- Email: [email protected]
Learning preferences of surgery residents: a multi-institutional study
Surgery 163 (2018) 901–905
Contents lists available at ScienceDirect
Surgery j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y m s y
Education Presented at the Academic Surgical Congress 2017
Learning preferences of surgery residents: a multi-institutional study Roger H. Kim a,*, Rebecca K. Viscusi b, Ashley N. Collier c, Marie A. Hunsinger d, Mohsen M. Shabahang d, George M. Fuhrman e, and James R. Korndorffer Jr f a
Department of Surgery, Southern Illinois University School of Medicine, Springfield, IL Department of Surgery, The University of Arizona College of Medicine, Tucson, AZ Department of Surgery, Louisiana State University Health Sciences Center, Sheveport, LA d Department of Surgery, Geisinger Medical Center, Danville, PA e Department of Surgery, Ochsner Clinic Foundation, New Orleans, LA f Department of Surgery, Tulane University School of Medicine, New Orleans, LA b c
A R T I C L E
I N F O
Article history: Accepted 11 October 2017 Available online 13 February 2018
A B S T R A C T
Background. The VARK model categorizes learners by preferences for 4 modalities: visual, aural, read/ write, and kinesthetic. Previous single-institution studies found that VARK preferences are associated with academic performance. This multi-institutional study was conducted to test the hypothesis that the VARK learning preferences of residents differ from the general population and that they are associated with performance on the American Board of Surgery In-Training Examination (ABSITE). Methods. The VARK inventory was administered to residents at 5 general surgery programs. The distribution of the VARK preferences of residents was compared with the general population. ABSITE results were analyzed for associations with VARK preferences. χ2, Analysis of variance, and multiple linear regression were used for statistical analysis. Results. A total of 132 residents completed the VARK inventory. The distribution of the VARK preferences of residents was different than the general population (P < .001). The number of aural responses on the VARK inventory was an independent predictor of ABSITE percentile rank (P = .03), percent of questions correct (P = .01), and standard score (P = .01). Conclusion. This study represents the first multi-institutional study to examine VARK preferences among surgery residents. The distribution of preferences among residents was different than that of the general population. Residents with a greater number of aural responses on VARK had greater ABSITE scores. The VARK model may have potential to improve learning efficiency among residents. © 2017 Elsevier Inc. All rights reserved.
A common experience of surgical educators is an unsuccessful attempt at communicating a concept or transferring knowledge to a trainee. One possible explanation for this type of experience comes from the theory of learning styles, which is based on the premise that learners have distinct preferences for the manner in which they assimilate new information and knowledge.1 A mismatch between a preferred learning style of a trainee and the teaching method of the instructor can serve as a barrier to learning, analogous to trying to fit the proverbial square peg into a round hole.2 This type of mismatch may be the cause for these failed attempts at transfer of knowledge in surgical education and can be responsible for substantial frustration for both the teacher and the learner.
Presented at the 12th Annual Academic Surgical Congress in Las Vegas, NV, February 7–9, 2017. * Corresponding author. Department of Surgery, Southern Illinois University School of Medicine, 701 N. First St, P.O. Box 19638, Springfield, IL 62794-9638. E-mail address: [email protected] (R.H. Kim). https://doi.org/10.1016/j.surg.2017.10.031 0039-6060/© 2017 Elsevier Inc. All rights reserved.
The VARK model developed by Neil Fleming groups learners based on their preferences for sensory modalities: visual (V), aural (A), read/ write (R), and kinesthetic (K), or as multimodal (MM).3 The validity of the VARK model has been reported across a diverse spectrum of learners.4-10 The VARK learning preferences have been examined previously among surgery residents and applicants interviewing for general surgery residency; these studies found that these groups have different distribution of VARK preferences than that of the general public.11,12 In addition, performance on standardized tests, such as the United States Medical Licensing Examination (USMLE) and the American Board of Surgery In-Training Examination (ABSITE), has been found to be associated with the VARK learning preferences.12,13 These studies, however, were conducted in single-institution settings; it is unclear if the findings from these studies can be generalized. To address this issue, investigators from 5 institutions across the United States partnered to form the VARK Collaborative Research Group with the goal of conducting multi-institutional research on VARK preferences in various learner populations. In this study, we, the VARK Collaborative Research Group, report the findings of a
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multi-institutional investigation of VARK learning preferences among general surgery residents in our institutions: Louisiana State University Health Sciences Center—Shreveport, The University of Arizona College of Medicine, Geisinger Medical Center, Ochsner Clinic Foundation, and Tulane University School of Medicine. We hypothesized that the learning style preferences of general surgery residents differ from that of the general population and that ABSITE performance is associated with VARK learning style preferences. Methods The study protocol was reviewed and received exempt status from the Institutional Review Board of the Louisiana State University Health Sciences Center—Shreveport. The Fleming VARK learning styles inventory (http://www.vark-learn.com/) was administered during the 2014–2015 academic year to residents at 5 general surgery residency programs: 3 university hospital-based and 2 independent programs. The VARK inventory consisted of 16 multiple choice questions, each with 4 possible responses. The instructions for adminstration of the VARK inventory involved instructing residents to choose 1, more than 1, or none of the 4 possible responses to each question. Responses to the inventory were scored to determine the learning style preferences for each resident. All VARK inventories were scored centrally by VARK Learn, Ltd (Christchurch, New Zealand). Residents were classified as having a dominant unimodal preference for visual (V), aural (A), read/write (R), or kinesthetic (K) learning styles or as having a multimodal preference (MM). The MM category encompasses all possible combinations of 2, 3, or 4 of the sensory modalities. Resident performance on the USMLE Step 1 and Step 2 Clinical Knowledge (CK) and ABSITE were collected. Residents with missing data for any individual examination were excluded from the respective analyses. We collected basic demographic data, including gender, categorical versus preliminary residency status, type of residency program, and postgraduate year (PGY) level. All participant data were collected in a de-identified fashion by using identifiers provided by the individual residency programs to link VARK responses to the standardized test performances and other collected data. Statistical analysis was performed using SPSS Version 21 (IBM Corp, Armonk, NY). χ2 Analysis, analysis of variance, and multiple linear regression were performed for statistical analysis.
