored students’
responses by counting only the propositions that were directly addressed in the text (e.g.,
“Korean coast guards are stationed in Dokdo island”). However, after we came across other
appropriate propositions such as ideas inferred from the text (e.g., “the Korean government has
been taking good care of the island”) and ideas to elaborate text propositions (e.g., “ Don’t you
know Korean police guards have been watching over the island for 24 hours”), we decided to
count them as appropriate propositions to represent their situation model.
Lastly, we needed to decide how to score what students wrote in their response to the
situation model question that could only be based on their background knowledge and not on any
proposition stated in the text. For instance, although the text did not mention anything about
evidence from 17th century Japanese and Korean maps or about Isabu, an admiral in the Shila
dynasty from the 7th century, some students used these ideas in their responses. We decided to
distinguish such statements from the rest of the recall and called them background knowledge
intrusions.
Through several iterations of scoring a sub-sample of protocols and then discussing and
revising our rubric, we produced a final rubric for scoring. Two raters who were blind to text
conditions coded open-ended responses into two categories of propositions: situation model
propositions (idea units directly stated in the text or closely inferable from the text) and
background knowledge intrusions (idea units reflecting background knowledge that extended far
beyond the text). Using a Pearson Product Moment correlation coefficient to measure interrater
reliability, we found reliabilities of .89 on average across the four topics (r=.93 for soccer text,
r=.84 for social networking text, r=.91 for island dispute text, r=.89 for plagiarism text).
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The students’ emotion ratings were grouped and averaged into the four categories of
emotions suggested by Pekrun et al. (2002): positive activating (excitement, hope, pride),
negative activating (anger, worry, shame), positive deactivating (gratitude, relief, satisfaction),
and negative deactivating (boredom, sadness, disappointment) emotions.
Results
We present our results in two main sections. First, we report on within-subject comparisons
to determine whether students experienced different emotions and understood the texts
differently depending on whether they were reading in Korean (their native language) or in
English (their foreign language) and depending on whether the text was written in its emotional
version or not. In the first set of analyses, text topic was ignored. In the second set of analyses,
we now disaggregated the data topic by topic and ran multiple regression analyses to determine
the degree to which several factors (interest in/attitude toward, prior knowledge, emotional
response, condition and language of the text) predicted comprehension.
Within-Subject Comparisons Using MANOVA
Emotional Responses to Texts. We began by establishing, as a manipulation check, that
students had experienced more emotions when reading the emotional text versions (see Table 3
for means and standard deviations for all measures). A repeated-measures 2-way MANOVA
with four dependent variables (the four categories of emotions) showed that there was a
significant main effect of text version (emotional vs neutral), and language (Korean vs. English),
but no significant interaction between text version and language. Univariate F tests for each
emotion category showed that the effect of text version was strongest for the negative activating
emotions (p=.000, η2=1.24), moderate for both positive activating and negative deactivating
emotions (p=.001, η2=.033, and p=.000, η2=.021), respectively, and was not significant with
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positive deactivating emotions as the dependent variable, (p=.093). The effect of language was
only significant for negative activating emotions (p=.002, η2=.021).
Table 3. Means (SDs) for different types of emotions following reading emotional and neutral
texts in Korean and English
Text
Language
Text
Version
Negative
Activating
Negative
Deactivating
Positive
Activating
Positive
Deactivating
Emotional
8.47 (3.0)
7.63 (2.4)
7.54 (2.7)
6.74 (2.7)
Neutral
7.40 (2.6)
7.30 (2.3)
7.15 (2.7)
6.55 (2.6)
Emotional
7.96 (2.9)
7.63 (2.5)
7.40 (2.8)
6.82 (2.7)
Neutral
7.31 (2.6)
7.46 (2.5)
7.09 (2.7)
6.76 (2.6)
Korean
English
Note. that these means are totals across three emotions on 5-point rating scales, and thus the minimum and
maximum values are 3 to 15. There was a significant text version effect, F(4,473)=24.489, p=.000 η2=1.72, and
language effect, F(4,473)=9.113, p=.000, η2=.072, and no interaction, F(4,473)=0.43, p=.837.
These results suggest that students were more emotionally aroused when they read the
emotional versions of each text, as a main effect of text version averaging across the language
versions. There was also a language main effect but it occurred only for the measure of negative
activating emotions (anger, shame, worry), which showed that the intensity of negative
activating emotions was higher for the Korean than English versions.
