1 Introduction

Learning styles define the dissimilarities in how students process learning [45]. Features such as the surrounding environment and personal experiences affect the way learners choose their idiosyncratic styles. Thus, teachers should supply teaching analyses accustomed to learners’ desired styles. According to several studies (e.g., [3, 40], a learning style is described as preferences, characteristics, and abilities every individual processes and receives information with. It is also related to the fact that individuals have personal strategies while learning (see, [5, 13, 66]). In other words, creating a roadmap for the future can provide a basis for practical standards that fit individuals learning styles. Accordingly, reading is assumed fundamental to learning, it seems rational to make individual learners aware of the concept of optimal learning situation and why some readers outperform others as good readers. Previous studies (e.g., [6, 67]) have indicated that reading comprehension is relevant to cognitive factors and styles. Learners take perspectives and have different perceptions that are the result of the interaction between the environment [28] and their cognitive styles [42]. Reading comprehension is the process of unlocking meaning from connected text [6]. Reading is an integral section of language tests, and it can change the academic and professional lives of many students (cf. [65]. Indeed, students have different levels of performance in language skills. They argued that learning styles are crucial in students’ learning strategy choices, and specific styles can lead to learners' success. Utilizing members of a group’s cognitive styles leads to increase in their collective intelligence [1]. Hence, it can be assumed that when teachers provide their teaching styles adjusted to the students’ needs positive outcome can be expected. Employing two sets of data, a learning style questionnaire and a reading comprehension test, this study investigates and discusses the potential correlation between EFL learners' reading performance and their learning styles.

1.1 Theoretical background

Learning styles, formerly discussed as cognitive styles, are grounded on a psychological basis and form long-lasting and constant characteristics [62]. A learning style is “the more or less consistent way in which a person perceives, conceptualizes, organizes and recalls information and students’ learning styles will be influenced by their genetic make-up, previous learning experiences, culture and the society they live in” [27]. In this vein, Kozhevnikov [46] argues that a learning style is consistent dynamic that is not necessarily influenced by other variables. Even in multicultural settings, teachers can benefit from awareness of their learners’ cognitive styles, as basic cognitive styles may not change in different contexts [9]. The theory is revisited very often, with new terminology and divisions. This diversity makes it challenging to create a unified model or indicator of styles. Nevertheless, enhancing learners' awareness about their learning process, factors, and styles empowers them to match their strategies to their assignment [54]. While learning styles and strategies are sometimes used interchangeably, in some studies, they are defined differently. For instance, learning styles are considered the general approaches to learning, whereas learning strategies are the unique and particular systems to take in a specific setting [58]. Learners need to recognize the learning strategies and their strengths and, therefore, develop their learning capacity,also, they can take advantage of learning styles by coordinating learning strategies with them. When teaching tasks are aligned with learners’ cognitive styles, their learning capabilities will be enhanced [2]. This is a form of engineering education. Still, educational settings have not benefited from the available literature regarding different learning styles. Neither learners' preferences and styles are tested according the programs nor teaching styles are designed and practiced to match their cognitive desires. Learning styles are attached to individual’s personality traits and teaching in line with the learners’ expectations enhances their mental skills [11], their level of proficiency [18], and their listening skills [67]. A considerable number of studies has been carried out, and many tools such as self-report surveys, interviews, direct observations, and think-aloud techniques are accessible to investigate the learning styles. In addition, Teachers need to be aware of learners’ preference to provide them with the most effective corrective feedback (for detailed examples, see [8]. Theorists view learning styles differently, and their methods for assessments and observations differ. De Bello [10] claims that there are numerous interpretations of learning styles. Some widely employed models and instruments are listed below.

