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Advances in Social Sciences Research Journal – Vol. 8, No. 6
Publication Date: June 25, 2021
DOI:10.14738/assrj.86.10469. Mavrogianni, A., Vassilaki, E., Sarris, A., & Yachnakis, E. (2021). Do Computer and Foreign Language Literacy Affect Native Language
(L1) Reading Strategies? Advances in Social Sciences Research Journal, 8(6). 543-559.
Services for Science and Education – United Kingdom
Do Computer and Foreign Language Literacy Affect Native
Language (L1) Reading Strategies?
Aristea Mavrogianni
Department of Primary Education, School of Education
Rethymno, University of Crete, Greece
Εleni Vassilaki
Department of Primary Education, School of Education
Rethymno, University of Crete, Greece
Apostolos Sarris
Department of History and Archaeology
University of Cyprus, Nicosia, Cyprus
Emmanuel Yachnakis
Medical School, Heraklion, University of Crete, Greece
ABSTRACT
This research investigates metacognitive awareness, students reading strategies
preferences and their correlation to independent demographic and educational
variables. Data were gathered through the MARSI-2fGR inventory administered to
a random sample of 632 students aged 12-24 from 68 schools in various urban,
semi-urban and rural regions in Greece. The alternative factorial structure of the
MARSI-2fGR inventory comprised of two factors, namely textor and textout,
standing for text-oriented and beyond text reading strategies that Greek secondary
students use. Results showed significant differences in favour of the textor reading
strategies compared to the textout. It seems that other parameters affect the
reading strategies preferences more than the family's socioeconomic status. Both
variables of foreign language knowledge and computer literacy showed statistically
significant differences. Therefore, it appeared that the more literate someone is in
foreign languages and computers, the more reading strategies he/she declared to
use. This research sheds new light on the way that Greek students read academic or
school-related material.
Keywords: MARSI-2fGR; metacognitive Awareness; reading Strategies; textor and
textout; foreign Language Knowledge; Computer Literacy.
INTRODUCTION
Reading comprehension plays a vital role in the educational process, helping students "unlock"
texts and reach the deeper meaning of most sciences [29]. However, teaching the students how
to read to accomplish a profound understanding [15] is not an easily manageable task. It
requires support that involves specifically designed instruction of reading strategies, which is
a challenge by itself. The finding that students, who got guidance in reading strategies use,
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Advances in Social Sciences Research Journal (ASSRJ) Vol. 8, Issue 6, June-2021
Services for Science and Education – United Kingdom
comprehend texts better [36] empowers the acknowledgement of the role of reading strategies
[6, 28, 35].
Exploring the effect of reading strategy instruction on reading comprehension has been a
critical objective of international research in recent decades [7, 11, 27, 31, 33, 34]. As Pinninti
[26, p. 27]says: "The proponents of strategy instruction assume that when readers are trained
in using reading strategies, they will become autonomous learners for a lifetime."
The importance of reading comprehension for assessing students' knowledge is strengthened
by the fact that the Organization for Economic Co-operation and Development (OECD) [25] has
placed it as one of the three critical elements of its Program for International Student
Assessment (PISA), the other two being Math and Science. PISA measurements record the weak
reading comprehension performance observed in the Greek student population in recent years
after 2000. More specifically, the latest findings of PISA 2018 concerning 15-year-olds
regarding their reading literacy reveal a mean score of 457 points compared to an average of
487 in OECD countries [25]. This irrefutable fact necessitates reforms that will lead to changes
to invert the situation.
In recent years, various scales have been developed to monitor and evaluate reading strategies
used by students. Among them, Mokhtari and Reichard [22] devised the Metacognitive
Awareness of the Reading Strategies Inventory (MARSI 1.0) to monitor and evaluate reading
strategies used by students when they study academic or school-related material. The MARSI
1.0 inventory comprises three factors: glob, prob, and sup for global, problem solving and
supportive strategies.
From the adaptation of the Metacognitive Awareness of Reading Strategies Inventory (MARSI
1.0) to a Greek secondary student population and examining the adapted instrument's
psychometric properties, the MARSI-2fGR [20] inventory emerged. This new factorial structure
with only two factors was found reliable and valid. These two factors were proposed as textor
for text-oriented reading strategies and textout for beyond text reading strategies (Appendix
A). This alternative factorization is inextricably interwoven with the Greek educational system
aligned with the Greek curriculum and syllabus, affecting the way teachers and students are
involved in the educational process [2-4, 13-14]. Despite its recent turn towards multiliteracy,
the Greek national curriculum still focuses on homogeneity [13]. Based on existing research, in
secondary education, the practice of exam-controlled teaching [32] has broadly and decisively
affected many parameters of both the teaching and learning process [1, 3, 13, 16].