Comprehension Measures. A repeated-measures MANOVA with three dependent
comprehension measures (textbase model scores, situation model scores, and background
intrusion scores; see Table 4 for means and standard deviations) showed that there was a
significant main effect of text version and of language but no significant interaction between text
version and language. As a follow-up, we conducted ANOVAs with each outcome measure.
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Table 4. Means (SDs) for comprehension measures for emotional and neutral texts in Korean and
English
Text
Language
Text
Version
Textbase model
scores1
Situation model
scores2
Background
knowledge intrusions
Emotional
3.96 (1.0)
2.43 (2.6)
.82 (1.6)
Neutral
3.75 (1.1)
2.11 (2.3)
.70 (1.4)
Emotional
3.43 (1.1)
1.33 (1.8)
.84 (1.3)
Neutral
3.11 (1.2)
1.25 (1.7)
.57 (1.1)
Korean
English
Based on students’ answers to 5 true-false statements reflecting the text
Based on coding of students’ responses to one open-ended essay question
Note. There was a significant text version effect, F(3,474)=17.326, p=.000, η2=.099, and language effect,
F(3,474)=94.482, p=.000, η2=.374, and no interaction, F(3,474)=1.614, p=.185.
1
2
For textbase model scores, the univariate F-test resulted in significant main effects for text
version (p=.000, η2=.058) and language (p=.000, η2=.233). Students performed better when they
read emotional than neutral texts, and this was true in both languages. As for language, students
showed higher scores with Korean than English texts, and this was true whether they read
emotional or neutral versions. These results suggest that students were better at textbase model
construction when reading emotional than neutral texts and when reading Korean rather than
English texts.
Next, a univariate F-test with situation model scores resulted in significant main effects for
text version (p=.014, η2=.013) and language (p=.000, η2=.253). Students recalled more situation
model propositions when they read emotional than neutral texts, a comparison that was
significant with Korean texts (p=.019) but not English texts. Students also showed higher scores
when they read Korean than English texts, and this was true for both emotional and neutral
versions. There was a suggestion that the effect of emotional texts on situation model scores
decreased when they read texts in English.
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Lastly, a univariate F-test of background intrusion scores resulted only in a main effect of
text version (p=.001, η2=.024). Students made more insertions of background knowledge
propositions in their recalls when they read emotional texts than neutral texts, with a suggestion
that this occurred more when reading the English than Korean texts, though not significant.
Regression Results
Our next step was to use hierarchical multiple regression to see how well our key variables of
emotion valence and language of a text predicted the comprehension dependent measures after
taking into account other variables that might explain variance in the outcome variables.
Whereas the MANOVA analysis had summed across topic, the regression analyses were
calculated separately for each topic, and thus a comparison across regressions allowed us to note
any differences in emotion valence and language effects by text topic. Also, the regression
analyses allowed us to account for such variables as students’ individual level of background
knowledge, gender, order assignment, reading motivation, and attitude/interest in the topic,
before adding our experimental variables. In addition, we wanted to see how level of emotions
(the positive and negative, activating and deactivating emotions) predicted the comprehension
measures.
Preliminary analyses showed that the assumptions of normality, linearity, and homogeneity
of variance were not violated. The dependent variables represented two comprehension levels:
textbase model and situational model scores. Thus, for each topic, we ran two regressions, one
for each of the dependent measures.
We ran the regression in four steps. In step 1, we tested the effect of gender. In step 2, we
entered order 2, order 3, and order 4, as separate “dummy” coded variables with order 1 as a
reference point. In step 3, we entered Korean or English school achievement levels in accordance
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with the language of the text, extrinsic and intrinsic reading motivation scores, and prior
knowledge and topic attitude/interest relevant to the topic of the text. In step 4, we entered text
language, text version (emotional vs. neutral), and emotion category scores (positive activating,
positive deactivating, negative activating, and negative deactivating emotion scores). When we
tested for multicollinearity issues, there were no such problems for the island and plagiarism
texts but there were for the soccer and social networking texts, and for the latter two, we reduced
the four emotion categories to two, positive emotion and negative emotion scores, ignoring the
distinction between activating and deactivating emotions.
Island topic
We begin our report with the island text whose results were consistent with those of the
repeated measure MANOVA and conformed relatively well to our expectations that level of
emotional response would significantly predict the comprehension scores.