Table 1 Demographic data of study participants (n = 132). Characteristic
Residents n (%)
Gender Male Female Program type University hospital based Independent Resident status Categorical Preliminary PGY level PGY-1 PGY-2 PGY-3 PGY-4 PGY-5
87 (66%) 45 (34%) 81 (61%) 51 (39%) 103 (78%) 29 (22%) 48 (36%) 25 (19%) 20 (15%) 21 (16%) 18 (14%)
shown in Table 2. The VARK distribution of residents was different from that of the general population (P = .0007), with the greatest difference being in the proportion of unimodal R respondents (5.3% of residents vs 10.2% of general population). There were no differences in VARK distribution when analyzed by gender (P = .70) or by residency program (P = .52). The mean percentile rank scores of the ABSITE, raw percent correct, and standard scores by learning style preference are shown
Results Across the 5 participating general surgery programs, there were a total of 160 residents enrolled at the time of the study. Of these, 132 residents completed the VARK inventory for an overall response rate of 82.5%. Response rates at individual programs ranged from 78% to 92%. The demographic characteristics of the study participants are listed in Table 1. Sixty-six percent of participants were male. Sixty-one percent of participants were at university hospital– based programs and 39% were at independent programs. Seventyeight percent were categorical residents. Thirty-six percent of residents were in the PGY-1 level, with roughly equal proportions in the remaining years. The distribution of learning style preferences according to the VARK inventory among residents is shown in Fig 1. The majority of residents had a multimodal (MM) preference (61%). Residents with dominant kinesthetic (K) preferences represented the greatest proportion among those with unimodal preferences (17%). Read/ write (R) preferences was the smallest group at 5%. The distribution of learning style preferences of residents and that reported for the general population of VARK respondents (data from VARK website, http://vark-learn.com/introduction-to-vark/research-statistics/) are
Fig. 1. Distribution of VARK learning style preferences among residents.
Table 2 Distribution of VARK learning style preferences for residents versus general population of VARK respondents. VARK preference
Residents
General population
P
V A R K VA VR VK AR AK RK VAR VAK ARK VRK VARK
6.8% 9.8% 5.3% 16.7% 1.5% 5.3% 2.3% 1.5% 3.0% 2.3% 0.8% 8.3% 3.8% 0.8% 31.8%
3.9% 7.5% 10.2% 14.8% 0.7% 1.2% 3.0% 2.1% 5.8% 2.5% 0.9% 4.2% 4.9% 2.5% 35.7%
.0007
R.H. Kim et al. / Surgery 163 (2018) 901–905
903
Fig. 2. Mean ABSITE percentile rank scores by VARK learning style preference.
Fig. 4. Mean ABSITE standard scores by VARK learning style preference.
in Figs 2, 3, and 4, respectively. There were no differences in scores based on overall VARK preference for ABSITE percentile rank (P = .31), raw percent correct (P = .60), or standard scores (P = .57). Multiple linear regression analysis was performed to determine independent predictors of ABSITE performance. These results for ABSITE percentile rank, raw percent correct, and standard scores are shown in Tables 3, 4, and 5, respectively. The number of aural responses given on the VARK inventory was an independent predictor for ABSITE percentile rank (P = .03), raw percent correct (P = .01), and standard scores (P = .01). PGY level was also an independent predictor for ABSITE raw percent correct (P = .006) and standard scores (P = .004).
