The Relationship between Learning Styles, Study Effort and English Language Proficiency in Chinese Middle Schools
Shih Ching Wang*, Eleanor Willard
Department of Psychology, School of Social Sciences, Leeds Beckett University, Leeds, UK
*Corresponding Author: serchenwang@yahoo.com Accepted: 15 July 2022 | Published: 1 August 2022
DOI:https://doi.org/10.55057/ajress.2022.4.2.18
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Abstract: Though disputed and deemed a neuromyth, many teachers are still convinced by the idea that identifying and later tailoring instructional strategies to individuals’ learning styles would enhance learning. This correlational study considers visual-auditory-kinaesthetic (VAK) preferences as a range instead of a category, and investigates the concept as applied to language learning, and the compensatory effects of study efforts exerted while learning a foreign language. The results showed that kinaesthetic preference was a poor predictor of English exam scores in contrast to visual and auditory preferences, and that effort played a significant role in predicting exam scores when combined with kinaesthetic preferences, suggesting a possibility that learners with kinaesthetic learning predispositions might have to compensate by working harder to attain higher language proficiency levels as compared to learners with visual or auditory learning preferences.
Keywords: Learning preferences, language learning, study effort
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1. Introduction
Learning style proponents postulate that academic performance will be enhanced if instructional adjustments are made according to students’ learning styles (Calhoun et al, 2014), and that not differentiating the instructional process will hinder learning (Tileston, 2010, p15).
Nonetheless, even advocates like Cassidy (2004) concede that the concept is merely an assumption with inconsequential empirical support. Though the concept has been deemed a neuromyth (Lethaby & Mayne, 2018), the intuitive appeal remains strong, and many teachers are still convinced by it (Lethaby & Harris, 2016).
This study considers individuals as multimodal, takes the degree of each perceptual preference into account, and attempts to investigate the correlations between the degrees of preference and test scores. In addition, as previous studies do not consider the effect of compensatory strategies (e.g., working harder) that potentially counteracts the effects for having an assumed
“disadvantaged” learning preference, this study seeks to investigate if each learning preference, when coupled with the amount of effort the students put in, could co-predict higher scores on English exams.
2. Literature Review
2.1 The Learning Styles Concept
Regardless of the model, proponents of the concept postulate that student performance will be enhanced if language teachers make instructional adjustments according to the students’
learning styles - a hypothesis known as the ‘Meshing Hypothesis’ (Calhoun et al, 2014), or
‘Learning Style Hypothesis’ (Bjork et al, 2009). Supporters claim that optimal instruction requires identifying an individual’s learning style, and to tailor the instruction process accordingly (Jowkar, 2012; Melton, 2008). Other researchers (Gilbert & Swanier, 2008;
Tileston, 2010) postulate that mismatches between teaching and learning styles could cause frustration, implying that not differentiating the instructional process would potentially impede the learning of some students.
The concept holds various definitions, theoretical positions, interpretations and measures of the construct (Calhoun et al, 2014). In his book, Reid (1995) lists some of the more popular inventories, for example, Field-Independent / Field-Dependent Learning Styles, and Myers- Briggs Temperament Styles. Hawk and Shah (2007) attempted to merge various models that share similar conventions and interpretations, but were unsuccessful. The list is not exhaustive, though one particular model which generated considerable interest is the Visual-Auditory- Kinaesthetic (VAK) model. However, even within this model, there are several interpretations, with Neil D Fleming’s VARK (Visual, Auditory, Reading, and Kinaesthetic) version (1992;
2012a; 2012b) and Dunn and Dunn’s VAK version (1979) being more prominent. Though these are similar models, few studies that compare them are available.
Even as the theories are based more on intuition rather than evidence (Burden & Williams, 1997; Stahl, 1999), the concept is so appealing that even experienced teachers seem to advocate it (Marcy, 2001; Sprenger, 2008). In addition, there is at present no unified theory that academics can follow, as there are numerous inventories and scales that render proper research or application burdensome (Cassidy; 2004).
The concept is also difficult to realise in practice, as it is quite unmanageable for teachers to cater to all style dimensions and offer a range of teaching methods to encompass all the differences (Stahl, 1999), a point which is acknowledged even by authors of such inventories (Fleming, 1992).
2.2 Study Effort
Study effort could be conceptually defined as the degree of engagement learners put into performing academic tasks, and could involve the time, attention and persistence invested when learners are undertaking challenging academic activities (Pintrich, 2004), including the amount of time spent on doing homework (National Centre for Education Statistics, 2001).