  • Myers-Briggs [49]—Type Indicator

  • Dunn and Dunn [21]—Learning Style Assessment Instrument

  • Gregorc [37]—Mind Styles and Gregorc Style Delineator

  • Felder and Silverman [33]—Index of Learning Styles

  • Oxford [53]—Learning Styles and Strategies

  • McCarthy [47] —4 Mat System

  • Honey and Mumford [39]—Learning Styles Questionnaire

  • Kolb [44]—Learning Style Inventory

  • Gardner [36]—Multiple Intelligence Inventory

  • Herrmann [38]—Brain Dominance Model

  • Ehrman and Leaver [25]—Learning Styles Questionnaire

  • Fleming [35]—VAK/ VARK Model

These models have shared and different characteristics, and apparently, their particular advantages. In summary, various learning strategy instruments have produced results beyond those mentioned above. Ehrman and Leaver’s model provides sufficient information in more facets, which is employed in this study. As frequently stated, each learning style model proposes different descriptions and classifications. Therefore, only an overview of Ehrman and Leaver is manageable in the following section.

1.2 Ehrman and leaver learning styles questionnaire

Ehrman–Leaver cognitive styles consists of a superordinate construct, synopsis–ectasis, and ten subscales. The ten opposite pairs are; field-independent vs. field-dependent, random vs. sequential, field-sensitive vs. field-insensitive, global vs. particular, impulsive vs. reflective, inductive vs. deductive, synthetic vs. analytic, analog vs. digital, concrete vs. abstract and leveling vs. sharpening. Initially, the questionnaire [25] saw a systematic, conceptual link among the scales. They initially thought that global, inductive, random, and leveling were ways for learners to group the information or treat it all at once and so used the term ‘synopsis’ to refer to that set of poles, and ectasis was adopted for the other pole [25]. Shortly, synoptic learners trust their intuition and primary perceptions, but ectenic learners tend not to. Take, the impulsive–reflective subscale, as an example. Reflective learners need to contemplate and think deeply about an entity. While impulsive learners prefer to answer questions immediately, probably with the least amount of thinking. Many researchers and theorists (e.g., [48, 64, 65] argue that a field-sensitive learner might be field-dependent or independent, impulsive or reflective in an unpredictable way. Unfortunately, such grouping of styles has not received sufficient attention, or probably it is cumbersome and challenging to pursue. The Ehrman and Leaver Learning Styles Questionnaire consists of three items for each of the ten subscales (for detailed explanations about the constructs of the questionnaire, see Ehrman & Leaver, [26].

1.3 Methodology and design

Since the data was collected from individual participants on two settings, we deployed a one-shot case design, with a quantitative approach. The participants of this study were 137 EFL advanced learners. They were administered a TOFEL PBT test for the recruitment examination. The test consisted of five sections, namely; listening, vocabulary, reading, cloze test, and grammar. Only the reading scores were collected and analyzed. Each section of the test was assigned 100 scores. In order to pass the test, the test takers had to succeed in 50 percent of each section to be recruited as teachers.

1.4 Participants

We recruited two groups of participants with the population of 137 EFL advanced learners. 31 participants were non-randomly selected qualified English teachers at Iran Language Institute, so that we had a homogeneous group of participants who had passed the teaching recruitment test previously. And 106 participants of this study were teaching candidates selected based on convenience sampling who attended the test. All of the participants were native Persian speakers. Age and gender were not studied in this studied, hence the participants were selected regardless of these variables.

1.5 Data collection procedures

Initially, formerly recruited EFL teachers filled out Ehrman and Leaver’s questionnaire [26] (E & L). The previously qualified teachers participated in the study individually. Their reading scores were available at the administrative office. The data related to their reading scores were excavated from the results of their tests as the test consisted of more modules. Subsequently, the teaching candidates filled out (E & L) questionnaires after their TOFEL PBT. Their reading scores were collected from the administrative office after 1 month. To quantify the results from the (E & L) questionnaire, an ecteno-synoptic continuum was created and scored from zero to ten. Every synoptic feature equaled a score. Thus, the candidate with 5 synoptic learning styles received 5. In other words, the spectrum began with zero that is a complete ectenic learner to ten that is a complete synoptic learner. A test of the Pearson Correlation Coefficient was provided to find a possible correlation between the ecteno-synoptic range and the scores achieved on the reading tasks. Eventually, a test of multiple regressions was calculated to signify the most prominent learning style.

