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Abstract

We posited that students in higher education would be able to comprehend the academic texts they encountered and think critically and flexibly. This study aimed to develop a scale to measure the academic literacy self-efficacy of higher education students and to examine the relationships between students’ academic literacy self-efficacy, cognitive flexibility, and critical thinking levels. The research included two study groups, one with 327 participants and the other with 414. We used the Academic Literacy Self-Efficacy Scale (ALSES), UF/EMI Critical Thinking Disposition Instrument, and Cognitive Flexibility Inventory as data collection tools. We analyzed the data using exploratory factor analysis, confirmatory factor analysis, reliability analyses, and structural equation modeling (SEM). The analyses showed that the ALSES was a valid and reliable measurement tool. The SEM results indicated that critical thinking and cognitive flexibility were moderately related. Furthermore, we found a moderate correlation between academic literacy self-efficacy and critical thinking, as well as between academic literacy self-efficacy and cognitive flexibility. The findings show that cognitive flexibility and critical thinking disposition together accounted for 28% of the total variance in academic literacy self-efficacy.

Plain Language Summary

Development of Academic Literacy Self-Efficacy Scale (ALSES): Examining its Relationship with Cognitive Flexibility and Critical Thinking

This research aims to examine how students in higher education develop the skills of comprehending academic texts, thinking critically and thinking flexibly. In particular, it was aimed to develop a scale measuring students‘ academic literacy self-efficacy and to determine the relationships between this self-efficacy and students’ critical thinking and cognitive flexibility levels. Two different groups of 327 and 414 participants participated in the study. Data were collected with various instruments such as Academic Literacy Self-Efficacy Scale (ALSES), UF/EMI Critical Thinking Dispositions Instrument and Cognitive Flexibility Inventory. The collected data were analyzed using factor analysis, confirmatory factor analysis, reliability analyses, and structural equation modelling (SEM). The results of the study revealed that ALSES is a valid and reliable measurement tool. In addition, a moderate relationship was found between critical thinking and cognitive flexibility. Moderate relationships were also found between academic literacy self-efficacy and critical thinking and cognitive flexibility. Finally, both factors (cognitive flexibility and critical thinking) played a role in predicting ALSES, but critical thinking was found to be a stronger predictor.

Keywords

academic literacy critical thinking cognitive flexibility higher education

Introduction

Universities are the primary institutions where scientific knowledge is produced and disseminated. From Plato’s “academia” in ancient times to the “university” of the West in the modern era, these developments have fulfilled the functions of meeting people’s need for accurate knowledge and transferring existing knowledge to new generations (Atasoy, 2018). Universities play an important role in educating individuals by using a scientific approach within the education system (İlhan et al., 2016). The most important functions of modern universities are to conduct impartial and universal scientific research, to provide intellectual and contemporary education, and to integrate these activities with society (Aydın, 2014). Students studying at universities, which are the center of scientific knowledge, need to understand the academic texts they encounter and be familiar with the basic principles of academic writing. Additionally, students in higher education are expected to think critically and flexibly, to be free from dogmatic thinking, and to use scientific knowledge as a guide in their lives. In this context, we assumed that there is a relationship between university students’ academic self-efficacy, critical thinking—which is one of the basic components of scientific processes—and related cognitive flexibility skills, and that these two variables affect academic self-efficacy.

It is now expected that individuals possess skills that can be classified into three main categories: learning and innovation skills, digital literacy skills, and career and life skills, which may collectively be termed “21st-century skills” (Trilling & Fadel, 2009). These skills are competencies that must be continuously developed to ensure success in the 21st century (Dede, 2010). We examined two key concepts: critical thinking and cognitive flexibility. Critical thinking is included in the domain of learning and innovation skills, while cognitive flexibility falls under career and life skills (Trilling & Fadel, 2009). Higher education institutions have an important role in developing these skills (Washer, 2007).

Academic Literacy Self-Efficacy

Academic literacy refers to proficiency in academic learning, particularly the reading and writing skills necessary for academic success (Elder & Read, 2015). Neeley (2005) defined academic literacy as an individual’s literacy competence in academic subjects. It is the ability to use literacy skills at a functional level in reading and understanding scientific texts, as well as in presenting one’s views on scientific issues (Miller, 1983). With academic literacy, university students developed ways of producing scientific texts and analyzing and interpreting written scientific texts (Livingston, 2021). Additionally, if we consider that academic literacy is not only the ability to understand and use scientific methods in academia, but also the ability to understand and produce solutions to natural events in everyday life (Branscomb, 1981), we can say that this skill is necessary for students not only in the academic process, but also in everyday life. Cognitive processes are one of the important dimensions of academic literacy in both learning and social life (Cope, 2005; Lave & Wenger, 1991). In addition to a certain level of cognitive development, students are expected to have confidence in their own academic literacy abilities, which is considered another cognitive function (Carson, 1993; Ross et al., 2016). Bandura (1997) defined an individual’s belief in their own capacity as self-efficacy and argued that self-efficacy should be examined and assessed within a specific domain rather than at a general level (Bandura, 2009). Because individuals’ success in a field at a high level of motivation is explained by self-efficacy (Bandura, 1977, 1986; Cassidy & Eachus, 1998; Pajares, 2002), students’ success through developing beliefs about their own abilities in academic and related fields is also recognized as a sub-dimension of self-efficacy (Schunk, 1985; Zimmerman, 2009). University students’ self-efficacy in academic literacy, which is an important concept in their academic lives, has been found to contribute to their motivation and success levels (Ross et al., 2016).

