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Learning preference as a predictor of academic performance in first year accelerated graduate entry nursing students: A prospective follow-up study
Nurse Education Today 31 (2011) 611–616
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Nurse Education Today j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / n e d t
Learning preference as a predictor of academic performance in first year accelerated graduate entry nursing students: A prospective follow-up study Jane Koch a,⁎, Yenna Salamonson a, John X. Rolley b,c, Patricia M. Davidson c a b c
School of Nursing and Midwifery, University of Western Sydney, Sydney, Australia St. Vincent's Centre for Nursing Research (Melbourne), School of Nursing and Midwifery (Victoria), Australian Catholic University, Sydney, Australia Centre for Cardiovascular and Chronic Care, Curtin Health Innovation Research Institute, School of Nursing and Midwifery, Curtin University, Sydney Campus, Australia
a r t i c l e
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Article history: Accepted 18 October 2010 Keywords: Academic performance Accelerated graduate entry nursing students English as a Second Language (ESL) Learning preference
s u m m a r y The growth of accelerated graduate entry nursing programs has challenged traditional approaches to teaching and learning. To date, limited research has been undertaken in the role of learning preferences, language proficiency and academic performance in accelerated programs. Sixty-two first year accelerated graduate entry nursing students, in a single cohort at a university in the western region of Sydney, Australia, were surveyed to assess their learning preference using the Visual, Aural, Read/write and Kinaesthetic (VARK) learning preference questionnaire, together with sociodemographic data, English language acculturation and perceived academic control. Six months following course commencement, the participant's grade point average (GPA) was studied as a measurement of academic performance. A 93% response rate was achieved. The majority of students (62%) reported preference for multiple approaches to learning with the kinaesthetic sensory mode a significant (p = 0.009) predictor of academic performance. Students who spoke only English at home had higher mean scores across two of the four categories of VARK sensory modalities, visual and kinaesthetic compared to those who spoke non-English. Further research is warranted to investigate the reasons why the kinaesthetic sensory mode is a predictor of academic performance and to what extent the VARK mean scores of the four learning preference(s) change with improved English language proficiency. © 2010 Elsevier Ltd. All rights reserved.
Background Accelerated graduate nursing programs were introduced as a strategy to address the nursing shortage (Carty and Redmond, 1991) and these programs have enabled non-nursing graduates from various backgrounds to obtain a second degree and move into the nursing profession in a timely manner (Seldomridge and DiBartolo, 2005). This student group challenges traditional teaching and learning methods for example, to develop new, culturally sensitive teaching methods (Carty et al., 1998) and their preference for selfdirected learning (Walker et al., 2007). Learning preferences can influence the approaches to course content, for example, knowing that students have learning preferences encourages teachers to use a variety of teaching methods, materials and media to cater for individual differences (Lujan and DiCarlo, 2006). Knowledge of these approaches also facilitates engaging the learner and supporting their learning processes (Howard-Jones, 2009). To date the learning preferences of students enrolled in accelerated graduate entry
⁎ Corresponding author. School of Nursing and Midwifery, College of Health & Science, University of Western Sydney, Parramatta Campus, Building EI, Locked Bag 1797, Penrith South. DC 1797, New South Wales, Australia. Tel.: +61 2 9685 9395; fax: +61 2 9685 9023. E-mail address: [email protected] (J. Koch). 0260-6917/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.nedt.2010.10.019
programs and the subsequent influence on academic performance have not been explored. Accelerated graduate entry programs generally use the same curricula as traditional nursing programs but involve students with a heavier course load to finish in a shorter time (Wink, 2005). This compressed approach to obtain a nursing qualification is a key motivator to enrol (Raines and Sipes, 2007). Limited evidence is available to determine the reliability and validity of this approach, although Raines and Sipes small cohort study, one year into the new graduates' workforce experience, demonstrated an increased intention to stay employed as well as the self-reported belief that they were effectively prepared for clinical work. Accelerated graduate entry nursing students differ to those in traditional nursing programs. A comparison of demographic variables and various outcomes investigated by Aktan et al. (2009) showed that the grade point average (GPA) was significantly higher in the accelerated group confirming previous studies (Seldomridge and DiBartolo, 2005; Youssef and Goodrich, 1996). Accelerated students have also been shown to outperform traditional students in all aspects of academic performance, test and examination scores, laboratory skills and final course grades (Korvick et al., 2008). These characteristics suggest that curricula need to be tailored to best meet their needs. When comparing preferred teaching methodologies for accelerated graduate students and traditional students, it has been
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found that graduate students have unique needs and expectations; for example the accelerated graduate entry students showed a stronger preference for self-directed learning and placed a greater importance on the grade they received (Walker et al., 2007). In Australia, high numbers of international students enrol in accelerated programs to facilitate achieving permanent residency status. Although there is research looking at language proficiency in international students in Australian universities (Anderson et al., 2004), and that studying in a second language causes many frustrations (Sanner and Wilson, 2008) there is little looking at the extent and effects of ESL in accelerated graduate nursing programs. Language difficulties impact on the capacity for learning and therefore academic performance, Abriam-Yago et al. (1999) and Salamonson et al. (2008), demonstrating that students with lower English language acculturation (literacy proficiency) do less well in all first year subjects in an undergraduate nursing course. Although it is known that there are an increasing proportion of accelerated graduate entry students with English as a second language (ESL), English language acculturation had not been measured and its impact therefore is unknown in this group. There is no literature specifically looking at learning preferences in students with ESL and thus whether learning preference is linked to language proficiency. We were also unable to identify any literature comparing the learning preferences of native English-speaking students with students with ESL. Academic performance is also influenced by our belief in how we can influence academic outcomes and this is known as perceived academic control (PAC). In a Canadian psychology course, students with higher academic control obtained better 3-year GPAs and withdrew from fewer courses (Perry et al., 2005) but it has not been reported whether this is important in accelerated graduate entry nursing students or students with ESL. Thus, knowing the learning preferences of accelerated graduate entry nursing students, their links with language proficiency and academic performance could provide the information to support curriculum changes, more appropriate teaching strategies and assessment methods. Measuring learning preferences Despite criticisms of the influence of learning styles (Norman, 2009), it is acknowledged that learning style awareness is only a small part of the learning process (Desmedt and Valcke, 2003), although some would assert that it is necessary for both the student and educational institutions to understand learning styles (Fielding, 1994). However small the effect on learning outcomes, it is accepted that valid and reliable instruments to measure learning styles can help students enhance their own learning and thus encourage selfdevelopment (Coffield et al., 2004a). Although the relationship between learning styles and outcomes remains contentious, students do have preferences for the ways by which they learn and tailoring is recommended (Lujan and DiCarlo, 2006). Several recent studies (Alkhasawneh et al., 2008; Isman and Gundogan, 2009; MeehanAndrews, 2009) have used the Visual, Aural, Read/write and Kinaesthetic (VARK) questionnaire (Fleming and Mills, 1992) to look at the learning preference of their students, but none have looked at the association of student specific preference(s) with academic performance. Whilst aware of the ‘families’ of learning styles (Coffield et al., 2004b, p.11) and the paradigmatic issues with the VARK questionnaire, most models of learning styles have multiple dimensions. We were interested in one learning preference, information input and output, which both students and teachers are able to change. This influenced the choice of using VARK in this study. Visual learners tend to prefer explanation of concepts diagrammatically or through pictures/diagrams/flow charts. Read/write learners prefer printed text; aural learners concentrate on listening, either directly via lectures or through podcasts or by discussing ideas and kinaesthetic
learners learn through touching and use experiences that emphasize doing, physical involvement, and manipulation of objects and practical examples during their learning (Fleming, 2007). By knowing the preferred mode(s) by which they learn, individual students should be able to enhance their own learning by using the appropriate media. Equally, it requires educators to adopt teaching and assessment strategies to cater for these differences.