2 Results

This section comprises four parts. At first, the results from descriptive statistics reporting minimum scores, maximum scores, the mean and standard deviation on the reading test, and the learners’ ecteno-synoptic scores are presented. Secondly, the inferential statistics regarding the first research question, including three tests of the Pearson-Product Correlation Coefficient, are provided. Third, the inferential statistics of multiple regressions, including the regression model summary, statistical significance through ANOVA, and estimated regression model coefficients, are represented. Finally, some discussions related to each analysis are provided.

The descriptive statistics provided in Table 1 compares the candidates reading scores from the examination and their ecteno-synoptic score.

Table 1 Learners’ Learning styles and Reading Performance Descriptive Statistics
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As is notable, Table 1 shows that 31 participants from the total number of 137 achieved significant reading scores, and the other 106 participants performed below the acceptable level of performance. The mean difference between the successful and failed learners’ reading performance is 33. The mean difference between the successful learners’ ecteno-synoptic scores and the failed ones is 2.1. This gap indicates a general relationship between the learning styles and the performance of the participants. Furthermore, to investigate the relationship between the participants learning (Tables 2, 3, 4) styles and their reading performance, three tests of the Pearson-Product Correlation Coefficient were provided; the first test found the relationship between all the scores achieved. The second found the relationship between the scores from successful examinees, and the last one found the relationship between the scores from the failed learners.

Table 2 The Correlation between All Ecteno-Synoptic Scores and Reading Scores
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Table 3 The Correlation between Passed Ecteno-Synoptic Score and their Reading Scores
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Table 4 The Correlation between Failed Ecteno-Synoptic Score and Learners' Reading Scores
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A Pearson product-moment correlation was run to determine the relationship between Ecteno-Synoptic Score and Reading Score. There was a strong, positive correlation between Ecteno-Synoptic Score and Reading Score, which is statistically significant (r = 0.768, n = 137, p = 0.000).

A Pearson product-moment correlation was run to determine the relationship between Passed Learners Scores and Passed Ecteno-Synoptic Scores. There was a strong, positive correlation between Passed Learners Scores and Passed Ecteno-Synoptic Scores, which is statistically significant (r = 0.706, n = 14, p = 0.005).

A Pearson product-moment correlation was run to determine the relationship between Failed Learners Scores and Ecteno-synoptic Scores. There was a strong, positive correlation between Failed Learners Scores and Failed Ecteno-synoptic Scores, which is statistically significant (r = 0.706, n = 14, p = 0.005).

Accordingly, analyses of multiple regressions, including the regression model summary, statistical significance through ANOVA, and estimated regression model coefficients, were conducted to investigate learning styles that may predict the reading performance of teaching candidates. Table 5 provides the R, R2, adjusted R2, and the standard error of the estimate, which can be used to determine how well a regression model fits the data. In general, the higher the R-squared, the better the model fits your data.

Table 5 Model Summaryk of the Regression Based on Learners Learning Styles and their Reading Scores
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Statistically, the "R" value signifies the quality of the prediction of the dependent variable. Consequently, the first model, field insensitive-sensitive, is the highest quality learning style (in a discrete fashion). A value of 0.66 indicates a good level of prediction, yet not the desirable and significant one. Considering all learning styles present in a learner, the most prominent model could be the last one. As a result, the most successful reading performance needs a learner with insensitive-sensitive, reflective-impulsive, Abstract-Concrete, sharpener-leveler, sequential-random, Digital-Analogue, Analytic-synthetic, deductive-inductive learning styles. As observable from the initial model with field insensitive-sensitive R2 value, which is 0.44, this independent variable explains 44% of the variability of our dependent variable, reading performance. Above all, significantly observable from Table 5, as R2 values increase, the standard error of the estimate values decreases (better fit and minor estimation error); this shows the model fits significantly well. The most predicative component in the field insensitive-sensitive learning style with the highest impact factor on learners’ reading proficiency. Nevertheless, the most compelling model is a coordinating combination of all learning styles (the first and the last model are represented below. To see all ten models, refer to the Appendix 2).

