Edited by: Ana Lucia Pereira, Universidade Estadual de Ponta Grossa, Brazil
Reviewed by: Sonia Brito-Costa, Instituto Politécnico de Coimbra, Portugal; Horace Crogman, California State University, Dominguez Hills, United States
This article was submitted to Educational Psychology, a section of the journal Frontiers in Education
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A well-known hypothesis amongst educators and the general public is that matching instructional method with an individual’s modality-specific learning style improves learning. Several critical reports in the past decade, however, have shown that the psychometric properties of the inventories applied to establish modality-specific learning styles have been poorly validated. Furthermore, theoretical development has challenged the theoretical basis for the modality-specific learning style model. Thus, the aim of the current study was to examine the psychometric properties and relationship between, two modality-specific learning style inventories: the Barsch Learning Style Inventory (BLSI) and the Learning Style Survey (LSS). University students (
In many fields and professions, there has been an ongoing debate regarding the implications for, and effect on, skill learning that emerges from the interaction between individual differences and instructional strategies. For example, the theoretical question of the early 1900s postulated by
The learning style literature in itself also demonstrates substantial heterogeneity in theoretical constructs and approaches. For example, the concept of learning styles has occasionally been considered equal to
The most cited and best-known learning style perspective amongst teachers and educators’ states that individual differences in learning styles represent individual differences in
Positive attitudes toward the modality-specific learning style concept can be found across levels of education and in research approaches. Regarding the latter,
The perspective of modality-specific learning styles, as with other learning-style taxonomies, is in principle a “type” theory, that is, learners must be classified into supposedly distinct groups, thus providing information that is said to be helpful in making instructional decisions. One can trace the lineage of such an approach back to the first modern typological theorizing in the personality field undertaken by the psychiatrist and psychoanalyst C.G. Jung, which was later incorporated into the Myers–Briggs Type Indicator test which remains widely used today in occupational settings (
The psychometric measurement model that can be derived from the modality-specific learning style models appears to be relatively straightforward: Provided a learning-style questionnaire focusing on modality-based preferences, participants will volunteer preferences about their preferred mode of studying and taking in new information. Respondents answer behavioral statements by ranking (often by using a Likert scale) how well the statements describe them and their behavior during a learning situation. Thus, they should answer relatively consistently that they prefer, for instance, visual aids if they are to be classified as having a visual-specific learning style.
Subjected to statistical analysis, the abovementioned data should demonstrate a clear-cut, latent structure with the modality-specific learning styles emerging as non-correlated factors (see
Prevalence of modality-specific and multi-modal learning styles across studies applying the VARK inventory in university students.
Study | Inventory | Population | N | Visual | Auditory | Read/Write | Kinesthetic | Multi-Modal |
VARK | Medical students | 89 | 2% | 26% | 2% | 3% | 67% | |
VARK | Medical students | 155 | 3% | 8% | 2% | 23% | 64% | |
VARK | Physiology students | 213 | 4% | 5% | 15% | 16% | 60% | |
VARK | Physiology students | 64 | 3% | 5% | 14% | 8% | 70% | |
VARK | Physiology students | 90 | 7% | 22% | 20% | 38% | 13% | |
VARK | Medical students | 100 | 7% | 4% | 2% | 26% | 61% | |
VARK | Medical students | 419 | 11% | 18% | 22% | 30% | 18% | |
VARK | Medical students | 166 | 5% | 5% | 8% | 18% | 64% | |
VARK | Medical students | 141 | 1% | 18% | 17% | 6% | 58% | |
VARK | Dental students | 200 | 1% | 9% | 4% | 12% | 76% | |
VARK | Medical students | 121 | 24% | 18% | 2% | 11% | 46% | |
Total | 1758 | 8% | 13% | 13% | 19% | 47% |
Overview of the psychometric measurement model from modality-specific learning style theory.
Previous studies conducted to evaluate the psychometric properties of self-assessment tools based upon different modality-specific learning style models have also generated mixed results.