of our study of VARK learning style preferences in general surgery residents, a multi-institutional study examining this learner population across 5 general surgery residency programs. Medical students and surgery residents have different patterns of learning style preferences than that reported for the general population.12-15 The present study confirms this finding with residents having a greater proportion of unimodal preferences. In particular, residents were found to have a smaller proportion of read/ write preferences with about half as many as would be expected for the general population. This finding points to the possibility of a mismatch between the learning preferences of many surgery trainees and the traditional teaching environment of general surgery residency programs, in which residents are expected to be selfdirected learners and derive much of their knowledge base from
Discussion Although learning style preference have been examined previously among different groups within medical education, these studies have been conducted in single-institution settings, and it is unclear if the findings can be generalized. The VARK Collaborative Research Group conducted the first multi-institutional study previously examining VARK preferences among applicants interviewing for general surgery residency. We now report the findings
Table 3 Multiple linear regression analysis of variables influencing ABSITE percentile rank scores. Variables
Unstandardized coefficients B (SE)
Standardized coefficients β
P value
Residency program Preliminary resident PGY level Number of V responses Number of A responses Number of R responses Number of K responses USMLE Step 1 USMLE Step 2 CK
15.8 (29.2) −6.2 (19.4) −3.7 (4.5) 1.1 (2.2) 5.1 (2.2) −0.3 (2.4) −4.5 (2.4) −0.2 (0.5) 0.5 (0.5)
0.16 −0.08 −0.20 0.14 0.61 −0.04 −0.46 −0.10 0.28
.59 .75 .43 .61 .03 .90 .08 .70 .34
Table 4 Multiple linear regression analysis of variables influencing ABSITE percent correct.
Fig. 3. Mean ABSITE percent correct by VARK learning style preference.
Variables
Unstandardized coefficients B (SE)
Standardized coefficients β
P value
Residency program Preliminary resident PGY level Number of V responses Number of A responses Number of R responses Number of K responses USMLE Step 1 USMLE Step 2 CK
0.7 (5.4) −1.4 (3.5) 2.6 (0.8) 0.4 (0.4) 1.1 (0.4) −0.2 (0.4) −0.8 (0.5) −0.05 (0.1) 0.1 (0.1)
0.03 −0.07 0.54 0.17 0.51 −0.11 −0.30 −0.10 0.31
.90 .70 .006 .40 .01 .60 .10 .60 .14
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Table 5 Multiple linear regression analysis of variables influencing ABSITE standard scores. Variables
Unstandardized coefficients B (SE)
Standardized coefficients β
P value
Residency program Preliminary resident PGY level Number of V responses Number of A responses Number of R responses Number of K responses USMLE Step 1 USMLE Step 2 CK
7.6 (63.0) −19.2 (41.8) 32.6 (9.8) 4.3 (4.8) 13.1 (4.7) −2.6 (5.1) −8.6 (5.4) −0.4 (1.0) 1.7 (1.1)
0.03 −0.08 0.56 0.17 0.50 −0.10 −0.27 −0.07 0.31
.91 .65 .004 .39 .01 .61 .13 .71 .13
textbooks. Therefore, residency programs may educate primarily toward a read/write modality that only a minority of surgery residents actual prefer to learn in. Although multimodal preferring residents may be able to adjust to this teaching method, residents who have other unimodal preferences exclusive of the read/write modality may be at a disadvantage. This mismatch may explain the difficulty that some residents have in achieving the necessary milestones during their training, particularly as it relates to underperformance on the ABSITE. Tailoring the curriculum to address this mismatch could allow for underperforming residents to catch up to their peers. Blended learning environments in which multiple sensory modalities are incorporated into teaching have been found to increase learner satisfaction and may result in improved efficiency.6,9,10 Although there were no associations between overall VARK preference and ABSITE performance noted in this study, the number of aural responses to the VARK inventory was found to be an independent predictor of greater ABSITE scores. A similar association was found among resident interviewees: aural dominant learners had greater scores on the USMLE.12 Given that much of medical education still relies on didactic lectures,16 it is possible that residents who tend to favor aural learning are at a slight advantage in this type of learning environment. The observation that PGY level was an independent predictor for ABSITE percent correct and standard scores inherently makes sense, because these scores are not derived from comparison within PGY levels as is the case for ABSITE percentile rank scores. These findings serve as a form of quality control in this study; if we had not found PGY level to be a predictor of ABSITE percent correct and standard scores, it would have called into question the validity of our data. The absence of USMLE scores as an independent predictor of ABSITE performance is a finding that also deserves some discussion. Although conventional “wisdom” holds that USMLE scores predict ABSITE performance, the existing literature on this relationship have produced mixed results.17,18 The results of this study seem to indicate that the relationship between USMLE and ABSITE scores are largely dependent on VARK responses. One can speculate that preferences in VARK learning style may serve as a surrogate marker for test-taking ability and therefore might predict both USMLE and ABSITE performance. There are some limitations inherent to this study. In spite of its multi-institutional setting, the sample size was still relatively small (n = 132) compared with the total number of general surgery residents in the United States; however, both university hospital– based and independent programs were included, and these programs were of different sizes and geographic locations. As such, we believe that we achieved a representative sample of general surgery residents as a whole. These findings, however, are probably not generalizable to nonsurgical specialties or even other surgical subspecialties, such as urology or neurosurgery. Because this was a retrospective investigation, the relationship between VARK