M1.: field insensitive-sensitive.

M10.: field insensitive-sensitive, reflective-impulsive, Abstract-concrete, sharpening-leveling, sequential-random, Digital-analogue, Analytic-synthetic, deductive-inductive.

The result from Table 6 indicates that the learners with higher ecteno-synoptic scores on these variables tend to have higher reading scores. None of the models is negatively correlated with the reading scores. This table illustrates that two of the models are most representative of the multiple regression model, with all ten predictors produced;

Table 6 ANOVAk of Learners Reading Performance and their learning Styles
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Model One: R2 = 0.75, F (8, 128) = 47.474, p < 0.001. Predictors: (Constant), field insensitive-sensitive, reflective-impulsive, abstract-concrete, field dependent-independent, sharpening-leveling, sequential-random, digital-analogue, analytic-synthetic.

Model Two: R2 = 0.76, F (8, 128) = 51.498, p < 0.001. Predictors: (Constant), field insensitive-sensitive, reflective-impulsive, abstract-concrete, sharpening-leveling, sequential-random, digital-analogue, analytic-synthetic, deductive-inductive.

The closeness of these two models analyzes the coefficients more interesting and vital to contribute to selecting one of them as the most appropriate model. Holding variables constant and analyzing their coefficients provided significant estimated model coefficients. The general form of the equation to predict reading scores from learning styles; field insensitive-sensitive, field dependent-independent, sequential-random, particular-global, deductive-inductive, analytic-synthetic, digital-analog, abstract-concrete, sharpening-leveling, reflective-impulsive, attained from the estimated model coefficients is field insensitive-sensitive, reflective-impulsive, abstract-concrete, sharpening-leveling, sequential-random, digital-analog, analytic-synthetic, deductive-inductive. That is if we consider the learning styles numerical items, reading score = 60.95—(6.81 × field insensitive-sensitive)—(18.76 × reflective-impulsive)—(13.16 × abstract-concrete)—(15.39 × sharpening-leveling)—(22.06 × sequential-random)—(7.43 × digital-analogue)—(13.40 × analytic-synthetic)—(8.97 × deductive-inductive). This predictor is obtained from the Coefficients table, as shown below:

The results from Table 7 show that the p-value for field dependent-independent is 0.189. This value is greater than the standard alpha level of 0.05, which indicates that it is not statistically significant. In other words, a field insensitive-sensitive predictor with this high p-value is unlikely to be a meaningful addition to the model because changes in its value are not related to changes in the criterion (response) variable (reading score). Typically, the coefficient p-values are employed to determine which terms are to be kept in the regression model. In the model obtained from Table 7 above, the removal of field dependent-independent is considered a fit for the model. The equation shows that the coefficient for field insensitive-sensitive learning style is 6.8. The coefficient indicates that for every additional score in this learning style, 6.8 scores rise in the reading are expected. Consequently, Model Two: R2 = 0.76, F (8, 128) = 51.498, p < 0.001 is considered fit, that is eight variables added statistically significantly to the prediction, p < 0.05.

Table 7 The Estimated Regression Model Coefficientsa
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3 Discussions