In addition to the classification scheme and the psychometric assumptions, the modality-specific learning style perspective also seems to reflect a certain viewpoint on human learning. Although this feature has somewhat surprisingly been given far less attention by scholars in the modality-specific learning style field, the significance of the human senses has long been recognized in information-processing approaches toward learning (
The modality-specific learning style perspective has undergone considerable scrutiny during the past two decades, and several reviews have pointed out a general lack of appropriate experimental evidence (
The findings of cross-modal interconnectivity seem to fit well with the notion of human learning emerging alongside dynamic changes in neuronal organization. As the brain forms various networks based on an individual’s experience and development, groups of neuronal connections form a repertoire of behavioral patterns and connect different parts of the brain together (
The perspective of cross-modality, interconnectivity and plasticity of neuronal networks as central features in learning signifies what can be termed a dynamic and non-linear viewpoint on learning. From this perspective, substantial variability, and individuality in how people both approach and respond to learning tasks contributes to context-dependent learning effects (
Based on the presented considerations, which highlighted the modality-specific learning style perspective (meshing hypothesis), the corresponding psychometric measurement model, and theoretical and empirical challenges to the model, we conducted a study of the psychometric properties and relationship between two modality-specific learning style instruments: the Barsch Learning Style Inventory (BLSI) and the Learning Style Survey (LSS). Two research questions were addressed in our study:
Does the latent structure of separate modality-specific learning-style instruments conform to the expected measurement model (see
What is the relationship between modality-specific learning style factors derived from the two different instruments?
Regarding the first question, we conducted confirmatory factor analysis (CFA) and reliability analysis of the two instruments. Specifically, we investigated whether the level of internal consistency (Cronbach’s alpha) is in accordance with established criteria and whether CFA demonstrates appropriate goodness-of-fit with the measurement model derived from modality-specific learning style theory. As to the second research question, we investigated the relationship between factors derived from the two instruments by correlational and exploratory factor analysis (principal component analysis, PCA). The modality-specific learning style perspective predicts that only similar factors should be correlated. For instance, the visual learning style factor derived from either instrument should be highly correlated.
Two hundred and forty-eight students were recruited from a university population (55 males, 192 females; mean [SD] age: 20.9 [2.5]) and responded to a questionnaire during lecture breaks consisting of the BLSI and LSS (see descriptions below). Total time to complete the questionnaires was 15 min. Participants were recruited through advertisement on information boards at campus, and thus represent a convenience sample. Given that there is no consensus regarding absolute sample size requirements for conducting factor analysis, we based our sample size upon an expected level of moderate-to-high communality derived from the expected measurement model, and the number of expected factors (6). In these instances, a
In the translation process, a pilot with 27 participants was conducted to examine whether the translation maintained the essence in the behavioral statements. Both the English and the translated Norwegian formats were handed out and the participants were requested to answer the original English version if they experienced the content of a behavioral statement to be dissimilar. Based on the results and feedback from the pilot, the questionnaires were revised and modified (see below).
The BLSI is a self-assessment tool which provides a score for the modality-specific learning styles, namely visual, auditory and kinesthetic (VAK) (
In the translation process and after conducting the pilot, three of the statements from the BLSI were removed – one statement from each VAK scale – resulting in a total of 21 behavioral statements. The removed statements were considered not to be relevant to a Norwegian context for young adults or lost their meaning in the translation process. The removed statements were:
Just like the BLSI, the LSS is a self-assessment tool, which consists of 11 parts (
The distribution of scores in the dataset was investigated using the Kolmogorov–Smirnov test, histograms, and Q–Q plots. For research question 1, CFA was applied to the data from the university population, through SPSS AMOS, on the three modality-specific learning styles (VAK) originating from each of the two assessments (LSS and BLSI). The expected model for each questionnaire is presented in
The multidimensional hierarchical model was evaluated against various types of overall goodness-of-fit indices for the constructed model: chi-squared (χ2) and normed chi-square (
For research question 2, summarized scores were calculated as a proxy of factor scores (
Research question 1 addresses the extent to which responses from learning style inventories conform to the expected measurement model (
Goodness-of-fit indices for the psychometric measurement model applied to Barsch Learning Style Inventory and learning style survey.