Greek students are encultured throughout their school years towards being successful exam
performers. They are urged to focus on the textbook rather than reach out to extra material.
This practice discourages them from reaching out to supplementary material and diminishes
them to mere listeners of the taught information [4-5], preparing for examinations based on
the textbook's content [2-3]. Conclusively, the way the Greek educational system is established
[3] urges students to opt for strategies that will ultimately lead them to the desired success.
Nevertheless, could those strategies be linked to broader educational parameters like computer
and foreign language literacy?
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Mavrogianni, A., Vassilaki, E., Sarris, A., & Yachnakis, E. (2021). Do Computer and Foreign Language Literacy Affect Native Language (L1) Reading
Strategies? Advances in Social Sciences Research Journal, 8(6). 543-559.
URL: http://dx.doi.org/10.14738/assrj.86.10469
OBJECTIVES
The current study aimed to explore the relationship between the reading habits and the
declared reading strategies of Greek secondary students, based on the adapted and
standardized 2-factor structure MARSI-2fGR [20].
MATERIAL AND METHODS OF ANALYSIS
The 632 students participating in the research derived from sixty-eight secondary public and
private schools from urban, semi-urban and rural school districts in Greece. They volunteered
themselves, and both parents and students granted their consent assured of data
confidentiality. The inventory was completed online in the school's computer laboratory by
rating the items on a 1 to 5 scale, according to each participant's preferences.
Further, the mean score of each structure was computed and categorized by percentages into
pre-determined levels of strategy use. Comparisons of total score means of the final scale and
its subscales, on the basis of selected demographic as well as educational variables, were
implemented to find the relation of these variables to the declared metacognitive awareness of
reading strategies throughout the scale and its subscales. A categorization of the mean score on
any of the subscales could be useful as it is related to the reading habits of the students.
For all data analyses IBM SPSS Statistics for Windows, Version 25, was used. Initially,
descriptive statistics were used to describe demographic and educational data. Then, before
starting exploratory factor analysis, the values of the bivariate correlation matrix of all items
were analyzed (inter-item correlations). In the case of bivariate correlation, scores either lower
than 3 or greater than .8, the items of the corresponding pair should be considered as
prospective for removal according to Field [9].
RESULTS
The final two-factor inventory comprised 26 items was named MARSI-2fGR [20] (Mavrogianni
et al., 2020). The first factor was termed "textor", and the second factor was termed "textout"
for reasons explained in detail in the Discussion section.
In Table 1 are displayed: (a) the grouping of the items in the two factors, (b) means and
standard deviations of items. Table 1 shows that the means of individual items ranged from
2.50 (SD = 1.28) to 4.02 (SD = 1.24). In descending order of their mean, the individual items are
ordered as follows: Q11 (4.02), Q16 (4.01), Q27 (3.97), Q08 (3.77), Q12 (3.67), Q03 (3.65), Q04
(3.57), Q20 (3.54), Q01 (3.53), Q06 (3.38), Q05 (3.35), Q30 (3.30), Q22 (3.29), Q19 (3.28), and
Q25 (3.20), Q02 (3.13), Q18 (3.11), Q23 (2.99), Q07 (2.93), Q24 (2.86), Q17 (2.85), Q29 (2.75),
Q28 (2.73), Q26 (2.69), Q09 (2.50), Q15 (2.50). This ordering indicates that, on average,
students respond more frequently using textor strategies while they use textout strategies less
frequently. Two exceptions to these are Q30 (textout, amid textor) and Q02 (textor, amid
textout). This ordering indicates too, by applying the benchmark mentioned above, that only
the nine strategies (items) Q11, Q16, Q27, Q08, Q12, Q03, Q04, Q020 and Q01 are of high usage
by the participants, all the rest of strategies are of medium usage, and there is no strategy of
low usage. So, only some of the textor strategies are of high usage, the rest of the textor
strategies (Q02, Q05, Q06, Q19, Q22) are of medium usage, and all textout strategies are of
medium usage.