Predicting textbase model scores. As shown in Table 5, the results of steps 1 and 2 indicated
that there were no gender or order effects. The results of step 3 indicated that language
achievement level, extrinsic reading motivation, and topic interest were significant contributors
to textbase model scores for the island text. Higher achievement level scores in Korean and
English school subjects and higher interest in the island issue were associated with higher
textbase model scores. In contrast, students who reported having higher extrinsic reading
motivation showed lower textbase model scores.
The results of step 4 indicated that the language of the text, the text version
(emotional/neutral), and negative activating emotion and negative deactivating emotion scores
were significant contributors to textbase model scores for the island text. Students who read the
island text in Korean and students who read the emotional version attained higher textbase model
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scores. Also, students who reported experiencing more negative activating emotions performed
better on the textbase model assessment than those with lower negative activating emotions. In
contrast, students who reported experiencing more negative deactivating emotions performed
worse on the textbase model assessment than their counterparts.
Table 5. Island text
Textbase model
Predictors

t
.005
Step 1
Gender
R
2
.068
Situation model
R
2
.005
F
.201
.014
.009
t
2.041
1.429
Step 2

1.394
.064
1.353
.197
4.258
Order 2
-.024
-.411
-.119
-2.109*
Order 3
-.105
-1.787
-.130
-2.257*
Order 4
-.084
-1.429
-.183
-3.210*
.130
.116
11.581*
Gender
.021
.458
.141
3.166*
Order 2
-.016
-.286
-.111
-2.125*
Order 3
-.113
-2.024*
-.136
-2.570*
Order 4
-.070
-1.263
-.166
-3.154*
Achievement
.266
5.425*
.291
6.242*
I_motivation
.055
1.121
.077
1.636
E_motivation
-.209
-4.497*
-.153
-3.472*
.113
2.094*
.150
2.921*
-.050
-.950
.056
1.119
Attitude
Prior knowledge
.271
Step 4
.141
13.785*
Gender
.030
.700
.146
Order 2
.002
.043
-.094
-1.826
Order 3
-.101
-1.900
-.127
-2.410*
Order 4
-.059
-1.106
-.157
-2.982*
F
.040
.040
18.656*
.064
.023
3.640*
.219
.155
17.196*
.281
.063
6.203*
3.372*
Achievement
.210
4.351*
.264
5.499*
I_motivation
.008
.164
.038
.801
E_motivation
-.055
-.975
-.049
-.878
.061
1.182
.116
2.271*
Prior knowledge
-.043
-.887
.056
1.163
Language (K/E)
.230
4.373*
.165
3.154*
T_Version (E/N)
.202
4.265*
.137
2.918*
PA_emotion
.068
.890
.097
1.278
Attitude
 R2
4.319*
Gender
Step 3
R2
18
PD_emotion
-.050
-.690
-.044
-.613
NA_emotion
.210
3.206*
.094
1.441
ND_emotion
-.190
-3.055*
-.107
-1.741
*P
Predicting situation model scores. As shown in Table 5, the results of steps 1 and 2
indicated that there were significant gender and order effects. Girls showed higher situation
model scores on the island text than boys. Students who read the island text as the first text
performed better on the situation model measure than those who read the island text as the 2nd,
3rd, or 4th text.
The results of step 3 indicated that language achievement, extrinsic reading motivation, and
topic interest were significant contributors to situation model scores. Students who had higher
achievement levels in their Korean and English school subjects and students who reported
having higher topic interest attained higher scores on the situation model assessment. In contrast,
students who reported having higher extrinsic reading motivation earned lower scores in the
situation model assessment.
The results of step 4 indicated that the language of the text and the text version were
significant contributors to situation model scores, in addition to language achievement and topic
interest. Students who read the island text in Korean and students who read the emotional version
of the island text attained higher situation model scores. However, students’ reported emotions as
measured by the four emotion scores were not significant contributors to predicting situation
modelo scores.
Soccer topic
We next present results for the soccer text because here, some of the background variables
were significant contributors as was the key variable of emotional response.