CFI | NFI | RMSEA (90% CI) | |||||
Acceptable fit | <3 | >0.90 | >0.90 | <0.08 | |||
Barsch Learning Style Inventory | 505.82 | <0.05 | 186 | 2.72 | 0.533 | 0.435 | 0.083(0.075–0.92) |
Learning Style Survey | 972.72 | <0.05 | 402 | 2.42 | 0.406 | 0.303 | 0.076(0.070–0.082) |
Descriptive statistics, internal consistency (α), Pearson correlations, and overview of principal component analysis of visual, auditory and kinesthetic learning style factors (
Pearson correlations |
PCA |
||||||||
Learning style | Mean (SD) | α | LSSA | LSSV | BV | BA | BK | ||
LSS – Visual (LSSV) | 22.71(3.52) | 0.42 | 0.09 | 0.07 | 0.58 | 0.34 | |||
LSS – Auditory (LSSA) | 20.75(3.45) | 0.39 | 0.11 | 0.08 | 0.61 | 0.37 | |||
LSS – Kinesthetic (LSSK) | 19.08(4.75) | 0.63 | 0.07 | 0.59 | 0.34 | ||||
BLSI – Visual (BV) | 16.00(1.85) | 0.14 | 0.01 | 0.00 | |||||
BLSI – Auditory (BA) | 14.64(2.69) | 0.62 | 0.66 | 0.43 | |||||
BLSI – Kinesthetic (BK) | 14.68(2.35) | 0.39 | 0.73 | 0.54 | |||||
Eigenvalue: | 2.02 | ||||||||
Total explained variance: | 33.59% |
Research question 2 addresses the relationship between modality-specific learning style factors within and between learning style inventories. Across the sample, Bonferroni correction in independent samples
The observed significant correlations in
The current study had two aims: (1) to examine the degree of fit between the psychometric measurement model originating from modality-specific learning style theory within the BLSI and the LSS and assess the internal consistency, and (2) to examine the relationship within and between modality-specific learning style factors derived from the two instruments. The first aim was carried out by conducting CFA on the responses from the two inventories as well as investigating the internal consistency using Cronbach’s alpha. In the context of the modality-specific learning style theory, participants should demonstrate clear modality-specific preferences for studying and taking in new information. Responses should thus conform to a measurement model with three factors corresponding to the VAK modalities (see
Results from research question 1 showed that reliability estimates for interrelatedness of items obtained across VAK factors appeared to be, by any standard, too small to be interpreted as adequate for any of the two studied inventories. Although the interpretation of Cronbach’s alpha is not a straightforward scientific endeavor (
The CFA on the expected measurement model (
In-depth examination of the inventories included in the current study might provide some explanation for the questionable levels of internal consistency and poor CFA model fit. First, and perhaps foremost, certain behavioral statements seem to be imprecise and ambiguous regarding the modality-specific learning style the item is intended to reflect. For instance, item 4 in the LSS inventory states: “I prefer to learn with TV or video rather than other media” and high agreement on the item is supposed to correspond to a visual learning style. However, this item might just as well reflect that university students prefer simultaneous audio-visual input in various learning contexts. Furthermore, some of the statements are vague about the fact that they do not seem to concern how individuals prefer to obtain and process information through different modalities in a learning context. For example, behavioral statement number 23 in the BLSI reads: “Feel very comfortable touching others, hugging, handshaking, etc.” It is not clear what aspect of modality-specific learning style this, and other items, is supposed to reflect upon. Indeed, ambiguity of behavioral statements has not only been a concern in the BLSI and LSS inventories, similar argumentation has been put forward by
Ambiguous items might lead to large variability in interpretation and invoke confusion among respondents. The lack of clarity might result in more random responses, or answers based on the respondent’s individual heuristic (
Results for research question 2 indicated that most VAK factors demonstrated significant correlations within and between inventories (see correlation matrix reported in
Intercorrelations and shared variance across VAK factors, as observed in the current study, points to a different pattern of responding to inventories compared to predictions from modality-specific learning style theory. Participants are expected to answer behavioral statements by volunteering clear and consistent modality-specific preferences for studying and taking in new information. As this is clearly not the case in the presented data, a tendency among the participants to give answers that lean toward a mean sum score across all three learning styles emerges. Thus, by checking the answer “
Intercorrelations between various scores, as observed between the VAK factors in the current study, are typically explained by positing the existence of an underlying and quantitative latent psychological factor (
The current study has limitations that warrant further investigation. Any psychometric study of various instruments needs to be evaluated against the sample of participants under investigation, as inventories might demonstrate different statistical properties across various populations. Still, one might argue that young adults from a university population represent an appropriate sample for evaluating the psychometric model based upon modality-specific learning style theory. These participants have long experience with various curricula and teaching at many (if not all) educational stages, as well as experience with their current habits for studying. Thus, they have had a substantial amount of teaching-related exposure that potentially has shaped their preferences across various learning contexts. Furthermore, this study does not include any performance- or ability-related assessments that are important for validating the modality-specific learning style hypothesis. This basic question, whether visual learners perform better in visual-processing tasks, has been examined in various other studies which have all indicated no relationship between modality-specific learning style and various task performances (e.g.,
The current study addressed the psychometric model (
All datasets generated for this study are included in the article/supplementary material.
The studies involving human participants were reviewed and approved by the Norwegian Centre for Research Data. The patients/participants provided their written informed consent to participate in this study.
KA, MH, HS, and HL contributed to the conception and design of the study. KA collected the data. KA and HL analyzed the data and wrote the first draft of the manuscript. All authors contributed to manuscript revision and read and approved the submitted version.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.