learning style preferences and ABSITE performance cannot be established as causality but rather only as an association. It is also possible that subsets of the multimodal group, such as VR or ARK, may have associations with improved ABSITE performance. Because 11 such subsets of the multimodal category exist, most of which had only a few data points, it was not feasible to perform an analysis of this type in the present study. A substantially larger, multiinstitutional study that includes the majority of US general surgery residency programs might have sufficient statistical power for such a multimodal subgroup analysis. Finally, only ABSITE scores were examined. Other measures of resident performance, such as faculty evaluations or assessment of technical and nontechnical skills, were not evaluated. It is possible that VARK learning preferences may have different associations with these metrics. For example, one could hypothesize that kinesthetic-dominant learners may have improved performance on assessments of technical skills such as the Fundamentals of Laparoscopic Surgery. An investigation into the relationship between VARK preferences and other metrics of resident performance was outside the focus of this study but may need to be explored in future research endeavors. Conclusion In summary, the distribution of VARK learning style preferences among general surgery residents is significantly different from that of the general population. The number of aural responses on the VARK inventory was an independent predictor of greater ABSITE performance. This study adds to the body of evidence suggesting that learning style preferences have the potential to be leveraged to improve learning efficiency among surgery residents. Further investigation into interventions based on VARK learning preferences is warranted. Acknowledgments The VARK questionnaire was used with permission. © Copyright Version 7.3 (2001) held by Neil D. Fleming, Christchurch, New Zealand. The authors thank Mr. Fleming for his assistance in scoring and processing the VARK questionnaire responses. References 1. Pashler H, McDaniel M, Rohrer D, Bjork R. Learning styles: concepts and evidence. Psychol Sci Public Interest 2008;9:105-19. 2. Romanelli F, Bird E, Ryan M. Learning styles: a review of theory, application, and best practices. Am J Pharm Educ 2009;73:9. 3. Fleming ND, Mills C. Not another inventory, rather a catalyst for reflection. In: To Improve the Academy, Vol. 11. 1992:137-55. 4. Alkhasawneh E. Using VARK to assess changes in learning preferences of nursing students at a public university in Jordan: implications for teaching. Nurse Educ Today 2013;33:1546-9. 5. Samarakoon L, Fernando T, Rodrigo C. Learning styles and approaches to learning among medical undergraduates and postgraduates. BMC Med Educ 2013;13: 42. 6. Horton DM, Wiederman SD, Saint DA. Assessment outcome is weakly correlated with lecture attendance: influence of learning style and use of alternative materials. Adv Physiol Educ 2012;36:108-15. 7. Koch J, Salamonson Y, Rolley JX, Davidson PM. Learning preference as a predictor of academic performance in first year accelerated graduate entry nursing students: a prospective follow-up study. Nurse Educ Today 2011;31:6116. 8. Leite WL, Svinicki M, Shi Y. Attempted validation of the scores of the VARK: learning styles inventory with multitrait–multimethod confirmatory factor analysis models. Educ Psychol Meas 2010;70:323-39. 9. Alkhasawneh IM, Mrayyan MT, Docherty C, Alashram S, Yousef HY. Problembased learning (PBL): assessing students’ learning preferences using VARK. Nurse Educ Today 2008;28:572-9. 10. Lujan HL, DiCarlo SE. First-year medical students prefer multiple learning styles. Adv Physiol Educ 2006;30:13-6. 11. Kim RH, Gilbert T, Ristig K, Chu QD. Surgical resident learning styles: faculty and resident accuracy at identification of preferences and impact on ABSITE scores. J Surg Res 2013;184:31-6.
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12. Kim RH, Gilbert T. Learning style preferences of surgical residency applicants. J Surg Res 2015;198:61-5. 13. Kim RH, Gilbert T, Ristig K. The effect of surgical resident learning style preferences on American Board of Surgery in-training examination scores. J Surg Educ 2015;72:726-31. 14. Kim RH, Kurtzman SH, Collier AN, Shabahang MM. The learning preferences of applicants who interview for general surgery residency: a multiinstitutional study. J Surg Educ 2016;73:e136-41. 15. Fleming ND. VARK, a guide to learning styles: research & statistics. 2014. http:// vark-learn.com/introduction-to-vark/research-statistics/. Accessed June 1, 2016.
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16. Derossis AM, Da Rosa D, Schwartz A, Hauge LS, Bordage G. Study habits of surgery residents and performance on American Board of Surgery In-Training examinations. Am J Surg 2004;188:230-6. 17. Spurlock DR, Holden C, Hartranft T. Using United States Medical Licensing Examination(®) (USMLE) examination results to predict later in-training examination performance among general surgery residents. J Surg Educ 2010;67:452-6. 18. Alterman DM, Jones TM, Heidel RE, Daley BJ, Goldman MH. The predictive value of general surgery application data for future resident performance. J Surg Educ 2011;68:513-8.