19
Predicting textbase model scores. The results of steps 1 and 2 indicated that there was no
gender effect but an order effect existed. Students who read the soccer text as their last text of
four were significantly lower in textbase model scores, compared with those who read the soccer
text as their first text. The results of Step 3 indicated that there was no effect of achievement
level but extrinsic and intrinsic reading motivation and attitude scores were significant
contributors to textbase model scores. Students who reported having higher intrinsic reading
motivation attained higher textbase model scores, whereas student with higher extrinsic reading
motivation showed lower textbase model scores. In addition, students who reported being a fan
of Park, the Korean soccer player mentioned in the emotional version of the text, attained higher
textbase model scores.
The results of step 4 indicated that text language, text version, and negative emotions were
significant contributors to textbase model scores for the soccer text, in addition to attitude and
order. Students who read the soccer text in Korean and students who read the emotional version
of the soccer text had higher textbase model scores. In contrast, students who reported
experiencing negative emotions showed lower textbase model scores.
Table 6. Soccer text
Textbase model
Predictors

t
Step 1
Gender
.040
R2
 R2
.002
.002
Situation model
F
.168
.058
.056
t
0.714
.845
Step 2

8.763*
.033
.715
.167
3.543*
Order 2
-.031
-.557
-.016
-.281
Order 3
.012
.216
-.004
-.079
Order 4
-.241
-4.352*
-.057
-1.018
.156
.098
 R2
F
.028
.028
12.823*
.031
.003
0.416
.148
.117
11.922*
3.581*
Gender
Step 3
R2
10.036*
Gender
.086
1.773
.190
3.884*
Order 2
-.019
-.360
.001
.023
Order 3
.020
.375
.004
.071
20
Order 4
-.235
-4.445*
-.045
-.839
Achievement
.081
1.680
.183
3.772*
I_motivation
.098
2.070*
.133
2.807*
E_motivation
-.262
-5.697*
-.275
-5.966*
Attitude
.122
2.320*
.039
.735
Prior knowledge
.086
1.539
.080
1.439
.238
Step 4
.082
11.594*
.215
Gender
.063
1.346
.170
3.592*
Order 2
-.026
-.510
-.004
-.072
Order 3
.018
.352
.005
.090
Order 4
-.226
-4.450*
-.028
-.541
Achievement
.059
1.246
.159
3.280*
I_motivation
.050
1.079
.081
1.747
E_motivation
-.085
-1.499
-.084
-1.459
Attitude
.114
2.241*
.018
.340
Prior knowledge
.100
1.871
.074
1.370
Language (K/E)
.249
4.729*
.298
5.582*
T_Version (E/N)
.181
4.246*
-.023
-.539
P_emotion
.074
1.576
.087
1.822
N_emotion
-.125
-2.655*
-.076
-1.586
.067
9.209*
*P
Predicting situational model scores. The results of steps 1 and 2 indicated that there was a
gender effect but no order effect. Girls earned significantly higher situation model scores than
boys for the soccer text. The results of step 3 indicated that language achievement level, and
intrinsic and extrinsic reading motivation scores were significant contributors to situation model
scores. Students with higher achievement levels attained higher situation model scores. Student
with higher intrinsic reading motivation earned higher situation model scores, whereas students
with higher extrinsic reading motivation showed lower situation model scores.
The results of step 4 indicated that, other than the previously tested variables of gender and
language achievement, only language of the text was a significant contributor. Students who read
the soccer text in Korean earned higher situation model scores. Text version and level of
emotional response were not significant contributors to situation model scores.
21
Social Networking topic
We move on to the social networking topic whose results are similar and also displayed
similar multicollinearity in the emotion scores as the Soccer text.
Predicting textbase model scores. The results of steps 1 and 2 indicated that there was a
gender effect (with girls earning higher scores) but no order effect. The results of step 3 showed
that achievement level and attitudes were significant contributors. Students with higher
achievement levels and those with more positive attitudes toward social networking attained
higher scores.
The results of step 4 indicated that, in addition to nearly all of the background variables from
Steps 1, 2, and 3, the language of the text significantly contributed to textbase model scores for
the social networking text. Students who read the social networking text in Korean showed
higher textbase model scores. However, the other independent variables of focal interest failed to
predict textbase model scores.
Predicting situational model scores. The result of steps 1 and 2 indicated that there was a
gender effect but no order effect. The results of step 3 indicated that language achievement level,
intrinsic and extrinsic reading motivation, and topic attitude were significant contributors.