Aim This study comprised three linked aims. Firstly to assess the impact of learning preference on performance as assessed by GPA; secondly to see if there is a link between learning preference(s), English language proficiency, ethnodemographic variables and PAC and finally to compare the learning preferences of native English-speaking students with students with ESL.
Method A single cohort of graduate entry students who commenced their baccalaureate nursing program in January 2009 was recruited for this study. A prospective, correlational design was used. Students were surveyed two weeks following starting their course. These data were compared to academic grades at 6-months following course commencement. Participants gave informed consent to take part in the study, and ethical approval was obtained from the University Human Research Ethics Committee.
Measures In addition to sociodemographic variables, three instruments were also included in the baseline survey. They were the: i) English language acculturation scale (ELAS) as an indicator to English language proficiency (Marin et al., 1987); ii) PAC as an indicator of students' self-belief in controlling their own academic performance (Perry et al., 2001); and iii) learning preferences of students using the VARK (visual, aural, reading/writing, and kinaesthetic) questionnaire, which identifies the students' preferred modality of information input and output in a learning context (Fleming, 2007).
English language acculturation scale The English Language Acculturation Scale (ELAS) is a short, 5-item measure of the linguistic aspects of acculturation and is an adaptation of the Short Acculturation Scale for Hispanics (Marin et al., 1987). The language use subscale consists of the following five items: (a) In general, what language(s) do you speak? (b) In general, what language(s) do you read? (c) What language(s) do you usually speak at home? (d) In which language(s) do you usually think? (e) What language(s) do you usually speak with your friends? The five response formats used for each question are: (1) Only non-English language(s); (2) More non-English than English; (3) Both nonEnglish and English equally; (4) More English than non-English; and (5) Only English. Values assigned to the ELAS response format ranged from 1 to 5 (low to high value). ELAS scores were calculated by summing the values to give an overall score for the scale. Potential scores ranged from 5 to 25. The reliability of this scale with a Cronbach's alpha of 0.89 has previously been demonstrated (Salamonson et al., 2008). In this study, Cronbach's alpha of this measure was 0.87. ELAS scores may be used to divide students into three groups: low (5–13), medium (14–18), and high (19–25) scoring groups.
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Perceived academic control scale Perceived academic control (PAC) is an eight item measure developed by Perry et al. (2001). This scale consists of eight items related to influencing academic achievement outcomes. Responses are provided on a Likert-type scale: 1 (strongly disagree) to 5 (strongly agree). The total score is determined by first reverse coding 4 items and then summing the ratings across all 8 items. This scale has been shown to be a reliable measure (Perry et al., 2001). In this study, Cronbach's alpha of this scale was 0.72. VARK questionnaire The VARK questionnaire is a learning preferences tool and version 7.0 consists of 16 multiple choice questions, each with four choices that can be completed in 10–15 minutes. In this study, the instrument was administered by hard copy, although it is also available online. All choices correspond to the four sensory modalities measured by VARK (visual, aural/auditory, read/write, and kinaesthetic). The students can select one or more choices based on the sensory modality preferred by the student to take in new information. The preferences are not absolute, but complementary, with individuals having one or a combination of preferences. Unimodal learners prefer a single modality, whereas individuals preferring a variety of styles are multimodal learners. Scoring of the results is done according to the instructions and sample charts available on the website (Fleming, 2007).
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learning preference category and language spoken at home (English versus non-English only), the Mann Whitney U test was used. To examine the predictors of academic performance at six months following program commencement, a linear multiple regression analysis was performed with GPA as the outcome variable. To control for age, international student status, PAC and English language acculturation, we performed a hierarchical regression analysis, with simultaneous entry of these five variables, and stepwise entry of five VARK (visual, aural, read/write, kinesthetic and multimodal) categories into the regression model. Hierarchical regression analysis was selected to enable the simultaneous entry of variables previously determined to be associated with academic performance (Salamonson and Andrew, 2006; Salamonson et al., 2008, 2010) and at the same time explore if any of the learning preference categories of VARK were an explanatory variable of academic performance. The reason for using stepwise procedure for this subset of predictor variables of the learning preferences was to assess the relative importance of each sensory modality on academic performance as this has yet to be reported in literature. A p value of less than 0.05 was considered statistically significant in this study. Power analysis was computed using G*Power version 3.0.10 (Faul et al., 2007). To detect a large effect size (f2 = 0.35) with 80% power, an alpha level of 0.05 (two-tailed), six predictor variables in the linear regression model, we needed a sample size of 46 participants.
Results
Study setting
Sample characteristics
The accelerated graduate baccalaureate nursing program, also commonly known as the accelerated graduate entry nursing program, began within the study setting in 2006. Most of the students who enrolled in this program were international full-fee paying students who held a baccalaureate degree from a non-Australian institution. Hence, these students are informed consumers of tertiary education, often with high expectations of their own performance, the academic staff as well as the institution.
Table 1 shows the sample characteristics of this study. Of the 61 students who completed the survey and consented for their academic grades to be collected, 90% (n = 55) of these students were females, and 85% (n = 52) were international students. Ninety-five percent (n = 58) were born outside Australia and spoke another language other than English at home. The English language acculturation scale (ELAS), mean was 12.8 (SD: 4.2), which would be in the lower group (5–13). The mean age was 26.5 (SD: 4.9) years, and the mean GPA score at the 6-month follow-up was 4.6 (SD: 0.9). The GPA, at the study setting, indicates the average of all unit grades where 7.0 is the highest possible grade and 3.0 is the lowest possible pass GPA.