Critical Thinking

Critical thinking can be defined as logical and purposeful thinking that requires problem solving, reasoning, calculating probabilities, and making judgments (Halpern, 1989). According to R. Paul (1984), critical thinking involves evaluating whether statements and information are valid, logical, or true, as well as determining whether a conclusion is rational and whether the reasoning and evidence supporting it are correct. Critical thinking is rational and reflective thinking that determines which statements and information should be accepted or rejected (Aizikovitsh-Udi, 2011).

Critical thinking, defined as logical thinking that helps individuals decide what to believe or what to do in a new situation (Ennis, 2011), is extremely important for university students because critical thinking skills enable students to understand arguments in others’ texts and create arguments in their own texts (Intersegmental Committee of the Academic Senates [ICAS], 2002). Lloyd and Bahr (2010) stated that critical thinking is one of the core competencies for university students. Wilson (2016) also pointed out that university students, in particular, need to develop their understanding of what critical thinking means. Furthermore, critical thinking skills are important not only in academic settings but also in any problem-solving platform (Braman, 1999).

Individuals who think critically filter information and situations they encounter based on their experiences or cognitive predictions, rather than accepting them as they are, and thus develop a specific attitude toward the information and situations they assimilate (Çuhadaroğlu, 2013). According to Watson and Glaser (1964), critical thinking enables individuals to recognize assumptions, values, attitudes, and beliefs. Therefore, arguably, people who can think critically are aware of these concepts.

The existing literature (Ekmekçi, 2017; du Plessis, 2012; ICAS, 2002; McWilliams & Allan, 2014) indicates that critical thinking is an essential component of academic literacy. Additionally, studies (e.g., Ayyıldız Çolak, 2022) have found a significant correlation between academic literacy levels and critical thinking abilities.

Studies examining the relationship between critical thinking and cognitive flexibility showed that these two variables had a positive relationship (Baysal Doğruluk, 2021; Güner & Gökçe, 2021; Jason, 2001). This relationship may be because both skills require individuals to view events from different perspectives, evaluate situations from multiple viewpoints, and consider several variables during decision-making processes. Researchers have argued that cognitive flexibility begins with critical thinking and that critical thinking also requires cognitive flexibility (Scheibling-Sève et al., 2022). In this study, our model did not include a causal a priori or posteriori relationship between these two variables; instead, it addressed the covariation of the variables.

Cognitive Flexibility

Cognitive flexibility refers to an individual’s ability to change cognitive structures to adapt to changing environmental stimuli or to produce adaptive solutions to changing conditions (Dennis & Vander Wal, 2010; Ionescu, 2012). Flexibility can also be described as the ability to develop alternative responses from a broad perspective (Takeuchi et al., 2010). Considering that the number of new stimuli to which individuals are exposed has significantly increased with modern life and the age of technology, flexible thinking ability can be expected to be an important determinant of an individual’s adaptation and mental health. In fact, cognitive behavioral therapy, which has significant evidence for its effectiveness on mental health, aims to protect and improve mental health by making a person’s thinking system flexible and adaptive (Butler et al., 2006; Ciarrochi et al., 2005; Dobson & Dobson, 2016; Hofmann et al., 2012). In this therapy model, researchers have argued that an individual’s rigid, change-resistant, and absolutist thinking style made adaptation difficult and served as a risk factor for mental health problems (A. T. Beck, 1979; J. S. Beck et al., 2005). For example, cognitive restructuring—among the most widely used therapy techniques in cognitive behavioral therapy—works with individuals to make their thoughts more flexible by challenging them (Dobson & Dobson, 2016). Studies examining the relationship between cognitive flexibility and mental health have revealed a negative relationship between flexibility and anxiety-based disorders, such as depression (Yazar & Şenyaşar Meterelliyoz, 2019), generalized anxiety disorder (Lee & Orsillo, 2014), and obsessive-compulsive disorder (Pajouhinia et al., 2020). However, studies have suggested that cognitive flexibility and emotion regulation skills affect each other (Deng et al., 2023) and that people with high cognitive flexibility have higher complex problem-solving skills (Krems, 1995).

The open-ended, questioning, critical, and non-dogmatic structure of scientific knowledge shows that people who work in science or use scientific knowledge to guide their lives need flexible thinking skills. Furthermore, because academic literacy represents an individual’s capacity to comprehend scientific paradigms, it is essential to develop the ability to perceive the world from diverse perspectives that extend beyond the conventional, one-dimensional viewpoint. At this stage, cognitive flexibility served as a crucial foundation for the advancement of academic literacy. Cognitive flexibility was a key component of academic literacy, facilitating essential skills such as critical thinking, problem solving, analytical reasoning, and the ability to consider multiple perspectives. At this point, it was crucial for universities, whose main tasks include the production and dissemination of scientific knowledge, to provide their students with critical and flexible thinking skills. Cognitive flexibility, critical thinking, and self-efficacy are characteristics that can be developed through different interventions. At this stage, determining the relationships between variables also provides data to determine the possible effects of intervention programs.