Students with high achievement levels and students who reported having higher intrinsic reading
motivation attained higher situation model scores. In contrast, students who reported having
higher extrinsic reading motivation and students with more positive attitudes toward social
networking showed lower scores on the situation model assessment. The results of step 4
indicated that only the language of the text was a significant additional contributor. Students who
read about social networking in Korean were better in answering the situation model prompt.
22
Table 7. Social networking text
Textbase model
Predictors

t
.057
Step 1
Gender
R
2
.238
R
Situation model
2
F
.057
26.644*
5.162*
.350
.067
Step 2

.010
t
1.536
.245
5.311*
.353
7.900
Order 2
.002
.029
-.014
-.250
Order 3
-.064
-1.108
-.058
-1.028
Order 4
-.099
-1.716
-.037
-.663
.178
.111
11.777*
Gender
.180
3.814*
.311
6.795*
Order 2
-.021
-.383
-.036
-.677
Order 3
-.069
-1.259
-.064
-1.217
Order 4
-.115
-2.108*
-.050
-.950
Achievement
.263
5.360*
.185
3.906*
I_motivation
.079
1.657
.132
2.848*
E_motivation
-.067
-1.455
-.147
-3.293*
Attitude
-.107
-2.340*
-.123
-2.788*
.038
.793
.069
1.501
Prior knowledge
.240
Step 4
.062
8.798*
Gender
.144
3.110*
.289
6.289*
Order 2
-.011
-.200
-.030
-.563
Order 3
-.051
-.963
-.053
-.999
Order 4
-.100
-1.882
-.039
-.736
Achievement
.252
5.217*
.175
3.650*
I_motivation
.049
1.040
.116
2.504*
E_motivation
.121
2.197*
-.050
-.914
-.100
-2.262*
-.119
-2.708*
Prior knowledge
.026
.554
.062
1.326
Language (K/E)
.296
5.701*
.153
2.956*
T_Version (E/N)
.051
1.206
.012
.276
P_emotion
-.058
-1.120
-.064
-1.248
N_emotion
-.054
-1.036
-.006
-.115
Attitude
 R2
F
.123
.123
61.879*
.125
.003
0.420
.232
.107
12.115*
.250
.018
2.559*
7.866*
Gender
Step 3
R2
*P
23
Plagiarism topic
We move on to the plagiarism topic whose results showed that it was the least interesting
topic to the participants based on their degree of emotional involvement. It elicited relatively
similar emotional response involvement as the island text but very low in its effect of the text
version.
Predicting textbase model scores. The results of Steps 1 and 2 indicated that there were
gender (favoring girls) and order effects. Students who read the plagiarism text in the third order
showed lower scores. The results of step 3 indicated that achievement level and prior knowledge
were significant positive contributors to textbase model scores.
The results of step 4 indicated that, in addition to gender, order, and achievement scores
from the previous steps, the language of the text and negative activating emotions and positive
activating emotions were significant contributors. Students who read the plagiarism text in
Korean and students who reported experiencing higher negative activating emotions attained
higher textbase model scores. In contrast, students who reported experiencing higher positive
activating emotions showed lower textbase model scores.
Predicting situational model scores. The results of steps 1 and 2 indicated that there was a
gender effect favoring girls but no order effect. The results of step 3 indicated that achievement
level, intrinsic and extrinsic reading motivation, and prior knowledge were significant
contributors. The results of step 4 indicated that, other than previously significant contributors
from steps 1 to 3 (gender, achievement level, intrinsic motivation), only the language of the text
was a significant contributor to situation model scores.