Data collection and procedure During the second week of the accelerated graduate entry nursing program, we invited students to complete the survey related to this study. In addition to the VARK questionnaire, we also asked students to complete the ELAS, perceive academic control scale, as well as questions related to students' sociodemographic characteristics. As well as providing students with an information sheet about the study, students were also briefed about the purpose of the study by teaching staff. Written consent was also sought from students to link their academic grades to their survey. Of the 67 students who enrolled in the program, 62 (93%) students completed the survey. One student did not consent for their academic grades to be linked to her survey, and was excluded from data analysis, leaving a final sample of 61 (91%) responses for data analysis. Data analysis All analyses were performed using SPSS version 17.0.1 for Windows. Descriptive statistics were computed to obtain an overall profile of the sample. Continuous variables were expressed as means with standard deviations (SD). Categorical variables were expressed as percentages. The Kaiser–Meyer–Olkin measure of sampling adequacy was used to examine for the appropriateness of factor analysis of the VARK. The number of factors to extract was determined using the scree plot and internal consistency was analysed using Cronbach's alpha. To test for differences between VARK scores in each
Table 1 Sociodemographic characteristics and learning preferences of the sample (n = 61). Characteristic Age, mean (SD) years; Range: 22–44 Sex (Female)% Country of birth (Non-Australian born)% Language spoken at home% • English only • Both English and non-English • Non-English only International student (Yes)% English language acculturation scale (ELAS), mean (SD), Range: 5–25 Engagement in paid employment during semester (Yes)% Average time in paid employment, mean (SD) hours; Range: 0–20 Perceived academic control, mean (SD); Range: 21–39 Multimodal learning preference (Yes)% Learning preference score • Visual, mean (SD); Range: 0–11 • Aural, mean (SD); Range: 1–13 • Read/write, mean (SD); Range: 0–13 • Kinesthetic, mean (SD); Range: 0–14 GPA, mean (SD); Range: 3–6
26.5(4.9) 90 95 5 29 66 85 12.8(4.2) 25 13.1(4.3) 30.8(4.0) 62 3.6(2.5) 6.1(3.1) 6.3(3.0) 5.6(3.2) 4.6(0.9)
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Validity and reliability The 16-item VARK was demonstrated to be factorable with a Kaiser–Meyer–Olkin measure of sampling adequacy of 0.86, a meritorious level of intercorrelation among items. The underlying number of factors identified by scree plot suggested one factor, which accounted for 38.9% of total variance. This was confirmed by principal axis factor, which extracted one factor, with all 16 items loading between 0.35 and 0.84. The Cronbach alpha of the VARK was 0.91, none of the alpha values were higher when the item was deleted indicated all 16 items contributed to the VARK scale. Learning preferences As shown in Table 1, 62% of students reported to have more than a single mode of learning preference, that is these students prefer more than a single mode of receiving and presenting learning information. Based on mean scores, the most dominant learning preferences were read/write (mean: 6.3, SD: 3.0) and aural (mean: 6.1, SD: 3.1), followed by kinaesthetic (mean: 5.6, SD: 3.2), and finally, visual (mean: 3.6, SD: 2.5). Comparison of mean VARK scores with language spoken at home When the mean VARK scores in each category were compared with language spoken at home, students who spoke only English at home had higher mean score in two of the four categories of VARK sensory modalities, which were visual (English-speaking: mean 4.7, SD: 3.0 versus non-English-speaking: mean: 3.3. SD: 2.3, P = 0.037) and kinaesthetic (English-speaking: mean 6.4, SD: 2.6 versus nonEnglish-speaking: mean: 5.2. SD: 3.4, P = 0.040). Predictors of academic performance at the 6-month follow-up The results of stepwise multiple regression of VARK learning preference categories, controlling for age, international student status, PAC and ELAS score indicated that kinesthetic sensory mode was a positive and significant predictor of academic performance at the 6month follow-up in graduate entry nursing students (Table 2). None of the five variables (age, international student status, PAC and ELAS score), which have been previously shown to be associated with academic performance, were found to be significant predictors of academic performance in this study sample. Discussion Two major finding in this study that have not been reported before are that the kinaesthetic sensory mode predicted academic performance (Table 2) and that students who spoke only English at home had higher mean scores across two of the four categories of VARK sensory modalities, visual and kinaesthetic. The finding that the kinaesthetic sensory mode was a positive and significant predictor of academic performance at the 6-month followup in the study cohort was interesting. It may be the case that early Table 2 Multiple regression model predicting students' GPA at 6 months following course commencement. Variable
β
t
Grade Point Average scores at six months of course commencement Age − 0.34 − 1.98 International student (Yes) − 0.07 − 0.39 Perceived academic control 0.01 0.08 English language acculturation scale (ELAS) score − 0.31 − 1.78 VARK score: Kinaesthetic 0.39 2.73 Overall model: R2 = 0.260, F(df) = 2.947 (5, 47), P = 0.023, adjusted R2 = 0.17.
p 0.055 0.699 0.934 0.082 0.009
educational experience in different countries underpins subsequent learning preferences and given the high percentage of international students in the cohort (85%), a possible explanation could be that their previous tertiary study was in a more didactic setting, where teaching was more instructive in style, less interactive and learning passive, whereas the nursing course in Australia is more clinically based and encourages ‘doing’, questioning, discussion and reflection which would favour students with kinesthetic sensory modality. Students with this learning preference take in information best through practical sessions, case studies or computer simulations. They process the information by recalling their experiences, and need to actually do things before they are able to fully understand the concepts. Although the 6-month follow-up may be considered to be premature, one may speculate that performance at this time point is likely to be predictive of ongoing academic success. It was a somewhat surprising finding that the read/write sensory modality was not a significant predictor of academic performance, as some of the assessments undertaken by the students during the first 6 months of their course and in their previous degrees would have been written. However, the most likely explanation is that this learning preference is very high in the group anyway and hence a ceiling effect was achieved. A ceiling effect occurs when a high proportion of the respondents score the highest possible score (Twisk and Rijmen, 2009). Thus the instrument will lack sensitivity at high levels, being incapable of achieving adequate discrimination in measuring the true construct range (McHorney and Tarlov, 1995) Alternatively, it could be argued that students with ESL will compensate for a lack of language competence/confidence and, in so doing, not have a preference for read/write learning, hence the preference for kinaesthetic sensory mode. That the kinaesthetic modality predicts academic performances leads to the question ‘can learning preferences change?’ It has been shown that undergraduate learning preferences are susceptible to change over time (Kell and van Deursen, 2002), affected by curricular factors, and thus, if students were given a more hands on approach to learning, this may encourage change and enhance success. This susceptibility to change also impacts on future curriculum development. A VARK questionnaire follow-up at 12 months in the present study would have confirmed the durability of the learning preference once language skills were more established. Comparison of students' mean VARK scores with language spoken at home was also an interesting finding in that the mean score across two of the four categories of VARK sensory modalities, (visual and kinaesthetic), were lower in students with ESL, when compared with native English-speaking students. It has been shown that the language spoken at home is a proxy for language proficiency (Salamonson et al., 2007). When looking at the universal diverse orientation among nursing students, specifically the ‘comfort with differences’ dimension or the sense of connectedness with others, it was found that students who never spoke English at home had low scores in this dimension. However, the score increased as the years of residence in Australia increased, possibly as they become more acculturated with the Australian lifestyle, e.g., customs and values, and their English language proficiency improved. Similarly, it would be informative to administer the VARK questionnaire at the end of the accelerated course to see if, as the English language proficiency improves, the mean VARK mean scores or learning preference modalities profile changes. Although resource intensive, it would be interesting to compare the mean scores of the learning preference modalities when students with ESL completed the VARK questionnaire in their native language. However, as every aspect of the nursing course is completed in English, it can be seen that the importance of English language proficiency is paramount. Further research is needed to see if students with ESL improve English language proficiency more quickly if learning resources were provided in their preferred modality.