Importance of The Study

Self-efficacy is a psychological factor that exerts a significant influence on students’ success, learning motivation, and effort (Bandura, 1977; Da, 2024). In contrast to general self-confidence, self-efficacy is specific to a particular domain and task. At this juncture, the acceptance and measurement of self-efficacy as a general concept may result in a misinterpretation of the structure (Artino, 2012; Bandura, 1977). When examining self-efficacy scales developed for the adult population, it is evident that some scales measure self-efficacy very broadly (Schwarzer & Jerusalem, 1995), while others assess self-efficacy more related to academic achievement (Hemade et al., 2025; van Zyl et al., 2022). However, a review of the literature reveals a paucity of valid scales specifically designed to measure self-efficacy related to academic literacy. Consequently, this discrepancy may result in misinterpretation or incomplete comprehension of the concept of academic self-efficacy. Academic literacy, a pivotal component of 21st-century skills, is a paramount skill at the higher education level. The development of a unique tool to measure this structure will contribute to a more accurate determination of student needs, the improvement of educational programmes, and the literature.

It is acknowledged that self-efficacy resources and motivations vary according to cultural structure and developmental characteristics (Klassen, 2004; Usher & Pajares, 2008). Therefore, it is imperative to consider not only the universal dimensions of self-efficacy scales but also their specific aspects related to particular ages and cultures. The university years represent a critical period in an individual’s development, during which they are introduced to topics such as scientific literacy and the evaluation of academic publications. It is during this time that their perceptions of competence in these skills are shaped. Concurrently, this period constitutes a preparatory phase during which skills such as cognitive flexibility and critical thinking, which are necessary for working life, develop. Consequently, the development of a culturally sensitive and age-appropriate academic literacy self-efficacy scale for higher education students is proposed as a means to ensure that the specific needs in this area are properly understood. Given that there are almost seven million students enrolled in 199 universities in Turkey (Council of Higher Education of Turkey, 2025), the examination of the academic literacy self-efficacy of these students can provide important clues about the effectiveness and quality of higher education processes. In this context, the academic literacy self-efficacy of higher education students is also of critical importance in terms of scientific productivity and social development.

In higher education, students are expected to know how to read and understand the scientific texts they encounter. Additionally, students should act in accordance with the basic principles of academic literacy in the scientific reports they produce. Their fulfillment is directly related to students’ academic literacy. In this context, how students assess themselves is important. Being “academically literate” plays a crucial role in academic success and in having a scientific perspective. For example, some universities, such as the University of Amsterdam (2005), use an academic literacy test for student admission. Although academic literacy is a well-known concept, examining this concept in the context of self-efficacy among higher education students is a relatively new field of study. At this juncture, we can posit that the scale developed caters to a more specific domain by incorporating a psychological variable, namely student competence, into the concept of academic literacy. Research has examined academic literacy in higher education (Braine, 2002; Defazio et al., 2010; Guzmán-Simón et al., 2017; Hoff, 2020; Lea, 2004; Lillis, 2003; Livingston, 2021; Morrell & Duncan-Andrade, 2002; Taylor et al., 2020). However, studies on academic literacy in higher education in Turkey are considerably limited (Demir & Deniz, 2020; Dokumacı Sütçü, 2021). We believe that the academic literacy self-efficacy scale developed in this study will contribute to the literature. Additionally, determining the academic literacy self-efficacy of students continuing their undergraduate education in Turkey can also be a source for interventions in this regard.

One reason for addressing critical thinking and cognitive flexibility skills, in addition to academic literacy, in this research is that these skills are part of 21st-century skills. Both academic literacy and 21st-century skills are fundamentally based on scientific and critical thinking. The 21st-century skills are divided into three main categories: learning and innovation skills, digital literacy skills, and career and life skills. Critical thinking is included in learning and innovation skills, and cognitive flexibility is included in career and life skills (Trilling & Fadel, 2009). Additionally, another reason for examining critical thinking and cognitive flexibility skills with the developed academic literacy scale is that both concepts are related to cognitive characteristics. Understanding the relationship between critical thinking and cognitive flexibility can provide information for educational curricula.

Another reason for conducting this research can be explained as follows. Academic literacy represents a new and changing situation for university students. In this context, academic literacy is related to critical thinking and cognitive flexibility in adapting to changing situations. In their definition of cognitive flexibility, Martin and Rubin (1995) emphasized the tendency of cognitively flexible individuals to adapt to change. Eze et al. (2022) also stated that critical thinking skills are effective in solving problems that arise from increasingly complex situations because of changing and evolving knowledge and information.