24
Table 8. Plagiarism Text
Textbase model
Predictors

t
.035
Step 1
Gender
R
2
.187
Situation model
2
R
F
.035
15.986*
3.998*
.340
.057
Step 2

.022
t
3.423*
.177
3.806*
.334
7.451*
Order 2
-.007
-.131
.014
.264
Order 3
-.151
-2.700*
-.032
-.601
Order 4
-.104
-1.841
-.084
-1.543
.097
.040
3.840*
Gender
.138
2.879*
.280
6.217*
Order 2
-.009
-.171
.011
.209
Order 3
-.155
-2.816*
-.029
-.565
Order 4
-.108
-1.926
-.086
-1.642
Achievement
.146
2.924*
.182
3.887*
I_motivation
.025
.503
.123
2.598*
E_motivation
-.061
-1.266
-.100
-2.229*
Attitude
-.047
-1.004
.026
.595
.102
2.283*
Prior knowledge
.101
2.135*
.132
Step 4
.035
2.895*
Gender
.111
2.323*
.261
5.761*
Order 2
.011
.203
.022
.421
Order 3
-.158
-2.838*
-.029
-.556
Order 4
-.096
-1.688
-.081
-1.501
Achievement
.122
2.359*
.163
3.337*
I_motivation
-.010
-.192
.101
2.102*
E_motivation
.038
.647
-.002
-.043
-.030
-.649
.022
.490
Prior knowledge
.085
1.785
.086
1.919
Language (K/E)
.141
2.529*
.149
2.817*
T_Version (E/N)
-.017
-.359
-.001
-.022
PA_emotion
-.131
-2.193*
.019
.330
PD_emotion
-.035
-.601
-.068
-1.239
NA_emotion
.142
2.109*
.075
1.183
ND_emotion
-.014
-.204
-.053
-.817
Attitude
 R2
F
.115
.115
57.846*
.123
.008
1.275
.203
.080
8.699*
.222
.019
1.772
7.606*
Gender
Step 3
R2
*P
25
Discussion and Study Significance
Like the students in the Gaskins (1996) study we were replicating, the Korean adolescent
readers in our study responded with stronger emotions to text versions that were written to
arouse culturally relevant emotional scripts when compared to more neutral texts that placed
some distance between the young reader and the topic. Thus, when they were reading a text that
compared two players on a British soccer team, one a famous Korean soccer player and the other
a Japanese player, and that implied that the Japanese player was a more valued member of the
team, students’ emotions were more intense than when they read an account that compared two
soccer players from countries for which these young readers felt little connection. Although
students’ emotional response and comprehension performance were stronger when reading a text
in their first rather than their second language, the contrast between emotional and neutral
versions of the text remained constant. These adolescent readers had better comprehension when
reading texts that appealed to their emotions, suggesting that emotional involvement helps
readers take agency in their reading. In response to a call by Benesch (2012) for second language
teachers to help young people see their endeavors as more than a cold, intellectual enterprise, our
study suggests the benefits of including texts young people care about, even if the emotional
involvement they feel, or frustration at difficulties arising from lack of proficiency needs some
careful guidance by a teacher.
We also found that the topic of the text made a difference in whether these young people
could become emotionally involved in what they were reading and could comprehend the text
better. Our results from the topic-specific regression analyses showed that level of emotional
response positively predicted textbase model scores for the island, soccer, plagiarism texts but it
did not predict textbase model scores for the social networking text. Moreover, emotional
26
involvement predicted the situation model scores only of the island text and not any other topic.
These results stand in contrast to previous research with expository texts (Fortner et al., 1991;
Gaskins, 1996; Reutzel et al., 1991). The previous research suggested that emotional
involvement affected delayed recall of the text (Reutzel et al., 1991), the interpretation of the text
(Gaskins, 1996), and answers to the open-ended questions (Fortner et al., 1991). However, this
contradictory result may lie on the fact that we excluded background knowledge intrusions from
our situation model assessments. Thus, the negative effect of emotional involvement in previous
research may be based in background knowledge intrusions.
From a socio-constructivist view on reading, our participants responded to the texts from
within their socio-cultural perspectives, whether they were reading their native language or in a
newly learned foreign language. As Gee (2001) suggested, our participants make situated
meanings according to the text versions. This situated meaning making drew on prior knowledge
and the emotional responses of readers. The main effect of emotional texts on emotional
response and textbase model and situation model scores empirically demonstrated that readers’
affective response was culturally constructed and evoked. As Gee (2002) noted, reading is not a
neutral but always perspectival process, and this perspectival process consists of not only
cognitive resources such as prior knowledge but also affective resources such as beliefs and
values.
Readers employ all resources in their reading, opportunistically making use of cognitive and
affective resources without prejudice, relying on both languages as resources, and on motivation
and prior knowledge as needed. As a resource, emotions fall in a special category as they are
sometimes associated with failure experiences that trigger the need to regulate affective
responses. When a reader experiences struggles in constructing a textbase model of the text, he
27
or she may feel frustration, anxiety, and anger. Sometimes, a reader can use the energy of those
emotional consequences to improve effort and lead to better comprehension. Other times, all a
reader can do is to regulate and downplay emotions that are not relevant to the text world. For
instance, when reading the island text, a reader may feel anger triggered by the issue of the text
and also feel anxiety and frustration by the difficulty of a text written in a second language. If the
person fails to regulate the emotions attributed to the difficulty of the text or to use productively
the energy of anger, say, caused by the issue, comprehension may suffer. However, if readers
succeed in properly regulating and garnering their emotions, they are more likely to be
successful at compensating for their lack of proficiency by drawing on all cognitive resources.