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Although the VARK questionnaire has not been used to compare with academic performance, it been used to look at the learning preferences of first year medical students in Michigan, United States of America (Lujan and DiCarlo, 2006), third year nursing students in Jordan (Alkhasawneh et al., 2008), first year nursing students in Australia (Meehan-Andrews, 2009) and medical students in Turkey (Isman and Gundogan, 2009). Consistent with our results where 62% of the students have more than a single mode of learning preference, three of the studies also reported this, ranging from 58 to 78%, although only 46% in the Meehan-Andrews study were multimodal. The multimodal preference is consistent with characteristics of adult learners, students being more flexible in taking in information than those who use a single modality (Fleming, 2007). It also has implications for teachers to use more multimodal learning resources and assessments. The most common unimodal preference for the medical students and the nursing students in the Meehan-Andrews study was kinaesthetic mode, whereas the unimodal preference of the students in this study and the Jordanian nursing students was read/ write. It is worth noting however, that as most assessment formats favour the student with the read/write learning preference, further research is needed to see if this disadvantages students with other learning preferences. Knowing their particular learning preferences (s) should encourage student self-development and further research is needed to see if students acted on the feedback received with their VARK scores and whether they perceived it to be effective. Based on their profile, the feedback suggested ways to help them to learn. However, knowing the learning preferences of accelerated graduate entry nursing students may provide the impetus to develop more multisensory resources and assignments, a more hands on approach, more web-based learning and computer simulation case studies (Kell and van Deursen, 2002; Walker et al., 2007) and possibly streamline or even shorten further the already shortened curriculum to help address the nursing shortage. Language proficiency was also measured by the ELAS. In this study, 85% were international students and the mean ELAS score of the total group was at the upper end of the low group (5–13). This is not surprising as many of them were new arrivals to Australia who would be expected to have lower scores. When comparing this group of accelerated graduate entry nursing students (12.8, SD 4) with students enrolled in a traditional undergraduate nursing course, the mean score was higher (16.42, SD 4.83) in the traditional nursing students, this being in the medium group (14–18) (Salamonson et al., 2008). Although the grade point average was not recorded in the Salamonson et al. study, they showed that students in the lower group (with lower language acculturation) did less well in all first year subjects. That this positive association between ELAS scores and academic performance was not shown to be a predictor of academic performance among the graduate entry nursing students is of note, particularly as many of these students had low levels of English language acculturation. A possible explanation for this could be that the graduate students have previous experience of academia and have already developed the skills to help their performance. It may also be that the first year structure of the traditional and accelerated courses are different and fewer written assessment items may advantage the graduate entry students, but this warrants further investigation. Similarly, in this study sample, age and PAC were not shown to be predictors of academic performance. Most studies have shown that mature-age students achieve better grades than younger undergraduate nursing students (Houltram, 1996; Kevern et al., 1999; McCarey et al., 2007; Ofori, 2000; Salamonson and Andrew, 2006; van Rooyen et al., 2006). Korvick et al. (2008) and Carty et al. (2007) found, as in this study, that age was not associated with academic performance in accelerated graduate entry nursing students. Similarly, although Perry et al. (2005) found that students higher in academic control obtained higher GPAs, in a study examining the links between PAC and academic comparative optimism, the positive expectations about
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future performance in undergraduate psychology students, it was shown that PAC did not predict performance (Ruthig et al., 2009). There are limitations that need to be considered in our study. Firstly, the study sample of 61 is a small number, although of the total cohort, this represented as a response rate of 91%. The veracity of subgroup analysis is also limited by the small sample size and requires further replication. Also, as the majority of the sample was international students, this may influence the external validity of the findings. Further exploration of the findings of this study is warranted. A follow-up administration of the VARK questionnaire at 12 months may have confirmed the durability of the learning preference once the language skills of the students were more established. Secondly, that the VARK questionnaire lacked statistical validation (Fleming and Baume, 2006), but despite this, its strength is that questions are developed from real life situations and students identify with the results that they are given— that is, they affirm the face validity of the instrument. Fleming and Baume (2006), when developing the instrument, decided against testing for construct validity as the original intention of the instrument was to “...stimulate reflection and discussion.”(p.139). Leite et al. (2010) analysed 15,137 completed questionnaires delivered over the internet and found, for teaching and learning purposes, the VARK is an appropriate instrument to use yet caution its use in research due to the above discussed lack of validity testing. Despite these limitations, these data provide a useful contribution to exploring the relationship between learning preferences and academic performance, particularly in an accelerated graduate nursing program. In terms of curriculum development, incorporating more practically based activities may be an important innovation. Conclusion In graduate entry accelerated students we found that the kinaesthetic sensory mode was a predictor of academic performance at 6-months. Although the majority of these students were international students, many of whom were in the low English language acculturation group, the academic performance of this cohort was better than nursing students undertaking a traditional course. This may have been that their previous tertiary study resulted in skills that enabled them to overcome this disadvantage but requires further research. References Abriam-Yago, K., Yoder, M., Kataoka-Yahiro, M., 1999. The Cummins model: a framework for teaching nursing students for whom English is a second language. Journal of Transcultural Nursing 10 (2), 143–149. Aktan, N.M., Bareford, C.G., Bliss, J.B., Connolly, K., DeYoung, S., Lancellotti Sullivan, K., et al., 2009. Comparison of outcomes in a traditional versus accelerated nursing curriculum. International Journal of Nursing Education Scholarship 6 (1), 1–11. Alkhasawneh, I.M., Mrayyan, M.T., Docherty, C., Alashram, S., Yousef, H.Y., 2008. Problem-based learning (PBL): assessing students' learning preferences using VARK. Nurse Education Today 28 (5), 572–579. Anderson, P., Reberger, H., Doube, L., 2004. Language proficiency and academic outcomes for international postgraduate students at the University of AdelaideRetrieved May 2, 2010, from http://www.adelaide.edu.au/graduatecentre/redc/ redc/Item5_08_15C.pdf2004. Carty, R.M., Redmond, G.M., 1991. An analysis of the process for the development of a career option in nursing for college graduates. Journal of Nursing Education 30 (7), 330–332. Carty, R.M., Hale, J.F., Carty, G.M., Williams, J., Rigney, D., Principato, J.J., 1998. Teaching international nursing students: challenges and strategies. Journal of Professional Nursing 14 (1), 34–42. Carty, R.M., Moss, M.M., Al-zayyer, W., Kowitlawakul, Y., Arietti, L., 2007. Predictors of success for Saudi Arabian students enrolled in an accelerated baccalaureate degree program in nursing in the United States. Journal of Professional Nursing 23 (5), 301–308. Coffield, F., Moseley, D., Hall, E., Ecclestone, K., 2004a. Should we be using learning styles? What research has to say to practice. London: The Learning and Skills Research Centre. Coffield, F., Moseley, D., Hall, E., Ecclestone, K., 2004b. Learning styles and pedagogy in post-16 learning: a systematic and critical review. London: The Learning and Skills Research Centre.