Skills such as critical thinking, problem analysis, adaptation to changing conditions, and considering different perspectives are fundamental characteristics of science and scientists. From this perspective, we hypothesized that critical thinking and cognitive flexibility would be related to academic literacy self-efficacy. No research in the literature examined the relationship between academic literacy self-efficacy and cognitive flexibility. However, various studies found that cognitive flexibility was positively related to critical reading self-efficacy, which is a component of academic literacy (Öztürk et al., 2022), and that critical thinking was related to academic literacy (Ayyıldız Çolak, 2022; Elçi, 2022). We originally aimed to examine these variables together in one model and to investigate academic literacy in terms of self-efficacy, rather than directly. Because this study examined educational program outcomes such as academic literacy and critical thinking together with mental health variables such as flexible thinking and self-efficacy, it may be important for the integration of different fields.

A 4-year study that integrated academic literacy into the education program found that critical reading and writing positively affected the academic performance of higher education students (Macnaught et al., 2024). In a systematic review, researchers revealed a relationship between academic writing, cognitive skills, and critical thinking (Sandoval-Cárcamo et al., 2024). Furthermore, studies have demonstrated a substantial correlation between critical thinking and cognitive flexibility with academic processes, including academic performance and academic motivation (Gökçe & Güner, 2024; Toprak et al., 2024; Zheng et al., 2024). Additionally, critical thinking was found to be associated with an individual’s self-efficacy level (Orakcı & Khalili, 2025). In the present study, we considered that both developing a measurement tool for academic literacy self-efficacy and revealing the cognitive characteristics that may be related to academic literacy self-efficacy would make important contributions to the academic literacy literature.

Aim of The Research

The purpose of this study was to develop a scale to measure the academic literacy self-efficacy of higher education students and to determine the relationships between students’ academic literacy self-efficacy and their levels of cognitive flexibility and critical thinking. We tested the following research hypotheses:

The ALSES is a valid and reliable instrument for measuring academic literacy self-efficacy in higher education students.

A significant relationship exists between cognitive flexibility, critical thinking, and academic literacy self-efficacy among students engaged in higher education.

Method

Participants

We collected data from two different study groups: one for scale development and the other for determining relationships between variables. We determined the study groups using (a) convenience sampling and (b) snowball sampling, based on the researchers’ accessibility. Convenience sampling is a method that aims to save time, money, and labor, and adds speed and practicality to the study; however, it is a risky method regarding the generalizability of the results (Büyüköztürk et al., 2008; Edgar & Manz, 2017; Yıldırım & Şimşek, 2016). To address the disadvantages of convenience sampling, we shared the research links with different departments at as many universities as possible. We used snowball sampling by asking individuals who completed the scale forms to send the research link to other students they could reach.

Study group 1: This study initially collected data from 327 individuals to develop the Academic Literacy Self-Efficacy Scale. In the group selected for exploratory factor analysis (EFA), data were first collected from 338 people. However, data from 11 participants were removed because four participants selected the “no” option on the consent form, four participants chose the same option for all items, and two participants were identified as extreme outliers. Therefore, analyses were conducted on data from 327 individuals. Among the 327 members of the EFA group, 238 were women (72.8%) and 89 were men (27.2%). The average age of the group was 23.96 years (SD = 4.24). Data was collected from 22 different universities and 18 different programs across Turkey.

Study group 2: Following the scale development study, we collected data from an additional 423 individuals to conduct confirmatory factor analysis (CFA) of the scale and to determine the relationships between the variables. After data cleaning procedures in this study group, analyses were conducted on 414 participants. Examination of the demographic characteristics of this group showed that 319 were women (77.1%), 95 were men (22.9%), and the mean age was 23.44 years (SD = 5.49). Analysis of the distribution of data from the second study group by university indicated that data were collected from 16 different programs in 29 universities across Turkey.

Data Collection Tool

Personal information form: This form was developed by researchers to determine the demographic characteristics of the individuals participating in the study. The form asked about age, gender, class, and university program attended.

Cognitive Flexibility Inventory

The Cognitive Flexibility Inventory (CFI) was developed by Dennis and Vander Wal (2010) to measure an individual’s ability to challenge maladaptive thoughts in difficult situations and to develop alternative, more harmonious, and balanced thoughts. The scale comprises 20 items and includes two subscales: Alternatives and Control. High scores on the five-point Likert-type scale indicate high cognitive flexibility. Gulum and Dağ (2012) adapted the scale into Turkish and reported sufficient validity and reliability values. The Turkish adaptation of the scale was based on validity and reliability analyses conducted with undergraduate students. High scores on the scale indicate an increase in cognitive flexibility. In the Cronbach’s alpha analysis of the CFI within the scope of this study, we reached a reliability coefficient of .90, which indicates a high level of internal consistency.

UF/EMI Critical Thinking Disposition Instrument

The UF/EMI Critical Thinking Disposition Instrument was developed by researchers at the University of Florida (Irani et al., 2007) and was adapted to Turkish by Ertaş Kılıç and Şen (2014). The original scale included 26 items; however, one item was removed during the Turkish adaptation, resulting in a 25-item version. The scale measures three sub-dimensions—Engagement, Cognitive Maturity, and Innovativeness—and higher scores indicate a stronger tendency toward critical thinking. Validity and reliability analyses conducted with university students showed that the scale had adequate psychometric properties in this age group (Yokus & Yildirim, 2023). In this study, the Cronbach’s alpha analysis of the scale produced a reliability coefficient of .92, which indicated a high level of internal consistency.