Contents lists available at ScienceDirect
Surgery j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y m s y
Education Presented at the Academic Surgical Congress 2017
Learning preferences of surgery residents: a multi-institutional study Roger H. Kim a,*, Rebecca K. Viscusi b, Ashley N. Collier c, Marie A. Hunsinger d, Mohsen M. Shabahang d, George M. Fuhrman e, and James R. Korndorffer Jr f a
Department of Surgery, Southern Illinois University School of Medicine, Springfield, IL Department of Surgery, The University of Arizona College of Medicine, Tucson, AZ Department of Surgery, Louisiana State University Health Sciences Center, Sheveport, LA d Department of Surgery, Geisinger Medical Center, Danville, PA e Department of Surgery, Ochsner Clinic Foundation, New Orleans, LA f Department of Surgery, Tulane University School of Medicine, New Orleans, LA b c
A R T I C L E
I N F O
Article history: Accepted 11 October 2017 Available online 13 February 2018
A B S T R A C T
Background. The VARK model categorizes learners by preferences for 4 modalities: visual, aural, read/ write, and kinesthetic. Previous single-institution studies found that VARK preferences are associated with academic performance. This multi-institutional study was conducted to test the hypothesis that the VARK learning preferences of residents differ from the general population and that they are associated with performance on the American Board of Surgery In-Training Examination (ABSITE). Methods. The VARK inventory was administered to residents at 5 general surgery programs. The distribution of the VARK preferences of residents was compared with the general population. ABSITE results were analyzed for associations with VARK preferences. χ2, Analysis of variance, and multiple linear regression were used for statistical analysis. Results. A total of 132 residents completed the VARK inventory. The distribution of the VARK preferences of residents was different than the general population (P < .001). The number of aural responses on the VARK inventory was an independent predictor of ABSITE percentile rank (P = .03), percent of questions correct (P = .01), and standard score (P = .01). Conclusion. This study represents the first multi-institutional study to examine VARK preferences among surgery residents. The distribution of preferences among residents was different than that of the general population. Residents with a greater number of aural responses on VARK had greater ABSITE scores. The VARK model may have potential to improve learning efficiency among residents. © 2017 Elsevier Inc. All rights reserved.
A common experience of surgical educators is an unsuccessful attempt at communicating a concept or transferring knowledge to a trainee. One possible explanation for this type of experience comes from the theory of learning styles, which is based on the premise that learners have distinct preferences for the manner in which they assimilate new information and knowledge.1 A mismatch between a preferred learning style of a trainee and the teaching method of the instructor can serve as a barrier to learning, analogous to trying to fit the proverbial square peg into a round hole.2 This type of mismatch may be the cause for these failed attempts at transfer of knowledge in surgical education and can be responsible for substantial frustration for both the teacher and the learner.
Presented at the 12th Annual Academic Surgical Congress in Las Vegas, NV, February 7–9, 2017. * Corresponding author. Department of Surgery, Southern Illinois University School of Medicine, 701 N. First St, P.O. Box 19638, Springfield, IL 62794-9638. E-mail address: [email protected] (R.H. Kim). https://doi.org/10.1016/j.surg.2017.10.031 0039-6060/© 2017 Elsevier Inc. All rights reserved.
The VARK model developed by Neil Fleming groups learners based on their preferences for sensory modalities: visual (V), aural (A), read/ write (R), and kinesthetic (K), or as multimodal (MM).3 The validity of the VARK model has been reported across a diverse spectrum of learners.4-10 The VARK learning preferences have been examined previously among surgery residents and applicants interviewing for general surgery residency; these studies found that these groups have different distribution of VARK preferences than that of the general public.11,12 In addition, performance on standardized tests, such as the United States Medical Licensing Examination (USMLE) and the American Board of Surgery In-Training Examination (ABSITE), has been found to be associated with the VARK learning preferences.12,13 These studies, however, were conducted in single-institution settings; it is unclear if the findings from these studies can be generalized. To address this issue, investigators from 5 institutions across the United States partnered to form the VARK Collaborative Research Group with the goal of conducting multi-institutional research on VARK preferences in various learner populations. In this study, we, the VARK Collaborative Research Group, report the findings of a
902
R.H. Kim et al. / Surgery 163 (2018) 901–905
multi-institutional investigation of VARK learning preferences among general surgery residents in our institutions: Louisiana State University Health Sciences Center—Shreveport, The University of Arizona College of Medicine, Geisinger Medical Center, Ochsner Clinic Foundation, and Tulane University School of Medicine. We hypothesized that the learning style preferences of general surgery residents differ from that of the general population and that ABSITE performance is associated with VARK learning style preferences. Methods The study protocol was reviewed and received exempt status from the Institutional Review Board of the Louisiana State University Health Sciences Center—Shreveport. The Fleming VARK learning styles inventory (http://www.vark-learn.com/) was administered during the 2014–2015 academic year to residents at 5 general surgery residency programs: 3 university hospital-based and 2 independent programs. The VARK inventory consisted of 16 multiple choice questions, each with 4 possible responses. The instructions for adminstration of the VARK inventory involved instructing residents to choose 1, more than 1, or none of the 4 possible responses to each question. Responses to the inventory were scored to determine the learning style preferences for each resident. All VARK inventories were scored centrally by VARK Learn, Ltd (Christchurch, New Zealand). Residents were classified as having a dominant unimodal preference for visual (V), aural (A), read/write (R), or kinesthetic (K) learning styles or as having a multimodal preference (MM). The MM category encompasses all possible combinations of 2, 3, or 4 of the sensory modalities. Resident performance on the USMLE Step 1 and Step 2 Clinical Knowledge (CK) and ABSITE were collected. Residents with missing data for any individual examination were excluded from the respective analyses. We collected basic demographic data, including gender, categorical versus preliminary residency status, type of residency program, and postgraduate year (PGY) level. All participant data were collected in a de-identified fashion by using identifiers provided by the individual residency programs to link VARK responses to the standardized test performances and other collected data. Statistical analysis was performed using SPSS Version 21 (IBM Corp, Armonk, NY). χ2 Analysis, analysis of variance, and multiple linear regression were performed for statistical analysis.