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Desmedt, E., Valcke, M., 2003. Learning style awareness: why would it work? Paper presented at the Proceedings of the Eighth Annual European Learning Styles Information Network Conference, University of Hull, UK. Faul, F., Erdfelder, E., Lang, A.-G., Buchner, A., 2007. G*Power3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods 39 (2), 175–191. Fielding, M., 1994. Valuing difference in teachers and learners: building on Kolb's learning styles to develop a language of teaching and learning. Curriculum Journal 5 (3), 393–417. Fleming, N., 2007. Frequently asked questions: VARK a guide to learning stylesRetrieved May 2, 2009, from http://www.vark-learn.com/english/page.asp? p=faq2007. Fleming, N., Baume, D., 2006. Learning styles again: VARKing up the right tree! Educational Developments, SEDA 7.4, 4–7. Fleming, N.D., Mills, C., 1992. Not another inventory, rather a catalyst for reflection. To Improve the Academy 11, 137–149. Houltram, B., 1996. Entry age, entry mode and academic performance on a Project 2000 common foundation programme. Journal of Advanced Nursing 23 (6), 1089–1097. Howard-Jones, P., 2009. Neuroscience and Education: Issues and Opportunities. Economic and Social Research Council, London. Isman, C.A., Gundogan, N.U., 2009. The influence of digit ratio on the gender difference in learning style preferences. Personality and Individual Differences 46 (4), 424–427. Kell, C., van Deursen, R., 2002. Student learning preferences reflect curricular change. Medical Teacher 24 (1), 32–40. Kevern, J., Ricketts, C., Webb, C., 1999. Pre-registration diploma students: a quantitative study of entry characteristics and course outcomes. Journal of Advanced Nursing 30 (4), 785–795. Korvick, L.M., Wisener, L.K., Loftis, L.A., Williamson, M.L., 2008. Comparing the academic performance of students in traditional and second-degree baccalaureate programs. Journal of Nursing Education 47 (3), 139–141. Leite, W.L., Svinicki, M., Shi, Y., 2010. Attempted validation of the scores of the VARK: learning styles inventory with multitrait-multimethod confirmatory factor analysis models. Educational and Psychological Measurement 70, 323–340. Lujan, H.L., DiCarlo, S.E., 2006. First-year medical students prefer multiple learning styles. Advances in Physiology Education 30 (1), 13–16. Marin, G., Sabogal, F., Vanoss Marin, B., Otero-Sabogal, F., Perez-Stable, E.J., 1987. Development of a short acculturation scale for Hispanics. Hispanic Journal of Behavioral Sciences 9 (2), 183–205. McCarey, M., Barr, T., Rattray, J., 2007. Predictors of academic performance in a cohort of pre-registration nursing students. Nurse Education Today 27 (4), 357–364. McHorney, C.A., Tarlov, A.R., 1995. Individual-patient monitoring in clinical practice: are available health status surveys adequate? Quality of Life Research 4 (4), 293–307. Meehan-Andrews, T.A., 2009. Teaching mode efficiency and learning preferences of first year nursing students. Nurse Education Today 29 (1), 24–32. Norman, G., 2009. When will learning style go out of style? Advances in Health and Science Education 14, 1–4.
Ofori, R., 2000. Age and ‘type’ of domain specific entry qualifications as predictors of student nurses' performance in biological, social and behavioural sciences in nursing assessments. Nurse Education Today 20 (4), 298–310. Perry, R.P., Hladkyj, S., Pekrun, R.H., Pelletier, S.T., 2001. Academic control and action control in the achievement of college students: a longitudinal field study. Journal of Educational Psychology 93 (4), 776–789. Perry, R.P., Hladkyj, S., Pekrun, R.H., Clifton, R.A., Chipperfield, J.G., 2005. Perceived academic control and failure in college students: a three-year study of scholastic attainment. Research in Higher Education 46 (5), 535–569. Raines, D.A., Sipes, A., 2007. One year later: reflections and work activities of accelerated second-degree bachelor of science in nursing graduates. Journal of Professional Nursing 23 (6), 329–334. Ruthig, J.C., Hanson, B.L., Marino, J.M., 2009. A three-phase examination of academic comparative optimism and perceived academic control. Learning and Individual Differences 19 (4), 435–439. Salamonson, Y., Andrew, S., 2006. Academic performance in nursing students: influence of part-time employment, age and ethnicity. Journal of Advanced Nursing 55 (3), 342–349. Salamonson, Y., Everett, B., Andrew, S., Koch, J., Davidson, P.M., 2007. Differences in universal diverse orientation among nursing students in Australia. Nursing Outlook 55 (6), 296–302. Salamonson, Y., Everett, B., Koch, J., Andrew, S., Davidson, P.M., 2008. English-language acculturation predicts academic performance in nursing students who speak English as a second language. Research in Nursing & Health 31 (1), 86–94. Salamonson, Y., Koch, J., Weaver, R., Everett, B., Jackson, D., 2010. Embedded academic writing support for nursing students with English as a second language. Journal of Advanced Nursing 66 (2), 413–421. Sanner, S., Wilson, A., 2008. The experiences of students with English as a second language in a baccalaureate nursing program. Nurse Education Today 28 (7), 807–813. Seldomridge, L.A., DiBartolo, M.C., 2005. A profile of accelerated second bachelor's degree nursing students. Nurse Educator 30 (2), 65–68. Twisk, J., Rijmen, F., 2009. Longitudinal tobit regression: a new approach to analyze outcome variables with floor or ceiling effects. Journal of Clinical Epidemiology 62 (9), 953–958. van Rooyen, P., Dixon, A., Dixon, G., Wells, C., 2006. Entry criteria as predictor of performance in an undergraduate nursing degree programme. Nurse Education Today 26 (7), 593–600. Walker, J.T., Martin, T.M., Haynie, L., Norwood, A., White, J., Grant, L., 2007. Preferences for teaching methods in a baccalaureate nursing program: how second-degree and traditional students differ. Nursing Education Perspectives 28 (5), 246–250. Wink, D.M., 2005. Accelerated nursing programs for non-nursing college graduates. In Annual Review of Nursing Education 3, 271–297. Youssef, F.A., Goodrich, N., 1996. Accelerated versus traditional nursing students: a comparison of stress, critical thinking ability and performance. International Journal of Nursing Studies 33 (1), 76–82.
