How Common Is Belief in the Learning Styles Neuromyth, and Does It Matter? A Pragmatic Systematic Review

The review followed the PRISMA guidelines for conducting and reporting a Systematic Review (Moher et al., 2009), with a consideration of measures of quality and reporting for survey-based research, taken from (Kelley et al., 2003; Bennett et al., 2011).

Eligibility Criteria, Information Sources, and Search Strategy

Education research is often published in journals that are outside the immediate field of education, but instead are linked to the subject being learned. Therefore, we used EBSCO to search the following databases: CINAHL Plus with Full Text; eBook Collection (EBSCOhost); Library, Information Science & Technology Abstracts; MEDLINE; APA PsycArticles; APA PsycINFO; Regional Business News; SPORTDiscus with Full Text; Teacher Reference Center; MathSciNet via EBSCOhost; MLA Directory of Periodicals; MLA International Bibliography. We also searched PubMed and the Education Research database ERIC.

The following search terms were used: “belief in learning styles”; “believe in learning styles”; “believed in learning styles”; “Individuals learn better when they receive information in their preferred learning style” (this is the survey question used in the original Howard-Jones paper (Howard-Jones et al., 2009). Neuromyth*; “learning styles” AND myth AND survey or questionnaire. We used advanced search settings for all sources to apply related words and to ensure that the searches looked for the terms within the full text of the articles. No date restriction was applied to the searches and so the results included items up to and including April 2020.

This returned 1,153 items. Exclusion of duplicates left 838 items. These were then screened according to the inclusion criteria (below). Screening articles on the basis of their titles identified 85 eligible items. The abstracts of these were then evaluated which resulted in 46 items for full-text screening. We also used Google Scholar to search for the same terms. Google Scholar provides better inclusion of non-journal research including of gray literature (Haddaway et al., 2015) and unpublished theses that are hosted on servers outside the normal databases (Jamali and Nabavi, 2015). For example, when searching for the specific survey item used in the original Howard-Jones paper (Howard-Jones et al., 2009) and in many studies subsequently; “Individuals learn better when they receive information in their preferred learning style.” This search returned zero results on ERIC and four result on PsychINFO, but returned 107 results on Google Scholar, most of which were relevant. However, all Google Scholar results had to hand screened in real-time since Google Scholar does not have the same functionality as the databases described above; it includes multiple versions of the same papers, and the search interface is limited, making it difficult to accurately quantify and report search results (Boeker et al., 2013).

Study Selection

To be included in the review a study had to meet the following criteria;

• Survey educators about their belief in the matching of instruction to one or more of the Learning Styles classifications identified in aforementioned reviews (Coffield et al., 2004; Pashler et al., 2008) and/or educators use of that matching in their teaching. This included pre-service or trainee teachers (individuals studying toward a teaching qualification).

• Report sufficient data to allow calculation of the number and percentage of respondents stating a belief that individuals learn better when they receive information in their preferred learning style (or use/plan to use Learning Styles theory in this way).

Exclusion criteria included the following

• Surveys of participant groups that were not educators or trainee educators.

• Only survey belief in individual learning preferences (i.e., rather than matching instruction).

• Survey other opinions about Learning Styles, for example whether they explain differences in academic abilities (e.g., Bellert and Graham, 2013).

• Survey belief in personalizing learning to suit preferences or other characteristics not included in the Learning Styles literature (e.g., prior educational achievement, “deep, surface or strategic learners.”

Some studies were not explicitly clear that they surveyed belief in matching instruction, but used related non-specific concepts such as the “existence of Learning Styles.” These were excluded unless additional information was available to confirm that the studies specifically surveyed belief in matching instruction to Learning Styles. For example (Grospietsch and Mayer, 2018) reported surveying belief in the existence of Learning Styles. However, the content of this paper discussed knowledge acquisition in the context of matching, and stated that the research instruments was derived from Dekker et al. (2012), and had been used in an additional paper by the same authors (Grospietsch and Mayer, 2019), while a follow-up paper from the same authors described both these earlier papers as surveying belief in matching instruction to Learning Styles (Grospietsch and Mayer, 2020). These two survey studies were therefore included. Another study (Canbulat and Kiriktas, 2017) was not clear and no additional information was available. Two emails were sent to the corresponding author with a request for clarity, but no response was received.

Application of the inclusion criteria resulted in 33 studies being included, containing a total of 37 samples. We then went back to Google Scholar to search within those articles which cited the 33 included studies. No further studies were identified which met the inclusion criteria.

Data Collection Process

Data were independently extracted from every paper by two authors working separately (PN + AS). Extracted data were then compared and any discrepancies resolved through discussion.

Data Items

The following metrics were collected where available (all data are shown in Appendix 1):

• The year the study was published

• Year that data were collected (where stated, and if different from publication date. If a range was stated, then the year which occupied the majority of the range was taken (e.g., Aug 2014–April 2015 was recorded as 2014).