Table 1 Demographic data of study participants (n = 132). Characteristic
Residents n (%)
Gender Male Female Program type University hospital based Independent Resident status Categorical Preliminary PGY level PGY-1 PGY-2 PGY-3 PGY-4 PGY-5
87 (66%) 45 (34%) 81 (61%) 51 (39%) 103 (78%) 29 (22%) 48 (36%) 25 (19%) 20 (15%) 21 (16%) 18 (14%)
shown in Table 2. The VARK distribution of residents was different from that of the general population (P = .0007), with the greatest difference being in the proportion of unimodal R respondents (5.3% of residents vs 10.2% of general population). There were no differences in VARK distribution when analyzed by gender (P = .70) or by residency program (P = .52). The mean percentile rank scores of the ABSITE, raw percent correct, and standard scores by learning style preference are shown
Results Across the 5 participating general surgery programs, there were a total of 160 residents enrolled at the time of the study. Of these, 132 residents completed the VARK inventory for an overall response rate of 82.5%. Response rates at individual programs ranged from 78% to 92%. The demographic characteristics of the study participants are listed in Table 1. Sixty-six percent of participants were male. Sixty-one percent of participants were at university hospital– based programs and 39% were at independent programs. Seventyeight percent were categorical residents. Thirty-six percent of residents were in the PGY-1 level, with roughly equal proportions in the remaining years. The distribution of learning style preferences according to the VARK inventory among residents is shown in Fig 1. The majority of residents had a multimodal (MM) preference (61%). Residents with dominant kinesthetic (K) preferences represented the greatest proportion among those with unimodal preferences (17%). Read/ write (R) preferences was the smallest group at 5%. The distribution of learning style preferences of residents and that reported for the general population of VARK respondents (data from VARK website, http://vark-learn.com/introduction-to-vark/research-statistics/) are
Fig. 1. Distribution of VARK learning style preferences among residents.
Table 2 Distribution of VARK learning style preferences for residents versus general population of VARK respondents. VARK preference
Residents
General population
P
V A R K VA VR VK AR AK RK VAR VAK ARK VRK VARK
6.8% 9.8% 5.3% 16.7% 1.5% 5.3% 2.3% 1.5% 3.0% 2.3% 0.8% 8.3% 3.8% 0.8% 31.8%
3.9% 7.5% 10.2% 14.8% 0.7% 1.2% 3.0% 2.1% 5.8% 2.5% 0.9% 4.2% 4.9% 2.5% 35.7%
.0007
R.H. Kim et al. / Surgery 163 (2018) 901–905
903
Fig. 2. Mean ABSITE percentile rank scores by VARK learning style preference.
Fig. 4. Mean ABSITE standard scores by VARK learning style preference.
in Figs 2, 3, and 4, respectively. There were no differences in scores based on overall VARK preference for ABSITE percentile rank (P = .31), raw percent correct (P = .60), or standard scores (P = .57). Multiple linear regression analysis was performed to determine independent predictors of ABSITE performance. These results for ABSITE percentile rank, raw percent correct, and standard scores are shown in Tables 3, 4, and 5, respectively. The number of aural responses given on the VARK inventory was an independent predictor for ABSITE percentile rank (P = .03), raw percent correct (P = .01), and standard scores (P = .01). PGY level was also an independent predictor for ABSITE raw percent correct (P = .006) and standard scores (P = .004).