The results can be discussed as the analyses in the sections above demonstrated from the total number of 137 participants (31 participants achieved significant reading scores, and the other 106 participants performed below the acceptable level of performance). First, Table 1 showed that the mean difference between the successful and failed learners’ reading performance was 33. The mean difference between the successful learners’ ecteno-synoptic scores and the failed ones was 2.1, indicating that more synoptic learners were generally successful. The continuum in an overall observation would be 6–4. On average, successful learners enjoyed six synoptic learning styles and four ectenic learning styles. Second, it was found from Table 2 that there was a strong, positive correlation between ecteno-synoptic scores and reading scores. This result indicated that the more synoptic learning styles a learner possessed received a higher reading score on the examination; the more ectenic learning styles a learner possessed, the lower the learner received a lower reading score. Third, tables three and four confirmed the results from table two, which illustrated the successful and failed learners’ scores and their ecteno-synoptic scores in particular tests. There was a strong, positive correlation between passed learners’ scores and their ecteno-synoptic scores, and there was a strong, positive correlation between failed learners’ scores and failed ecteno-synoptic scores. Fourth, it was derived from Table 5 that the primary model for Iranian successful learning style at reading performance is being field-insensitive. Adding more learning styles to the model, the most prominent model was the last one which was successful reading performance = (Constant), insensitive-sensitive, reflective-impulsive, abstract-concrete, sharpener-leveler, sequential-random, digital-analogue, analytic-synthetic, deductive-inductive. It was found that synopsis is the favorable learning style superordinate to succeed in reading tasks. This model indicates that the more a learner was field sensitive, field-independent, impulsive, concrete, leveler, random, analog, synthetic, and inductive was successful, which is not the fact, even though the model fits significantly. The result from table one indicated that the mean difference between the synoptic and ectenic styles was only 2. Fifth, the result from Table 6 indicated that even though none of the models is negatively correlated with the reading scores. There are two of the models that are the most representative of the multiple regression models, with all ten predictors produced;

Model One: R2 = 0.75, F (8, 128) = 47.474, p < 0.001.

Predictors: (Constant), field insensitive-sensitive, reflective-impulsive, abstract-concrete, field dependent-independent, sharpening-leveling, sequential-random, digital-analogue, analytic-synthetic.

Model Two: R2 = 0.76, F (8, 128) = 51.498, p < 0.001.

Predictors: (Constant), field insensitive-sensitive, reflective-impulsive, abstract-concrete, sharpening-leveling, sequential-random, digital-analogue, analytic-synthetic, deductive-inductive.

The eventual general form of the equation derived from Table 7 to predict reading scores from learning styles; field insensitive-sensitive, field dependent-independent, sequential-random, particular-global, deductive-inductive, analytic-synthetic, digital-analog, abstract-concrete, sharpening-leveling, reflective-impulsive, attained from the estimated model coefficients was: field insensitive-sensitive, reflective-impulsive, abstract-concrete, sharpening-leveling, sequential-random, digital-analog, analytic-synthetic, deductive-inductive. That is if we consider the learning styles numerical items, reading score = 60.95—(6.81 × field insensitive-sensitive)—(18.76 × reflective-impulsive)—(13.16 × abstract-concrete)—(15.39 × sharpening-leveling)—(22.06 × sequential-random)—(7.43 × digital-analogue)—(13.40 × analytic-synthetic)—(8.97 × deductive-inductive). The closeness of these two models made the coefficients more interesting and vital to contributing to selecting one of them as the most appropriate model.

The findings of the study are in accordance with a large body of research (e.g., [4, 7, 12, 16, 17, 24, 34, 35, 41, 43, 50, 55, 57, 63]. The importance and effectiveness of learning-style-matched teaching and learning a foreign or second language is highlighted in some studies (see, [16, 18, 65]. Research on learning styles must be theoretically well-motivated, which the current study’s findings are thoroughly in line with their findings and affirms that knowing learners’ styles and providing instructions to the favorable ones are highly effective. The findings also accord with Cohen and Weaver [15] and Nunan [51] who found that this learner-oriented instructions and strategies led to higher motivation, self-efficacy, and proficiency.

Based on Ehrman and Leaver [26], ectenic learners are field insensitive, field-dependent, reflective, abstract, sharpener, sequential, digital, analytic, and deductive. Besides, synoptic learners are field sensitive, field-independent, impulsive, concrete, leveler, random, analog, synthetic, and inductive. Based on the model found in this study, successful Iranian learners tend to be synoptic but not complete synoptic learners. That is, learners are exposed or imposed to various learning styles on a continuum. Directly or indirectly, the current results accord with studies [19, 20, 22, 23] as they all claimed that there is no individual with a fixated learning style and learners possess various styles which might be strong or more potent at one style in comparison to another. In other words, they take advantage of one style at a time and the others at another. This is in line with studies [41, 56, 59].