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Nurse Education Today j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / n e d t
Learning preference as a predictor of academic performance in first year accelerated graduate entry nursing students: A prospective follow-up study Jane Koch a,⁎, Yenna Salamonson a, John X. Rolley b,c, Patricia M. Davidson c a b c
School of Nursing and Midwifery, University of Western Sydney, Sydney, Australia St. Vincent's Centre for Nursing Research (Melbourne), School of Nursing and Midwifery (Victoria), Australian Catholic University, Sydney, Australia Centre for Cardiovascular and Chronic Care, Curtin Health Innovation Research Institute, School of Nursing and Midwifery, Curtin University, Sydney Campus, Australia
a r t i c l e
i n f o
Article history: Accepted 18 October 2010 Keywords: Academic performance Accelerated graduate entry nursing students English as a Second Language (ESL) Learning preference
s u m m a r y The growth of accelerated graduate entry nursing programs has challenged traditional approaches to teaching and learning. To date, limited research has been undertaken in the role of learning preferences, language proficiency and academic performance in accelerated programs. Sixty-two first year accelerated graduate entry nursing students, in a single cohort at a university in the western region of Sydney, Australia, were surveyed to assess their learning preference using the Visual, Aural, Read/write and Kinaesthetic (VARK) learning preference questionnaire, together with sociodemographic data, English language acculturation and perceived academic control. Six months following course commencement, the participant's grade point average (GPA) was studied as a measurement of academic performance. A 93% response rate was achieved. The majority of students (62%) reported preference for multiple approaches to learning with the kinaesthetic sensory mode a significant (p = 0.009) predictor of academic performance. Students who spoke only English at home had higher mean scores across two of the four categories of VARK sensory modalities, visual and kinaesthetic compared to those who spoke non-English. Further research is warranted to investigate the reasons why the kinaesthetic sensory mode is a predictor of academic performance and to what extent the VARK mean scores of the four learning preference(s) change with improved English language proficiency. © 2010 Elsevier Ltd. All rights reserved.
Background Accelerated graduate nursing programs were introduced as a strategy to address the nursing shortage (Carty and Redmond, 1991) and these programs have enabled non-nursing graduates from various backgrounds to obtain a second degree and move into the nursing profession in a timely manner (Seldomridge and DiBartolo, 2005). This student group challenges traditional teaching and learning methods for example, to develop new, culturally sensitive teaching methods (Carty et al., 1998) and their preference for selfdirected learning (Walker et al., 2007). Learning preferences can influence the approaches to course content, for example, knowing that students have learning preferences encourages teachers to use a variety of teaching methods, materials and media to cater for individual differences (Lujan and DiCarlo, 2006). Knowledge of these approaches also facilitates engaging the learner and supporting their learning processes (Howard-Jones, 2009). To date the learning preferences of students enrolled in accelerated graduate entry
⁎ Corresponding author. School of Nursing and Midwifery, College of Health & Science, University of Western Sydney, Parramatta Campus, Building EI, Locked Bag 1797, Penrith South. DC 1797, New South Wales, Australia. Tel.: +61 2 9685 9395; fax: +61 2 9685 9023. E-mail address: [email protected] (J. Koch). 0260-6917/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.nedt.2010.10.019
programs and the subsequent influence on academic performance have not been explored. Accelerated graduate entry programs generally use the same curricula as traditional nursing programs but involve students with a heavier course load to finish in a shorter time (Wink, 2005). This compressed approach to obtain a nursing qualification is a key motivator to enrol (Raines and Sipes, 2007). Limited evidence is available to determine the reliability and validity of this approach, although Raines and Sipes small cohort study, one year into the new graduates' workforce experience, demonstrated an increased intention to stay employed as well as the self-reported belief that they were effectively prepared for clinical work. Accelerated graduate entry nursing students differ to those in traditional nursing programs. A comparison of demographic variables and various outcomes investigated by Aktan et al. (2009) showed that the grade point average (GPA) was significantly higher in the accelerated group confirming previous studies (Seldomridge and DiBartolo, 2005; Youssef and Goodrich, 1996). Accelerated students have also been shown to outperform traditional students in all aspects of academic performance, test and examination scores, laboratory skills and final course grades (Korvick et al., 2008). These characteristics suggest that curricula need to be tailored to best meet their needs. When comparing preferred teaching methodologies for accelerated graduate students and traditional students, it has been
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found that graduate students have unique needs and expectations; for example the accelerated graduate entry students showed a stronger preference for self-directed learning and placed a greater importance on the grade they received (Walker et al., 2007). In Australia, high numbers of international students enrol in accelerated programs to facilitate achieving permanent residency status. Although there is research looking at language proficiency in international students in Australian universities (Anderson et al., 2004), and that studying in a second language causes many frustrations (Sanner and Wilson, 2008) there is little looking at the extent and effects of ESL in accelerated graduate nursing programs. Language difficulties impact on the capacity for learning and therefore academic performance, Abriam-Yago et al. (1999) and Salamonson et al. (2008), demonstrating that students with lower English language acculturation (literacy proficiency) do less well in all first year subjects in an undergraduate nursing course. Although it is known that there are an increasing proportion of accelerated graduate entry students with English as a second language (ESL), English language acculturation had not been measured and its impact therefore is unknown in this group. There is no literature specifically looking at learning preferences in students with ESL and thus whether learning preference is linked to language proficiency. We were also unable to identify any literature comparing the learning preferences of native English-speaking students with students with ESL. Academic performance is also influenced by our belief in how we can influence academic outcomes and this is known as perceived academic control (PAC). In a Canadian psychology course, students with higher academic control obtained better 3-year GPAs and withdrew from fewer courses (Perry et al., 2005) but it has not been reported whether this is important in accelerated graduate entry nursing students or students with ESL. Thus, knowing the learning preferences of accelerated graduate entry nursing students, their links with language proficiency and academic performance could provide the information to support curriculum changes, more appropriate teaching strategies and assessment methods. Measuring learning preferences Despite criticisms of the influence of learning styles (Norman, 2009), it is acknowledged that learning style awareness is only a small part of the learning process (Desmedt and Valcke, 2003), although some would assert that it is necessary for both the student and educational institutions to understand learning styles (Fielding, 1994). However small the effect on learning outcomes, it is accepted that valid and reliable instruments to measure learning styles can help students enhance their own learning and thus encourage selfdevelopment (Coffield et al., 2004a). Although the relationship between learning styles and outcomes remains contentious, students do have preferences for the ways by which they learn and tailoring is recommended (Lujan and DiCarlo, 2006). Several recent studies (Alkhasawneh et al., 2008; Isman and Gundogan, 2009; MeehanAndrews, 2009) have used the Visual, Aural, Read/write and Kinaesthetic (VARK) questionnaire (Fleming and Mills, 1992) to look at the learning preference of their students, but none have looked at the association of student specific preference(s) with academic performance. Whilst aware of the ‘families’ of learning styles (Coffield et al., 2004b, p.11) and the paradigmatic issues with the VARK questionnaire, most models of learning styles have multiple dimensions. We were interested in one learning preference, information input and output, which both students and teachers are able to change. This influenced the choice of using VARK in this study. Visual learners tend to prefer explanation of concepts diagrammatically or through pictures/diagrams/flow charts. Read/write learners prefer printed text; aural learners concentrate on listening, either directly via lectures or through podcasts or by discussing ideas and kinaesthetic
learners learn through touching and use experiences that emphasize doing, physical involvement, and manipulation of objects and practical examples during their learning (Fleming, 2007). By knowing the preferred mode(s) by which they learn, individual students should be able to enhance their own learning by using the appropriate media. Equally, it requires educators to adopt teaching and assessment strategies to cater for these differences.