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30
Appendix A – Sample text versions: Emotional and neutral versions of Island text in English.
Island text (Emotional version / English)
Called Laila by Morroco and Tura by Spain, the island is claimed by both countries as their territory.
Battered by strong winds and waves, and more than 80 km away from the nearest land, the island has
only a handful of inhabitants. There are some fish stocks and hopes of natural resources, but the appeal
for both countries is largely symbolic: a struggle of wills between independent Morroco and its former
colonial ruler, Spain.
Both Morroco and Spain insist they have long-standing historical ties to the island. Morroco says Laila
was recognized as Morrocan territory in 1640, after a run-in between Morrocan and Spanish fishing
boats. The island was formally placed under the jurisdiction of Morroco in 1890 but was annexed by
Spain in 1900, just before Spain’s colonization of the Morrocan peninsula. Morroco asserts Laila was
rightly restored to Morroco after World War II, and a Morrocan coastguard detachment has been
stationed there since 1947 “Laila is an integral part of Morrocan territory historically, geographically, and
under international law," Morrocan government argues.
However, Spain claims that it established sovereignty over the island by the mid 17 th centry when
Spanish sailors used the zone as a port and a fishing ground. Spain incorporated the island in 1900. Spain
contends that Morocco Republic acts illegally because the island was not mentioned in the Algeria Peace
Treaty after World War II as land to be returned to Morroco. “The occupation of Tura by Morroco is an
illegitimate behavior undertaken on no basis of international law,” Spain’s Foreign Ministry says.
Island text (Neutral version / English )
Called Dokdo by Korea and Takeshima by Japan, the island is claimed by both countries as their territory.
Battered by strong winds and waves, and more than 80 km away from the nearest island, the island has
only a handful of inhabitants. There are some fish stocks and hopes of natural resources, but the appeal
for both countries is largely symbolic: a struggle of wills between independent South Korea and its
former colonial ruler, Japan.
Both Japan and Korea insist they have long-standing historical ties to the island. Korea says Dokdo was
recognized as Korean territory in 1696, after a run-in between Korean and Japanese fishermen. The island
was formally placed under the jurisdiction of Korea in 1900 but was annexed by Japan in 1905, just
before Japan’s colonization of the Korean peninsula. Korea asserts Dokdo was rightly restored to Korea
after World War II, and a Korean coastguard detachment has been stationed there since 1954. “Dokdo is
an integral part of Korean territory historically, geographically, and under international law," Korean
government argues.
However, Japan claims that it established sovereignty over the island by the mid 17 th centry when
Japanese sailors used the zone as a port and a fishing ground. Japan incorporated the island in 1905.
Japan contends that South Korea acts illegally because the island was not mentioned in the Sanfrancisco
Peace Treaty after World War II as land to be returned to Japan. “The occupation of Takeshima by Korea
is an illegitimate behavior undertaken on no basis of international law,” Japan’s Foreign Ministry says.
Note. Backtranslated versions of the above paragraphs were made in Korean for Korean texts.
31
Appendix B – Attitude and knowledge scales
1.
나는 축구 선수 박지성의 팬이다.
(I am a fan of Korean soccer player, Jisung Park.)
2.
나는 피겨스케이팅 선수 김연아의 팬이다.
(I am a fan of Korean figure skater, Yuna Kim.)
3.
나는 전문용어를 사용해서 축구경기를 설명할 수 있다.
(I can explain a soccer game with technical terms. )
4.
나는 전문용어를 사용해서 피겨스케이팅 경기를 설명할 수 있다.
(I can explain a figure skating competition with technical terms. )
5.
나는 독도 분쟁에 관심을 갖고 있다.
(I am interested in the Dokdo island dispute between Korea and Japan.)
6.
나는 백두산 분쟁에 관심을 갖고 있다.
(I am interested in the Beakdu mountain dispute between Korea and China.)
7.
나는 근거를 들어 독도 분쟁을 설명할 수 있다.