Academic Literacy Self-Efficacy Scale (ALSES)

ALSES was the measurement tool developed within the scope of this study. During the development of a standardized instrument to measure university students’ academic literacy self-efficacy, we followed the scale development steps suggested by Slavec and Drnovšek (2012; see Figure 1).

Figure 1.

Scale development steps (Slavec & Drnovšek, 2012).

As shown in Figure 1, ALSES was developed in three main stages and 10 steps. The first stage involved conducting a literature review to establish the theoretical framework of the scale. During this stage, we examined relevant studies and scales that could be theoretically related to the scale and attempted to formulate the items. Additionally, we interviewed two academics who teach courses on scientific research methods to expand the item pool. We asked them about students’ knowledge and awareness of science and scientific processes, as well as common difficulties encountered in the course content. We also received item suggestions directly related to the scale. At the end of this process, we reached an item pool of 91 items. We then examined the statements in the pool and removed 34 items that were repetitive, unclear, or unrelated to the subject context. The remaining pool contained 57 items. During the second stage of establishing the theoretical structure, we sought expert opinion to determine whether the 57-item scale battery measured university students’ scientific literacy self-efficacy. Three field experts evaluated the scale items on a scale of 1–10 and suggested alternative items. Based on their feedback, we revised some items, removed 21 items with low scores, and obtained a 36-item form.

In the second stage of the ALSES development process, we transformed the form, which comprised 36 items, into a scale format. During this process, we made the necessary adjustments by consulting two Turkish language teachers to review the scale for language, spelling, and comprehensibility. In the following stage, as a pilot application, 20 students from different grade levels at the university completed the scale and rated their understanding of each item. At this stage, we found that the items had a sufficient level of comprehensibility, and we began data collection using the 36-item form.

Procedure

The research process began with obtaining ethical permission from … University’s Social Sciences and Humanities Ethics Committee (decision no. …, dated …). We collected data from various universities using an online form. The links to the online form were distributed within student groups at accessible universities. Participants who completed the forms were asked to share the link with as many students as possible. The form clearly stated that personal information would remain anonymous and that participation was voluntary. Before participants began to complete the research scales, they were asked to read a text that clearly informed them about the research process, and volunteers were then asked to tick an electronic consent form.

Data Analysis

The study analyzed two separate data sets. We subjected the first data set to EFA to develop ALSES, and we subjected the second data set to CFA and structural equation modeling analyses to determine the relationships between variables.

Data Preparation and Testing Assumptions of Multivariate Analysis

Before conducting statistical analyses on the research data, we removed any data that was obtained without voluntary consent, was systematically filled out on the form, or included extreme values based on Mahalanobis values from the data set. We then examined the collected data to determine whether it met the assumptions of multivariate analysis, including normality, multicollinearity, homoscedasticity, and homogeneity of variance. Because Bartlett’s test was significant in study group 1, we analyzed the data using the maximum likelihood technique. We applied Direct Oblimin, one of the oblique rotation methods, because of the relationship between the sub-dimensions. In study group 2, we performed maximum likelihood technique analyses with bootstrap resampling 5,000 times at a 95% confidence interval (Byrne, 2010) because the assumption of multivariate normality was not met (multivariate kurtosis: 148.65; c.r. = 56.36). We analyzed variable pairs with scatter plots to assess linearity and homoscedasticity. The analysis showed a linear relationship between the variables, with variances distributed homogeneously, forming an elliptical dot array (Tabachnick & Fidell, 2012). To examine potential multicollinearity issues between the variables, we first analyzed the correlation values and found that no pair of variables had a very high correlation (see Table 2). Furthermore, the VIF values were considerably smaller than 10, indicating the absence of multicollinearity issues between the variables (Cokluk et al., 2010).

Item Parceling

In studies that used structural equation modeling (SEM), problems could occur with goodness of fit values when the number of parameters in the model increased. To address these problems, the item parcellation process was recommended. This process involved averaging and combining items, as described in the literature (Güler & Çetin, 2020; Şen, 2020). When determining the number of parcels into which to divide the items, we followed the recommendation of Little et al. (2002) to use three parcels to obtain the best-fit values. Accordingly, we divided the “Academic Writing (AW)” section, which comprised 12 items, into two parcels of six items each (AW1 and AW2). We used the “Scientific Knowledge (SK)” section, which comprised six items, as an indicator on its own. Similarly, the seven-item control sub-dimension of the cognitive flexibility scale was used as an indicator, while the 13-item alternative dimension was divided into two parcels: Alt1 (seven items) and Alt2 (six items). The Critical Thinking Disposition Instrument comprised three sub-dimensions, each of which was accepted as a parcel: Eng, CM, and Inn. SPSS was used for the scale development stages of the research, and the AMOS package program was used for confirmatory factor analysis and to identify the relationships between variables.

Findings

The findings obtained in this study are presented in two stages. First, we present the findings related to the ALSES development process. Then, we present the findings related to the relationships between the variables.