Not surprisingly, teachers are often reminded of the importance of study effort and are trained to encourage, reward (e.g., praise) and sustain student efforts (Haynes, 2010; Petty, 2009;
James et al., 2006). Moreover, some authors take a step further to propose that educators should guide learners to think about how to direct their cognitive efforts when engaging in learning tasks (Goswami, 2008), as retrieval of stored information in the long term memory (LTM) is dependent on the strength of initial storage, which in turn is heavily affected by the mental effort exerted at the time of learning (Kyriacou, 2009).
The effort exercised by students when studying has always been deemed an essential contributor to academic success. Indeed, differentiating between cognitive factors (e.g., intellectual abilities) and non-cognitive factors (e.g., effort), the studies conducted by Robbins et al. (2004) and Richardson et al. (2012) showed that effort was one of the two strongest non- cognitive predictors of academic success (the other being self-efficacy), after controlling for prior academic attainment. A study by Hall and Fife (2017) additionally shows that the time spent accessing online course content was also a strong predictor of educational outcomes.
Taken together, these studies essentially provide support for the traditional assumption that cognitive abilities could be compensated by effort.
While the effort put in by students during the course of study influences how well they learn a subject, the effort put into taking exams also affects how well examinees perform during exams (Schiel, 1996). In fact, assessment scores could potentially underestimate what an examinee really knows or can do, and effort in this case could be seen as a “construct-irrelevant variance”
that could pose a threat to the validity of assessment scores (Wise, 2007).
3. The Present Study
3.1 Research Design and Objectives
This is a correlational study where Chinese high school students are recruited to complete an online questionnaire (using Qualtrics) with 3 primary sections to: (a) collect their middle school final English exam score, (b) measure the degree of their learning preferences based on the VAK model, and (c) measure the amount of effort they put in studying the English language subject during middle school (i.e., the last school they attended before entering the current high school).
The main objectives are: (a) to find out if learning preferences could predict academic performance, if these preferences were to be treated as a range instead of a single type, (b) to find out if certain people are indeed endowed with unique perceptual strengths suitable for studying a foreign language (i.e., English), and (c) if this was the case, could others achieve the same level of competence by simply working harder?
3.2 Research Questions
So far, there have been inadequate studies that attempt to investigate the relationships between learning styles, effort (as a compensatory factor) and language proficiency. This study thus seeks to fill the research gap by answering these research questions:
RQ1: Do learning styles predict English exam performance in Chinese Middle Schools?
RQ2: Do learning styles and study effort co-predict English exam performance in Chinese Middle Schools?
4. Research Methodology
4.1 Background
The study was conducted in a public high school in eastern China as the researcher was a staff member in the school, and had support from the administration.
4.2 Participants
A total of 100 Chinese high school students from two classes (50 per class) were recruited for this study. All participants were above sixteen years old during the study, were from different
middle schools and would have studied the English language for 6 years before entering this high school.
The main reason for selecting this particular group was because they were current year 1 students in the high school, hence the exam scores collected would be the latest summative assessment score from a high-stakes major exam (middle school final exams) that could be used as a variable in the study; whereas assessment scores during their current high school phase would mainly be informal formative assessments that are less indicative of their linguistic competencies.
4.3 Instruments
4.3.1 English Language Proficiency at Middle School
English language proficiency was defined by the scores obtained on the middle school final English language exam (i.e., existing scores which are considered public information in a school setting). The study was designed to be minimally intrusive, as obtaining available scores would be less intrusive and easier as compared to conducting a special test for the purpose of the study. These are hence scores from the Chinese middle school English final exam, designed as a single question incorporated in the questionnaire.
4.3.2 Learning Styles
This section serves to determine the learning preferences of the individuals according to the VAK descriptions. The participants will not be categorised into any particular “dominant” type;
instead, the individuals are viewed as having varying degrees of preference for each modality.
The design of this questionnaire was influenced by the works of Sprenger (2008), who proposed a simple form to determine the three learning styles. In addition, prevalent descriptions by proponents of the meshing hypothesis were collected from numerous sources (e.g., articles, books) and added to the list according to the three categories.
The descriptions were then synthesized and reworded to form new statements that best reflected the original content, and further organised according to common language lesson stages (e.g., input stage, when receiving Instructions). Items without direct references (e.g., “receiving Instructions” for kinaesthetic learners) were interpreted and improvised. The completed form is a 10-item questionnaire, each with three statements where participants have to indicate the degree of their agreement for each statement (i.e., “A”, “B” and “C”), on a scale from 1 (strongly disagree) to 5 (strongly agree). Upon completion, the scores for each learning preference (for each question) would be totalled up, and each participant would thus present a
“degree of preference” for each style.