Aim This study comprised three linked aims. Firstly to assess the impact of learning preference on performance as assessed by GPA; secondly to see if there is a link between learning preference(s), English language proficiency, ethnodemographic variables and PAC and finally to compare the learning preferences of native English-speaking students with students with ESL.
Method A single cohort of graduate entry students who commenced their baccalaureate nursing program in January 2009 was recruited for this study. A prospective, correlational design was used. Students were surveyed two weeks following starting their course. These data were compared to academic grades at 6-months following course commencement. Participants gave informed consent to take part in the study, and ethical approval was obtained from the University Human Research Ethics Committee.
Measures In addition to sociodemographic variables, three instruments were also included in the baseline survey. They were the: i) English language acculturation scale (ELAS) as an indicator to English language proficiency (Marin et al., 1987); ii) PAC as an indicator of students' self-belief in controlling their own academic performance (Perry et al., 2001); and iii) learning preferences of students using the VARK (visual, aural, reading/writing, and kinaesthetic) questionnaire, which identifies the students' preferred modality of information input and output in a learning context (Fleming, 2007).
English language acculturation scale The English Language Acculturation Scale (ELAS) is a short, 5-item measure of the linguistic aspects of acculturation and is an adaptation of the Short Acculturation Scale for Hispanics (Marin et al., 1987). The language use subscale consists of the following five items: (a) In general, what language(s) do you speak? (b) In general, what language(s) do you read? (c) What language(s) do you usually speak at home? (d) In which language(s) do you usually think? (e) What language(s) do you usually speak with your friends? The five response formats used for each question are: (1) Only non-English language(s); (2) More non-English than English; (3) Both nonEnglish and English equally; (4) More English than non-English; and (5) Only English. Values assigned to the ELAS response format ranged from 1 to 5 (low to high value). ELAS scores were calculated by summing the values to give an overall score for the scale. Potential scores ranged from 5 to 25. The reliability of this scale with a Cronbach's alpha of 0.89 has previously been demonstrated (Salamonson et al., 2008). In this study, Cronbach's alpha of this measure was 0.87. ELAS scores may be used to divide students into three groups: low (5–13), medium (14–18), and high (19–25) scoring groups.
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Perceived academic control scale Perceived academic control (PAC) is an eight item measure developed by Perry et al. (2001). This scale consists of eight items related to influencing academic achievement outcomes. Responses are provided on a Likert-type scale: 1 (strongly disagree) to 5 (strongly agree). The total score is determined by first reverse coding 4 items and then summing the ratings across all 8 items. This scale has been shown to be a reliable measure (Perry et al., 2001). In this study, Cronbach's alpha of this scale was 0.72. VARK questionnaire The VARK questionnaire is a learning preferences tool and version 7.0 consists of 16 multiple choice questions, each with four choices that can be completed in 10–15 minutes. In this study, the instrument was administered by hard copy, although it is also available online. All choices correspond to the four sensory modalities measured by VARK (visual, aural/auditory, read/write, and kinaesthetic). The students can select one or more choices based on the sensory modality preferred by the student to take in new information. The preferences are not absolute, but complementary, with individuals having one or a combination of preferences. Unimodal learners prefer a single modality, whereas individuals preferring a variety of styles are multimodal learners. Scoring of the results is done according to the instructions and sample charts available on the website (Fleming, 2007).
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learning preference category and language spoken at home (English versus non-English only), the Mann Whitney U test was used. To examine the predictors of academic performance at six months following program commencement, a linear multiple regression analysis was performed with GPA as the outcome variable. To control for age, international student status, PAC and English language acculturation, we performed a hierarchical regression analysis, with simultaneous entry of these five variables, and stepwise entry of five VARK (visual, aural, read/write, kinesthetic and multimodal) categories into the regression model. Hierarchical regression analysis was selected to enable the simultaneous entry of variables previously determined to be associated with academic performance (Salamonson and Andrew, 2006; Salamonson et al., 2008, 2010) and at the same time explore if any of the learning preference categories of VARK were an explanatory variable of academic performance. The reason for using stepwise procedure for this subset of predictor variables of the learning preferences was to assess the relative importance of each sensory modality on academic performance as this has yet to be reported in literature. A p value of less than 0.05 was considered statistically significant in this study. Power analysis was computed using G*Power version 3.0.10 (Faul et al., 2007). To detect a large effect size (f2 = 0.35) with 80% power, an alpha level of 0.05 (two-tailed), six predictor variables in the linear regression model, we needed a sample size of 46 participants.
Results
Study setting
Sample characteristics
The accelerated graduate baccalaureate nursing program, also commonly known as the accelerated graduate entry nursing program, began within the study setting in 2006. Most of the students who enrolled in this program were international full-fee paying students who held a baccalaureate degree from a non-Australian institution. Hence, these students are informed consumers of tertiary education, often with high expectations of their own performance, the academic staff as well as the institution.