Such findings contradict researchers who claimed there appears the little existence or absence of a published independent evaluation of self-report measures [3]. Peterson, Rayner, and Armstrong [60] reclaimed that cognitive or learning styles’ significant potential source of the problem is the quality of research on the topics. In brief, these contrastive claims about the efficacy of learning styles seem to have partly resulted from measures of development and the contexts in which the studies were conducted, the current study in the context of Iran revealed the opposite findings, and it is claimed that there were high positive correlations found between learners’ reading success and the type of learning style they had. Moreover, the results from multiple regressions revealed that particular types of learning styles are great predictors of achieving high reading scores and proficiency.

The chief objective of the study was also to reveal whether particular learning styles are predictors of reading scores and the model which fits. According to the analyses, there were significant positive correlations between the learning styles and the reading scores obtained and the regression model of field insensitive-sensitive, reflective-impulsive, abstract-concrete, sharpening-leveling, sequential-random, digital-analog, analytic-synthetic, deductive-inductive was concluded fit and significant. In this respect, the findings reaffirm the studies of EFL learners in various countries (e.g., [14, 53] reveal that some strategies (mostly metacognitive ones) are likely to be predictors of proficiency among second language learners.

In summary, particular learning styles can be predicted among Iranian EFL learners. A number of these findings have been summarized in the literature review. However, Ehrman and Leaver’s model provides sufficient information in more facets. As frequently stated, each learning style model proposes different descriptions and classifications of learning styles. In this study, we investigated the relationship between learning styles and L2 reading task performance results and possible predictors based on Ehrman and Leaver Questionnaire [26].

4 Conclusion

It is concluded that the predicting learning styles resulting from the multiple regression model suggest that Iranian EFL learners hierarchically need be field sensitive, impulsive, concrete, leveling, random, analog, synthetic, and deductive to achieve the highest reading score and proficiency. Incorporating learning styles into the classroom adds variety and opportunities for effective language production. Providing tasks and teaching methodologies that benefit different types of learners with different features and psychological mindsets in classrooms contributes to practical and utilized learning. It is recommended that teachers provide analyses in classes and highlight their learners’ styles to find their strengths in classes being adjusted to learners’ aims after elaborate needs analyses. Iranian EFL learners must hierarchically be field sensitive, impulsive, concrete, leveling, random, analog, synthetic, and deductive to achieve the highest reading score and proficiency. Respectively, learners need to gather material by osmosis which happens gradually through unconscious assimilation of ideas and knowledge by relating everything together and observing the whole scene. It is recommended that learners move from specific to the general and have the assembly of components into a constructed whole, interact with the world, be experiential, look for similarities, and react quickly as having thoughts after actions. The entire point of these learning style models is to understand students’ tendencies for learning, and to help them branch out into other learning styles, rather than being fixated in all comfort zones. So whether you are puzzling out which dimension you fit into to grasp your methods, or thinking about how to alter your teaching to reach students of various styles, what is worth remembering is that variety is the key. Knowing that you have a range of learners in your class and that they will all benefit from exploring the whole array of learning styles can help you design activities that will exercise a range of processes and perspectives.

5 Suggestions for further studies

In our study, gender differences were not under investigation, which, as learning styles are directly related to individual differences, compelling results may be concluded. Other levels and types of tests could be challenging studies to be conducted as the models were reviewed in the literature. This study only considered the relationship between learning styles and the performance of EFL learners on reading tasks; more skills can be investigated. The domain yields research on various aspects of learning styles and preferences not only in the field of language learning, but also in other fields such as STEM and in particular mathematics [30, 31]. Future research can investigate the factors (both verbal and nonverbal) which affect learners' learning styles in mathematics and other STEM related subjects [29].