• Country where the research was undertaken

• Publication type (peer reviewed journal, thesis, gray literature)

• Population type (e.g., academics in HE, teachers, etc.)

• Whether or not funding was received and if so where from

• Whether or not a Conflict of Interest was reported/detected

• Target population size

• Sample size

• “N” (completed returns)

• Average teaching experience of participant group

• Percentage and number of participants who stated agreement with a question regarding belief in the matching of instruction to Learning Styles, and the text of the specific question asked

• Percentage and number of participants who stated agreement with a question regarding belief that learners express preferences for how they receive information, and the text of the specific question asked

• The percentage and number of participants who stated that they did, or would, use matching to instruction in their teaching, and the text of the specific question asked

• The percentage and number of participants who stated agreement with a question regarding belief in the matching of instruction to Learning Styles after any intervention aimed at helping participants understand the lack of evidence for matching instruction to Learning Styles

Summary Measures and Synthesis of Results

Most measures are simple percentages of participants who agreed, or not, with questionnaire statements. Summary measures are then the average of these. In order to account for unequal sample size, simple weighted percentages were calculated; percentages were converted to raw numbers using the stated “N” for an individual sample. The sum of these raw numbers from each study was then divided by the sum of “N” from each study and converted to a percentage. Percentages from individual studies were used as individual data points in groups for subsequent statistical analysis, for example to compare the percentage of participants who believed in matching instruction to the percentage who actually used Learning Styles in this way.

Risk of Bias Within and Across Studies

Bias is defined as anything which leads a review to “over-estimate or under-estimate the true intervention effect” (Boutron et al., 2019). In this case an “intervention effect” would be belief in, or use of, Learning Styles either before or after any intervention, or belief in a preference for receiving information in different ways.

Many concerns regarding bias are unlikely to apply here. For example, publication bias, wherein results are less likely to be reported if they are not statistically significant. Most of the data reported in the studies under consideration here are not subject to tests of significance, so this is less of a concern.

However, a number of other factors affect can generate bias within a questionnaire-type study of the type analyzed here. These factors also affect the external validity of study findings, i.e., how likely is it that study findings can be generalized to other populations. We collected the following information from each study in order to assess the external validity of the studies. These metrics were derived from multiple sources (Kelley et al., 2003; Bennett et al., 2011; Boutron et al., 2019). Some were calculated from the objective data described above, whereas others were subject to judgement by the authors. In the latter case, each author made an independent judgement and then any queries were resolved through discussion.

• Sampling Method. Each study was classified into one of the following categories. Categories are drawn from the literature (Kelley et al., 2003) and the studies themselves.

• Validity Measures

Additional Analyses

The following additional analyses were pre-specified in line with our initial research questions.

Has Belief in Matching Instruction to Learning Styles Decreased Over Time?

The lack of evidence to support matching instruction to Learning Styles has been established since the mid-2000s and has been the subject of substantial publicity. We might therefore hypothesize that belief in matching instruction has decreased over time, for example due to the effects of the publicity, and/or from a revision of teacher-training programmes to reflect this evidence. Three different analyses were conducted to test for evidence of a decrease.

• A Spearman Rank Correlation test was conducted to test for a correlation between the year that the study was undertaken and the percentage of participants who reported a belief in matching instruction to learning styles. A significant negative correlation would indicate a decrease over time.

• Belief in matching instruction to Learning Styles was compared in trainee teachers vs. practicing teachers. If belief in Learning Styles was declining then we would expect to see lower rates of belief in trainee teachers. Two samples (Tardif et al., 2015; van Dijk and Lane, 2018) contained a mix of trainee and qualified teachers and were excluded from this analysis. The samples of teachers in Dekker et al. (2012) and Macdonald et al. (2017) both contained 94% practicing teachers and 6% trainee teachers, and so the samples were counted as practicing teachers for the purpose of this analysis.

• A Spearman Rank Correlation test was conducted to test for a correlation between the average teaching experience of study participants and the percentage of participants who reported a belief in matching instruction to Learning Styles. If belief in matching instruction to learning styles is decreasing then we might expect to see a negative correlation.

Is There a Difference Between Belief in Learning Styles and Use of Learning Styles

The weighted percentage for each of these was calculated, and the two groups of responses were also compared.

Question Validity Analysis

In many of the studies here, participants were asked about both “preferences for learning” and “matching instruction to Learning Styles.” As described in the introduction, the wording for both questions was similar. If there was confusion about the difference between these two statements, then we would expect the pattern of response to them to be broadly similar. To test for this, we calculated a difference score for each study by subtracting the percentage of participants who believed in matching instruction to Learning Styles from the percentage who agreed that individuals have preferences for how they learn. We then conducted a one-tailed t-test to determine whether the distribution of these scores was significantly different from zero. We also compared both groups of responses.

Analysis

All datasets were checked for normal distribution before analysis using a Kolmogorov-Smirnov test. Non-parametric tests were used where datasets failed this test. Individual tests are described in the results section.