of our study of VARK learning style preferences in general surgery residents, a multi-institutional study examining this learner population across 5 general surgery residency programs. Medical students and surgery residents have different patterns of learning style preferences than that reported for the general population.12-15 The present study confirms this finding with residents having a greater proportion of unimodal preferences. In particular, residents were found to have a smaller proportion of read/ write preferences with about half as many as would be expected for the general population. This finding points to the possibility of a mismatch between the learning preferences of many surgery trainees and the traditional teaching environment of general surgery residency programs, in which residents are expected to be selfdirected learners and derive much of their knowledge base from
Discussion Although learning style preference have been examined previously among different groups within medical education, these studies have been conducted in single-institution settings, and it is unclear if the findings can be generalized. The VARK Collaborative Research Group conducted the first multi-institutional study previously examining VARK preferences among applicants interviewing for general surgery residency. We now report the findings
Table 3 Multiple linear regression analysis of variables influencing ABSITE percentile rank scores. Variables
Unstandardized coefficients B (SE)
Standardized coefficients β
P value
Residency program Preliminary resident PGY level Number of V responses Number of A responses Number of R responses Number of K responses USMLE Step 1 USMLE Step 2 CK
15.8 (29.2) −6.2 (19.4) −3.7 (4.5) 1.1 (2.2) 5.1 (2.2) −0.3 (2.4) −4.5 (2.4) −0.2 (0.5) 0.5 (0.5)
0.16 −0.08 −0.20 0.14 0.61 −0.04 −0.46 −0.10 0.28
.59 .75 .43 .61 .03 .90 .08 .70 .34
Table 4 Multiple linear regression analysis of variables influencing ABSITE percent correct.
Fig. 3. Mean ABSITE percent correct by VARK learning style preference.
Variables
Unstandardized coefficients B (SE)
Standardized coefficients β
P value
Residency program Preliminary resident PGY level Number of V responses Number of A responses Number of R responses Number of K responses USMLE Step 1 USMLE Step 2 CK
0.7 (5.4) −1.4 (3.5) 2.6 (0.8) 0.4 (0.4) 1.1 (0.4) −0.2 (0.4) −0.8 (0.5) −0.05 (0.1) 0.1 (0.1)
0.03 −0.07 0.54 0.17 0.51 −0.11 −0.30 −0.10 0.31
.90 .70 .006 .40 .01 .60 .10 .60 .14
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Table 5 Multiple linear regression analysis of variables influencing ABSITE standard scores. Variables
Unstandardized coefficients B (SE)
Standardized coefficients β
P value
Residency program Preliminary resident PGY level Number of V responses Number of A responses Number of R responses Number of K responses USMLE Step 1 USMLE Step 2 CK
7.6 (63.0) −19.2 (41.8) 32.6 (9.8) 4.3 (4.8) 13.1 (4.7) −2.6 (5.1) −8.6 (5.4) −0.4 (1.0) 1.7 (1.1)
0.03 −0.08 0.56 0.17 0.50 −0.10 −0.27 −0.07 0.31
.91 .65 .004 .39 .01 .61 .13 .71 .13
textbooks. Therefore, residency programs may educate primarily toward a read/write modality that only a minority of surgery residents actual prefer to learn in. Although multimodal preferring residents may be able to adjust to this teaching method, residents who have other unimodal preferences exclusive of the read/write modality may be at a disadvantage. This mismatch may explain the difficulty that some residents have in achieving the necessary milestones during their training, particularly as it relates to underperformance on the ABSITE. Tailoring the curriculum to address this mismatch could allow for underperforming residents to catch up to their peers. Blended learning environments in which multiple sensory modalities are incorporated into teaching have been found to increase learner satisfaction and may result in improved efficiency.6,9,10 Although there were no associations between overall VARK preference and ABSITE performance noted in this study, the number of aural responses to the VARK inventory was found to be an independent predictor of greater ABSITE scores. A similar association was found among resident interviewees: aural dominant learners had greater scores on the USMLE.12 Given that much of medical education still relies on didactic lectures,16 it is possible that residents who tend to favor aural learning are at a slight advantage in this type of learning environment. The observation that PGY level was an independent predictor for ABSITE percent correct and standard scores inherently makes sense, because these scores are not derived from comparison within PGY levels as is the case for ABSITE percentile rank scores. These findings serve as a form of quality control in this study; if we had not found PGY level to be a predictor of ABSITE percent correct and standard scores, it would have called into question the validity of our data. The absence of USMLE scores as an independent predictor of ABSITE performance is a finding that also deserves some discussion. Although conventional “wisdom” holds that USMLE scores predict ABSITE performance, the existing literature on this relationship have produced mixed results.17,18 The results of this study seem to indicate that the relationship between USMLE and ABSITE scores are largely dependent on VARK responses. One can speculate that preferences in VARK learning style may serve as a surrogate marker for test-taking ability and therefore might predict both USMLE and ABSITE performance. There are some limitations inherent to this study. In spite of its multi-institutional setting, the sample size was still relatively small (n = 132) compared with the total number of general surgery residents in the United States; however, both university hospital– based and independent programs were included, and these programs were of different sizes and geographic locations. As such, we believe that we achieved a representative sample of general surgery residents as a whole. These findings, however, are probably not generalizable to nonsurgical specialties or even other surgical subspecialties, such as urology or neurosurgery. Because this was a retrospective investigation, the relationship between VARK