To help the participants with weaker English respond more accurately, the items were translated into Chinese, with the final questionnaire displaying both languages.
4.3.3 Study Effort
The questions in this section measure the amount of effort (in terms of time, effort, ease) put into studying the English language subject in middle school, on a scale from 1 (strongly disagree) to 5 (strongly agree). With reference to Butler’s (2007) school effort scale (a 9-item questionnaire), a scale consisting of four items was developed to measure effort. The number of questions was kept minimal in relation to the learning styles section to keep the overall participation time short, and the questions were positively worded to prevent invoking any negative feelings.
For the purpose of this study, effort was considered as a general factor, with no further elaborations – macro and micro factors which might affect academic persistence, like socioeconomic status, or time spent on doing homework were not considered. Also, effort was not broken down further to measure specific tasks (e.g., drilling, reading), as this would make the questionnaire unnecessarily tedious. Hence, the amount of relaxation felt when studying English, time invested, general perceptions about effort involved, and external help needed were collectively interpreted as “effort” in this study.
5. Procedure
5.1 Recruitment and Ethical Considerations
The participants were briefed on the voluntary nature of participation, non-relation to their school performance, and also the various withdrawal mechanisms. Permission to conduct the study was obtained from the school principal, informed consent was obtained from the participants, and ethics permission was granted.
5.2 Administration
Over the course of three days, the participants approach the researcher’s office in pairs during break times to complete the online questionnaire, using laptops provided by the office. At the start of session, the participants were reminded that the questions were directed at their experiences in middle school. Each pair took around six minutes each to complete the questionnaire.
6. Results
Simple linear regression analyses were conducted to see if higher scores on each of the learning preferences in general (i.e., the composite scores of all items measuring each style) correlate with, and are able to predict higher scores on the English exam. The raw effort scores were reversed during analysis, as higher scores meant higher agreement with the positively-worded statements (hence less effort). Multiple linear regression analyses were then conducted to investigate if each general perceptual preference, when coupled with effort, could co-predict higher exam scores.
For assumptions testing, (i) scatterplots showed that the relationship between each pair of IV and DV were linear, (ii) analysis of collinearity statistics showed that there were no multicollinearity in the data (all VIF scores were below 2, and tolerance scores above 0.64), (iii) the values of the residuals were independent (Durbin-Watson = 1.949), (iv) the assumption of homoscedasticity was met (no signs of funnelling in the plots), (v) the P-P plots for the models suggested that the values of the residuals were normally distributed, and (vi) there were no influential cases biasing the models (Cook’s Distance values were all under 1).
The analyses were performed using SPSS version 24.
6.1 Learning Preferences vs English Scores
Figure 1: Scatterplot of Visual Preference vs Exam Scores
Simple regression analyses showed that: (i) there was a moderate positive correlation between visual preference and exam scores (r = .38, p < .001); overall model fit was significant, F(1,98) = 16.93, p < .001 (see Figure 1); visual preference explained 15% of the variance in exam scores (R2 = .15), and is a good predictor of exam scores, t(98) = 4.12, p <
.001.
Figure 2: Scatterplot of Auditory Preference vs Exam Scores
(ii) there was a weak positive correlation between auditory preference and exam scores (r = .28, p < .005); overall model fit was significant, F(1,98) = 8.52, p < .005 (see Figure 2); auditory preference explained 8% of the variance in exam scores (R2 = .08), and is a modest predictor of exam scores, t(98) = 2.92, p < .005.
Figure 3: Scatterplot of Kinaesthetic Preference vs Exam Scores
and (iii) there was a weak positive correlation between kinaesthetic preference and exam scores (r = .18, p = .079); overall model fit was not significant, F(1,98) = 3.15, p = .08 (see Figure 3); kinaesthetic preference explained 3% of the variance in exam scores (R2 = .03), and is not a good predictor of exam scores, t(98) = 1.77, p = .08.
6.2 Learning Preferences and Effort vs English Scores
Multiple regression analysis showed that: (i) the overall model fit for visual preference and effort was significant, F(1,97) = 10.20, p < .001, and the combination explained 17% of the variance in exam scores (R2 = .17). Visual preference is a good predictor of exam scores, t(97) = 3.57, p < .001, though effort does not predict exam scores when visual preference is controlled for, t(97) = -1.76, p = .08.