Findings Related to ALSES Development Process

During the ALSES development process, we first conducted Exploratory Factor Analysis (EFA) and reliability studies on the data obtained from Study Group 1, and then performed Confirmatory Factor Analysis (CFA) on the data obtained from Study Group 2. In the initial stage of the analysis, we determined the suitability of the data for EFA using the KMO value (.94) and Bartlett’s test (X2 = 6342.42, p < .01), which were conducted before performing the EFA (Cokluk et al., 2010; Field, 2009). To determine the number of factors in the scale’s structure, we used Catell’s slope test to identify the point where the curve changes direction (Pallant, 2017). Additionally, we followed the rule that a factor’s contribution to explaining the total variance should not be less than 5% (Can, 2019). Considering that explained variance between 40% and 60% in multi-factor structures is sufficient (Büyüköztürk, 2010; Tavşancil, 2002), we found that sufficient variance values were reached. When the scale items were mostly concentrated in the first factor, it was challenging to combine the factor on a common denominator. To address this issue, the rotation process was necessary (Can, 2019). After the rotation process, we removed 18 items with low factor loadings, low common factor variance, or a low factor loading difference between the dimension and other dimensions (Can, 2019; Ho, 2006). Consequently, a two-dimensional structure was obtained. The measurement tool now explains 49.13% of the total variance. When naming the sub-dimensions of the scale, we consulted field experts to identify the common theme of the items. Based on their feedback, we named the first dimension “Academic Writing (AW),” which comprised 12 items, and the second dimension “Scientific Knowledge (SK),” which comprised six items. We assessed the reliability of the scale through Cronbach’s Alpha analyses. Cronbach’s Alpha internal consistency values were α = .92 for AW and α = .83 for SK. Table 1 presents the common factor variance (h²), factor loadings, Cronbach’s Alpha (α) values, eigenvalues of the factors, and explained variance.

Table 1.

The Common Factor Variance (h²), Factor Loading, Cronbach’s Alpha (), and Eigenvalues of the Factors and Explained Variance.

		h ²	Factor loading
ALSES item		h ²	F 1	F 2
Factor 1. Academic writing (AW)Cronbach’s α = .92
30	I know which knowledge should be included in the abstract of a research paper.	.66*	.88*	−.12
29	I know what to look for when writing a title.	.56*	.84*	−.22
31	I know which knowledge to include in the introduction of a scientific text.	.65*	.82*	−.04
36	I know which knowledge should be included in the conclusion section.	.53*	.74*	−.03
32	I know which knowledge will be provided in the method section of a scientific study.	.62*	.67*	.15
28	I know the differences between introduction, method, findings, and conclusion, which form the basic components of a scientific study.	.48*	.63*	−.01
35	I know how to interpret research findings.	.62*	.57*	.18
26	I know what keywords in research are used for.	.62*	.56*	.19
34	I know how to present research findings.	.67*	.53*	.25
33	I have knowledge about sampling.	.53*	.50*	.22
25	I can create keywords for a study I will conduct.	.61*	.49*	.27
27	I know the stages of scientific research.	.56*	.48*	.29
	Eigenvalue: 7.42; Contribution to total variance: 41.23
Factor 2. Scientific knowledgeCronbach’s α = .83
10	I know the nature of scientific knowledge.	.59*	−.05	.79*
9	I know the basic principles of scientific research.	.52*	.01	.72*
14	I pay attention to scientific ethics.	.53*	.05	.70*
12	I can distinguish the differences between scientific terms, such as theory, law, and hypothesis.	.38*	.04	.60*
13	I can establish the relationship between science and social development.	.40*	.08	.58*
11	I can relate scientific knowledge to my life.	.27*	.01	.51*
	Eigenvalue: 1.42; Contribution to total variance: 7.90
	Total explained variance: 49.13Total Cronbach’s α = .92

Note. n = 327. Extraction Method: Maximum Likelihood; Rotation method: Oblimin with Kaiser Normalization. The loading values of the items on the factors are bold.

p < .01.

We conducted CFA to test whether the two-factor structure obtained from EFA was confirmed in a different sample. We used item factor loadings, goodness of fit, and t-values as references when evaluating the CFA results. Figure 2 shows the goodness of fit values and standardized path coefficients obtained in the analyses. Table 2 shows the fit values of this model and the standard fit index.

Figure 2.

Relationships between sub-dimensions and standardized item factor loadings.

Table 2.

Goodness-of-Fit Indices for the Confirmatory Factor Analysis Model.

Fit index	Threshold	This model	Result
^fχ²/df	χ²/df ≤ 5	4.24	Accept
^a,d,eCFI	CFI > .90	.90	Accept
^b,cRMSEA	.08 < RMSEA ≤ .10	.09	Accept
^e,fSRMR	SRMR ≤ .08	.06	Accept
^bGFI	.85 ≤ GFI < 95	.86	Accept

Note.χ² = 559,727, df = 132, p < .001.

Byrne (2010).

Çelik and Yılmaz (2013).

Kline (2005).

Hu and Bentler (1999).

Schermelleh-Engel et al. (2003).

Şimşek (2007).