learning style preferences and ABSITE performance cannot be established as causality but rather only as an association. It is also possible that subsets of the multimodal group, such as VR or ARK, may have associations with improved ABSITE performance. Because 11 such subsets of the multimodal category exist, most of which had only a few data points, it was not feasible to perform an analysis of this type in the present study. A substantially larger, multiinstitutional study that includes the majority of US general surgery residency programs might have sufficient statistical power for such a multimodal subgroup analysis. Finally, only ABSITE scores were examined. Other measures of resident performance, such as faculty evaluations or assessment of technical and nontechnical skills, were not evaluated. It is possible that VARK learning preferences may have different associations with these metrics. For example, one could hypothesize that kinesthetic-dominant learners may have improved performance on assessments of technical skills such as the Fundamentals of Laparoscopic Surgery. An investigation into the relationship between VARK preferences and other metrics of resident performance was outside the focus of this study but may need to be explored in future research endeavors. Conclusion In summary, the distribution of VARK learning style preferences among general surgery residents is significantly different from that of the general population. The number of aural responses on the VARK inventory was an independent predictor of greater ABSITE performance. This study adds to the body of evidence suggesting that learning style preferences have the potential to be leveraged to improve learning efficiency among surgery residents. Further investigation into interventions based on VARK learning preferences is warranted. Acknowledgments The VARK questionnaire was used with permission. © Copyright Version 7.3 (2001) held by Neil D. Fleming, Christchurch, New Zealand. The authors thank Mr. Fleming for his assistance in scoring and processing the VARK questionnaire responses. References 1. Pashler H, McDaniel M, Rohrer D, Bjork R. Learning styles: concepts and evidence. Psychol Sci Public Interest 2008;9:105-19. 2. Romanelli F, Bird E, Ryan M. Learning styles: a review of theory, application, and best practices. Am J Pharm Educ 2009;73:9. 3. Fleming ND, Mills C. Not another inventory, rather a catalyst for reflection. In: To Improve the Academy, Vol. 11. 1992:137-55. 4. Alkhasawneh E. Using VARK to assess changes in learning preferences of nursing students at a public university in Jordan: implications for teaching. Nurse Educ Today 2013;33:1546-9. 5. Samarakoon L, Fernando T, Rodrigo C. Learning styles and approaches to learning among medical undergraduates and postgraduates. BMC Med Educ 2013;13: 42. 6. Horton DM, Wiederman SD, Saint DA. Assessment outcome is weakly correlated with lecture attendance: influence of learning style and use of alternative materials. Adv Physiol Educ 2012;36:108-15. 7. Koch J, Salamonson Y, Rolley JX, Davidson PM. Learning preference as a predictor of academic performance in first year accelerated graduate entry nursing students: a prospective follow-up study. Nurse Educ Today 2011;31:6116. 8. Leite WL, Svinicki M, Shi Y. Attempted validation of the scores of the VARK: learning styles inventory with multitrait–multimethod confirmatory factor analysis models. Educ Psychol Meas 2010;70:323-39. 9. Alkhasawneh IM, Mrayyan MT, Docherty C, Alashram S, Yousef HY. Problembased learning (PBL): assessing students’ learning preferences using VARK. Nurse Educ Today 2008;28:572-9. 10. Lujan HL, DiCarlo SE. First-year medical students prefer multiple learning styles. Adv Physiol Educ 2006;30:13-6. 11. Kim RH, Gilbert T, Ristig K, Chu QD. Surgical resident learning styles: faculty and resident accuracy at identification of preferences and impact on ABSITE scores. J Surg Res 2013;184:31-6.
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12. Kim RH, Gilbert T. Learning style preferences of surgical residency applicants. J Surg Res 2015;198:61-5. 13. Kim RH, Gilbert T, Ristig K. The effect of surgical resident learning style preferences on American Board of Surgery in-training examination scores. J Surg Educ 2015;72:726-31. 14. Kim RH, Kurtzman SH, Collier AN, Shabahang MM. The learning preferences of applicants who interview for general surgery residency: a multiinstitutional study. J Surg Educ 2016;73:e136-41. 15. Fleming ND. VARK, a guide to learning styles: research & statistics. 2014. http:// vark-learn.com/introduction-to-vark/research-statistics/. Accessed June 1, 2016.
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16. Derossis AM, Da Rosa D, Schwartz A, Hauge LS, Bordage G. Study habits of surgery residents and performance on American Board of Surgery In-Training examinations. Am J Surg 2004;188:230-6. 17. Spurlock DR, Holden C, Hartranft T. Using United States Medical Licensing Examination(®) (USMLE) examination results to predict later in-training examination performance among general surgery residents. J Surg Educ 2010;67:452-6. 18. Alterman DM, Jones TM, Heidel RE, Daley BJ, Goldman MH. The predictive value of general surgery application data for future resident performance. J Surg Educ 2011;68:513-8.