(ii) the overall model fit for auditory preference and effort was significant, F(1,97) = 6.38, p <
.005, and the combination explained 12% of the variance in exam scores (R2 = .12). Auditory preference is a modest predictor of exam scores, t(97) = 2.36, p < .05, and effort mildly predicts exam scores when auditory preference is controlled for, t(97) = -1.99, p < .05.
(iii) the overall model fit for kinaesthetic preference and effort was significant, F(1,97) = 4.75, p < .05, and the combination explained 9% of the variance in exam scores (R2 = .89).Kinaesthetic preference is not a good predictor of exam scores, t(97) = 1.58, p = .12, though effort modestly predicts exam scores when kinaesthetic preference is controlled for, t(97) = -2.49, p < .05.
7. Discussion
7.1 Answering the Research Questions
RQ1: Do learning styles predict English exam performance in Chinese Middle Schools?
Participants who reported higher agreement for visual and auditory-related items presented higher correlations on English scores as compared to kinaesthetic learners, with both variables demonstrating stronger predictive powers. This could suggest that students who prefer
visual and auditory-related learning strategies and possess stronger associated predispositions might find it easier to perform on the English test. On the other hand, kinaesthetic preference was not a predictor, and only displayed a weak correlation. This could suggest that having higher preferences for kinaesthetic-related activities, or having such predispositions, while cannot be conclusively deemed a disadvantage, may not be as helpful as visual and auditory preferences, at least in the case of language learning.
RQ2: Do learning styles and study effort co-predict English exam performance in Chinese Middle Schools?
Noting that higher scores on kinaesthetic-related items did not predict exam scores, the results showed that when both kinaesthetic preference and effort were combined, only effort could predict higher exam scores. In contrast, effort was a weak predictor when combined with auditory preference, and not a predictor when combined with visual responses. Therefore, this could pose a possibility that learners who prefer kinaesthetic-related strategies, or have this predisposition could possibly compensate by putting in extra effort in attaining higher scores in English language tests, also implying that individuals with higher kinaesthetic tendencies might have to work harder than people with other preferences. It could also suggest that students who veer towards kinaesthetic type activities are the “active types” who may be less inclined to learn effectively from traditional “lecture style” lessons.
In general, the trend also showed that effort becomes a better predictor as perceptual strength falls, further hinting at a possibility of compensatory strategies supressing the effects (and discovery of the effects) of learning preferences. Nevertheless, additional studies have to be done to confirm this presumption.
7.2 Limitations of the Study
7.2.1 Validity and Reliability of Instruments
Though the instruments had face validity, the question items may lack construct validity. The problem with categorising these preference-specific activities was in reconciling the various definitions based on superficial descriptions by various authors - whether it is the understanding of the learning preferences - for example, Gregory’s (2002) definition of a “visual learner”
might be different from Kinsella’s (1993) interpretation; or whether it is the interpretation of the terms and descriptions, for example, “The visual learner likes to learn procedures by seeing them in print or pictures” (Sprenger, 2008) might have different implications from what Lim (2002) is trying to communicate: “Visual learners relate most effectively to visual displays like written information, notes, diagrams and pictures”. However, even with the potential problems, it was still necessary to operationalise the statements for close scrutiny; otherwise they would remain descriptive and impractical.
With regard to reliability, it is not known if the same participants would give similar responses if the same (or comparable) questionnaire were to be administered after an extended period, and hence may present test-retest concerns.
7.2.2 Problems with Self-Reporting
As the questionnaire is a self-reporting tool, the data could be inaccurate due to biases (Langdridge, 2004). Moreover, though they were reminded verbally and in printed form (i.e., as part of the questions) that they were answering based on what happened during middle school (as they have left middle school for at least 9 months at the time of the study), the students could simply have forgotten about how they felt (especially when trying to recall how
much effort they exerted), and what strategies they used (pertaining to learning preferences) during middle school.
8. Conclusion
This study primarily seeks to investigate the learning styles hypothesis as applied to language teaching, and the compensatory effects of efforts exerted when studying the subject.
Preferences for kinaesthetic items were shown to be a poor predictor of exam scores, unlike visual and auditory preferences. With regard to the main research question on whether there were any compensatory effects, the results convincingly but inconclusively revealed that effort played a more significant role in predicting exam scores when combined with kinaesthetic scores, as compared to visual and auditory preferences.
Nonetheless, no compelling or definitive findings emerged from the study and academics interested in the learning style concept are advised carry out further studies to gather empirical data, before recommending any techniques for teachers, especially after the concept have already been increasingly disregarded.
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