Figure 2 shows that the goodness of fit values for the confirmatory factor analysis conducted on ALSES (χ²/df = 4.24, CFI = .90, RMSEA = .09, GFI = .86, SRMR = .06) were within the acceptable reference range (see Table 2). However, the goodness of fit values shown in Figure 2 were obtained because of the modification between the error terms e7–e8 and e16–e17 in the Academic Writing (AW) sub-dimension. Figure 2 also shows that the standardized path coefficients for the Academic Writing (AW) sub-dimension ranged from .59 to .85, while those for the Scientific Knowledge dimension ranged from .56 to .82. Furthermore, we observed that each item significantly contributed to the model, as evidenced by the t-values of the factors in which the items were situated (p < .01).

Findings on the Relationships Between Research Variables

After we confirmed that the ALSES developed in this study was a valid and reliable measurement tool, we examined the correlation between the research variables. Table 3 shows significant, medium-level relationships between the variables.

Table 3.

Descriptive Statistics and Correlations of Variables.

Variable	M	SD	1	2	3	4	5	6	7	8	9
1. Engagement (Eng)	4.03	.50	—
2. Cognitive maturity (CM)	4.11	.45	.69*	—
3. Innovativeness (INN)	4.06	.48	.83*	.74*	—
4. Alternatives1 (Alt1)	4.04	.54	.53*	.55*	.51*	—
5. Alternatives2 (Alt2)	3.99	.64	.53*	.48*	.48*	.79*	—
6. Control (cont)	3.33	.79	.28*	.24*	.20*	.32*	.39*	—
7. Scientific knowledge (SK)	3.62	.71	.42*	.31*	.38*	.35*	.33*	.21*	—
8. Academic writing (AW1)	3.94	.67	.42*	.27*	.36*	.36*	.33*	.16*	.66*	—
9. Academic writing (AW2)	3.65	.77	.43*	.28*	.40*	.31*	.35*	.23*	.63*	.77*	—

Note. n = 414.

p < .01

Finally, we used structural equation modeling to examine the relationships between the research variables. The findings obtained from the analysis conducted within this framework are presented in Figure 3.

Figure 3.

Results of SEM analysis.

Figure 3 shows the goodness of fit values for the structural model that examines the relationships between variables (χ²/df = 3.39, CFI = .98, RMSEA = .08, GFI = .96, SRMR = .04). The model was confirmed according to Byrne (2010), Çelik and Yılmaz (2013), Hu and Bentler (1999), Kline (2005), Schermelleh-Engel et al. (2003), and Şimşek (2007). When we examined the path coefficients in the model, we observed that all paths were significant. Both cognitive flexibility (β = .21, p < .01) and critical thinking disposition (β = .36, p < .01) were significant correlates of academic literacy self-efficacy. These two variables accounted for 28% of the total variance in academic literacy self-efficacy.

Discussion and Conclusion

This study developed a scale to measure the academic literacy self-efficacy of students in higher education and examined the relationships between students’ academic literacy self-efficacy and their cognitive flexibility and critical thinking levels. In this section, we discuss the results of the study. The first aim of this research was to develop ALSES and calculate its reliability and validity scores. For this purpose, we used the scale development process proposed by Slavec and Drnovšek (2012) as a reference. Within this framework, the EFA process resulted in a structure that explained 49.13% of the total variance. This explained variance ratio was within acceptable limits for a two-factor scale (Büyüköztürk, 2010; Tavşancil, 2002). The Cronbach’s alpha analysis of the scale’s sub-dimensions showed that the sub-dimensions had sufficient internal consistency (Tavakol & Dennick, 2011). The goodness of fit values, standardized path coefficients, and t-values in the CFA analysis conducted after the EFA demonstrate that the scale was within the acceptable reference range (Byrne, 2010; Çelik & Yılmaz, 2013; Hu & Bentler, 1999; Kline, 2005; Schermelleh-Engel et al., 2003; Şimşek, 2007). We made two modifications to the AW sub-dimension during the CFA process. The modification procedures indicate that a small number of modifications can be made, provided that they are among the items under the same latent variable (Gurbuz, 2021). All the data from the study show that the ALSES is a valid and reliable instrument for measuring the academic self-efficacy of university students.

Our analysis identified a moderate relationship between critical thinking skills and cognitive flexibility. Similarly, studies in the literature (Baysal Doğruluk, 2021; Güner & Gökçe, 2021; Jason, 2001; Kuşdemir, 2023) have also found a moderate relationship between cognitive flexibility and critical thinking dispositions. Gökçe and Güner (2024) identified a positive and statistically significant effect of cognitive flexibility on critical thinking disposition. Consistent with these results, R. W. Paul (1990) states that critical thinking shows a structure related to cognitive flexibility, which is regarded as the ability to adapt to new situations in terms of testing the strengths and weaknesses of different perspectives. The fact that cognitive flexibility is one of the processes involved when an individual begins to think critically (Scheibling-Sève et al., 2022) may explain the relationship between cognitive flexibility and critical thinking. Furthermore, this relationship may be related to the fact that both skills require individuals to look at events from different perspectives, evaluate situations from multiple angles, and consider multiple variables in decision-making processes.