(I can explain the Dokdo island dispute with background information.)
8.
나는 근거를 들어 백두산 분쟁을 설명할 수 있다.
(I can explain the Beakdu mountain dispute with background information.)
9.
나는 소셜네트워크를 하루에 한시간이상 한다.
(I use social networking sites for more than 1 hour per day.)
10. 나는 친구들과 직접 만나 이야기하는 것을 좋아한다.
(I like talking with my friends in face to face meetings.)
11. 나는 소셜네트워크의 잠재적 위험성을 설명할 수 있다.
(I can explain the potential harms of using social networking sites.)
12. 나는 핸폰의 잠재적 위험성을 설명할 수 있다.
(I can explain the potential harms of using a cell phone.)
13. 나는 우리나라의 교육이 자랑스럽다.
(I am proud of education in Korea.)
14. 나는 우리나라의 정치가 자랑스럽다.
(I am proud of politics in Korea.)
15. 나는 불법다운로드나 출처를 밝히지 않은 정보사용이 옳지 않다는 것을 설명할 수 있다.
(I can explain why illegal downloading and using information without citation are wrong.)
16. 나는 부정선거나 뇌물을 사용한 로비활동이 옳지 않다는 것을 설명할 수 있다.
(I can explain why corrupt election and lobbing with bribes are wrong.)
Note. Items were rated on 5 point Likert scale using 1 = strongly disagree, 2 = disagree, 3 = neither, 4 = agree, 5 =
strongly agree (or 1 = Not at all, 2 = Rarely, 3 = Sometimes, 4 = Frequently, 5 = Always)
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Appendix C – First language and second language reading motivation scales
First language reading motivation
1.
나는 독서를 좋아한다.
(I like reading.)
2.
나는 TV 보다 책읽는 것이 더 좋다.
(I like reading rather than watching TV.)
3.
나는 나의 관심사나 취미에 대한 책을 읽는다
(I read books about my interests and hobbies.)
4.
나는 내용이 재밌으면 어려운 글도 읽는다.
(I read difficult books when they are interesting.)
5.
나는 읽을 거리가 있으면 기다리는 시간도 지루하지 않다.
(I do not care waiting for something or somebody when I have something to read. )
6.
나는 대학입시에 도움이 되기 때문에 독서를 한다.
(I read due to college entrance.)
7.
나는 학교성적에 도움이 되기 때문에 독서를 한다.
(I read due to school grades.)
8.
나는 친구들과 어울리기 위해 독서를 한다.
(I read in order to get along with my friends.)
9.
나는 선생님과 부모님께 인정받기 위해 독서를 한다.
(I read in order to get approval from my teacher and parents.)
10. 나는 친구들보다 더 많은 책을 읽었을 때 기분이 좋다.
(I feel proud when I read more books than my friends.)
Note. Items were rated on 5 point Likert scale using 1 = Not at all, 2 = Rarely, 3 = Sometimes, 4 = Frequently, 5 =
Always.
33
Second language reading motivation
1.
나는 영어를 읽는 것이 즐겁다.
(I like reading in English.)
2.
나는 영어를 읽을 때 깊게 몰두한다.
(I tend to get engaged with English texts.)
3.
나는 어려운 영어 글을 읽는 것이 중요하다고 생각한다.
(It is important to me to read challenging English texts.)
4.
나는 영어로 된 글도 내용이 재밌으면 빠져든다.
(I get indulged in English texts when they are interesting.)
5.
나는 영어를 읽고 영어권 사람들의 문화를 알아가는 것이 재밌다.
(It is fun to me to read and understand the culture of English speaking people.)
6.
나는 대학입시를 위해 영어를 읽는다.
(I read English for college entrance.)
7.
나는 학교성적 때문에 영어를 읽는다.
(I read English for school grades.)
8.
나는 미래에 좋은 직업을 얻기 위해 영어를 읽는다.
(I read English in order to get a job in the future.)
9.
나는 영어를 친구들보다 더 빨리 읽었을 때 기분이 좋다.
(I feel proud when I read English faster than my friends.)
10. 나는 선생님이나 부모님께 인정받기 위해 영어를 읽는다.
(I read English in order to get approval from my teachers and parents.)
Note. Items were rated on 5 point Likert scale using 1 = Not at all, 2 = Rarely, 3 = Sometimes, 4 = Frequently, 5 =
Always.
34