We found a moderate relationship between academic literacy self-efficacy and cognitive flexibility. We did not find any research in the literature that examined the relationship between academic literacy self-efficacy and cognitive flexibility. However, studies in the literature (Akyüz, 2020; Çelikkaleli, 2014; Gan et al., 2007; Kim & Omizo, 2005; Martin & Rubin, 1995; Pepe, 2021; Shimogori, 2013) identified a relationship between cognitive flexibility and self-efficacy. Furthermore, Kercood et al. (2017) found that cognitive flexibility is related to reading and writing, which are components of academic literacy. The finding that individuals with high self-efficacy also have high levels of cognitive flexibility (Bandura, 2000; Martin & Rubin, 1995) may explain this relationship. Flexibility is the ability to change and adapt to the environment (Georgsdottir & Getz, 2004, p. 166), and cognitive flexibility is the ability of individuals to adapt their cognitive processing strategies to new situations in their environment (Cañas et al., 2003). Considering these statements, the relationship between academic literacy, which represents a new situation for students, and cognitive flexibility can be explained.

We found a moderate relationship between the academic literacy self-efficacy and critical thinking skills of the students who participated in the study. Similarly, studies in the literature (Ayyıldız Çolak, 2022; Elçi, 2022) also identified a moderate relationship between academic literacy and critical thinking disposition. In addition to these studies, other research in the literature (Bayat, 2014; Catterall & Ireland, 2010; Wingate et al., 2011) found a relationship between critical thinking and academic writing, which is one of the sub-dimensions of academic literacy. Kasper and Weiss (2005) also noted that studies in the literature emphasized the relationship between critical thinking skills and the development of academic writing. Similarly, the study by Taghinezhad et al. (2019) found that teaching critical thinking contributed to academic writing skills. According to Hammer and Green (2011), the development of critical reading should be supported to improve academic writing skills. Because critical thinking includes critical writing, critical reading, critical speaking, and critical listening (R. Paul et al., 1989), this relationship can be explained by the fact that reading and writing are the foundation of academic literacy. Therefore, we can posit that individuals who exhibit high levels of critical thinking are also characterized by a high level of academic literacy self-efficacy.

Recent studies have demonstrated a consistent correlation between cognitive flexibility and general self-efficacy (Bandura, 2000; Martin & Rubin, 1995). We examined a novel aspect of this relationship by focusing on academic literacy self-efficacy, which is a more specific construct. The moderate correlation between academic literacy self-efficacy and cognitive flexibility aligned with previous findings that suggest flexible thinking is related to perceptions of academic competence. Furthermore, this result supports Bandura’s (1997) social cognitive theory, which emphasizes the role of self-efficacy in regulating emotions and behaviors. Martin and Rubin (1995) found that cognitively flexible individuals adapt more easily to change, and Eze et al. (2022) indicated that high critical thinking skills are effective in solving complex problems when information changes and evolves. These findings informed our interpretation of the results. In this context, it is understandable that both cognitive flexibility and critical thinking tendencies are related to self-efficacy beliefs about complex skill areas in higher education, such as academic literacy. Additionally, the observed relationship between academic literacy and critical thinking is consistent with the theoretical framework proposed by R. Paul et al. (1989), which states that critical thinking plays a decisive role in academic achievement and assessment processes.

In the context of the relationship between critical thinking and academic literacy self-efficacy, we suggest that integrating critical thinking activities into academic reading and writing studies in higher education would benefit students. Studies have examined the potential for integrating academic writing and critical thinking skills (Rodríguez-Escobar & Saldías, 2025). Additionally, researchers have proposed ways to develop flexible thinking, which is an essential skill in the 21st century, within educational contexts (Örün & Sever, 2025).

When evaluating the results of the study in general, it can be said that the developed instrument for measuring academic literacy self-efficacy is a valid and reliable tool. Cognitive flexibility and critical thinking can also be considered in the development of academic literacy and the creation of an academic literacy program in higher education. As stated in the literature (Clark, 2004; Taghinezhad et al., 2019), researchers have argued that critical thinking education should be included in academic writing education. In this way, both students’ critical thinking skills and academic literacy skills can be developed together. The assessment of academic literacy self-efficacy in higher education and the effectiveness of the academic literacy program can be measured with the ALSES, which we developed in this study.

Limitation and Future Studies

One of the key limitations of this study was the use of a convenience sampling method instead of a randomized sampling method. This approach might have increased the likelihood of validity and reliability concerns, including issues with representativeness, bias, and limited sample diversity (Büyüköztürk et al., 2008). To address this limitation, we expanded the two sample groups used in the EFA and CFA processes to a large sample size, which represents the strongest sample size recommended by Kline (2005). Future research replicating the validity and reliability analyses of the scale with more specific study groups may show the extent to which this limitation affects the scale. Additionally, when evaluating this study’s findings, we recommend considering that sociocultural structure and educational processes may shape academic literacy, critical thinking, and cognitive flexibility.

Footnotes

ORCID iDs

Mustafa Onur Kan

Idris Kaya

Ethical Considerations

Ethics committee approval was obtained for this study from the Gaziantep University Social and Human Sciences Ethics Committee (Meeting number 11, Decision number 05), with its decision dated 05/10/2023.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Statements of Publication Ethics

We hereby declare that the study has not unethical issues and that research and publication ethics have been observed carefully.

Researchers’ Contribution Rate

The study was conducted and reported with equal collaboration among the researchers.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

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