Table 1 shows the sample characteristics of this study. Of the 61 students who completed the survey and consented for their academic grades to be collected, 90% (n = 55) of these students were females, and 85% (n = 52) were international students. Ninety-five percent (n = 58) were born outside Australia and spoke another language other than English at home. The English language acculturation scale (ELAS), mean was 12.8 (SD: 4.2), which would be in the lower group (5–13). The mean age was 26.5 (SD: 4.9) years, and the mean GPA score at the 6-month follow-up was 4.6 (SD: 0.9). The GPA, at the study setting, indicates the average of all unit grades where 7.0 is the highest possible grade and 3.0 is the lowest possible pass GPA.
Data collection and procedure During the second week of the accelerated graduate entry nursing program, we invited students to complete the survey related to this study. In addition to the VARK questionnaire, we also asked students to complete the ELAS, perceive academic control scale, as well as questions related to students' sociodemographic characteristics. As well as providing students with an information sheet about the study, students were also briefed about the purpose of the study by teaching staff. Written consent was also sought from students to link their academic grades to their survey. Of the 67 students who enrolled in the program, 62 (93%) students completed the survey. One student did not consent for their academic grades to be linked to her survey, and was excluded from data analysis, leaving a final sample of 61 (91%) responses for data analysis. Data analysis All analyses were performed using SPSS version 17.0.1 for Windows. Descriptive statistics were computed to obtain an overall profile of the sample. Continuous variables were expressed as means with standard deviations (SD). Categorical variables were expressed as percentages. The Kaiser–Meyer–Olkin measure of sampling adequacy was used to examine for the appropriateness of factor analysis of the VARK. The number of factors to extract was determined using the scree plot and internal consistency was analysed using Cronbach's alpha. To test for differences between VARK scores in each
Table 1 Sociodemographic characteristics and learning preferences of the sample (n = 61). Characteristic Age, mean (SD) years; Range: 22–44 Sex (Female)% Country of birth (Non-Australian born)% Language spoken at home% • English only • Both English and non-English • Non-English only International student (Yes)% English language acculturation scale (ELAS), mean (SD), Range: 5–25 Engagement in paid employment during semester (Yes)% Average time in paid employment, mean (SD) hours; Range: 0–20 Perceived academic control, mean (SD); Range: 21–39 Multimodal learning preference (Yes)% Learning preference score • Visual, mean (SD); Range: 0–11 • Aural, mean (SD); Range: 1–13 • Read/write, mean (SD); Range: 0–13 • Kinesthetic, mean (SD); Range: 0–14 GPA, mean (SD); Range: 3–6
26.5(4.9) 90 95 5 29 66 85 12.8(4.2) 25 13.1(4.3) 30.8(4.0) 62 3.6(2.5) 6.1(3.1) 6.3(3.0) 5.6(3.2) 4.6(0.9)
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Validity and reliability The 16-item VARK was demonstrated to be factorable with a Kaiser–Meyer–Olkin measure of sampling adequacy of 0.86, a meritorious level of intercorrelation among items. The underlying number of factors identified by scree plot suggested one factor, which accounted for 38.9% of total variance. This was confirmed by principal axis factor, which extracted one factor, with all 16 items loading between 0.35 and 0.84. The Cronbach alpha of the VARK was 0.91, none of the alpha values were higher when the item was deleted indicated all 16 items contributed to the VARK scale. Learning preferences As shown in Table 1, 62% of students reported to have more than a single mode of learning preference, that is these students prefer more than a single mode of receiving and presenting learning information. Based on mean scores, the most dominant learning preferences were read/write (mean: 6.3, SD: 3.0) and aural (mean: 6.1, SD: 3.1), followed by kinaesthetic (mean: 5.6, SD: 3.2), and finally, visual (mean: 3.6, SD: 2.5). Comparison of mean VARK scores with language spoken at home When the mean VARK scores in each category were compared with language spoken at home, students who spoke only English at home had higher mean score in two of the four categories of VARK sensory modalities, which were visual (English-speaking: mean 4.7, SD: 3.0 versus non-English-speaking: mean: 3.3. SD: 2.3, P = 0.037) and kinaesthetic (English-speaking: mean 6.4, SD: 2.6 versus nonEnglish-speaking: mean: 5.2. SD: 3.4, P = 0.040). Predictors of academic performance at the 6-month follow-up The results of stepwise multiple regression of VARK learning preference categories, controlling for age, international student status, PAC and ELAS score indicated that kinesthetic sensory mode was a positive and significant predictor of academic performance at the 6month follow-up in graduate entry nursing students (Table 2). None of the five variables (age, international student status, PAC and ELAS score), which have been previously shown to be associated with academic performance, were found to be significant predictors of academic performance in this study sample. Discussion Two major finding in this study that have not been reported before are that the kinaesthetic sensory mode predicted academic performance (Table 2) and that students who spoke only English at home had higher mean scores across two of the four categories of VARK sensory modalities, visual and kinaesthetic. The finding that the kinaesthetic sensory mode was a positive and significant predictor of academic performance at the 6-month followup in the study cohort was interesting. It may be the case that early Table 2 Multiple regression model predicting students' GPA at 6 months following course commencement. Variable
β
t
Grade Point Average scores at six months of course commencement Age − 0.34 − 1.98 International student (Yes) − 0.07 − 0.39 Perceived academic control 0.01 0.08 English language acculturation scale (ELAS) score − 0.31 − 1.78 VARK score: Kinaesthetic 0.39 2.73 Overall model: R2 = 0.260, F(df) = 2.947 (5, 47), P = 0.023, adjusted R2 = 0.17.
p 0.055 0.699 0.934 0.082 0.009
educational experience in different countries underpins subsequent learning preferences and given the high percentage of international students in the cohort (85%), a possible explanation could be that their previous tertiary study was in a more didactic setting, where teaching was more instructive in style, less interactive and learning passive, whereas the nursing course in Australia is more clinically based and encourages ‘doing’, questioning, discussion and reflection which would favour students with kinesthetic sensory modality. Students with this learning preference take in information best through practical sessions, case studies or computer simulations. They process the information by recalling their experiences, and need to actually do things before they are able to fully understand the concepts. Although the 6-month follow-up may be considered to be premature, one may speculate that performance at this time point is likely to be predictive of ongoing academic success. It was a somewhat surprising finding that the read/write sensory modality was not a significant predictor of academic performance, as some of the assessments undertaken by the students during the first 6 months of their course and in their previous degrees would have been written. However, the most likely explanation is that this learning preference is very high in the group anyway and hence a ceiling effect was achieved. A ceiling effect occurs when a high proportion of the respondents score the highest possible score (Twisk and Rijmen, 2009). Thus the instrument will lack sensitivity at high levels, being incapable of achieving adequate discrimination in measuring the true construct range (McHorney and Tarlov, 1995) Alternatively, it could be argued that students with ESL will compensate for a lack of language competence/confidence and, in so doing, not have a preference for read/write learning, hence the preference for kinaesthetic sensory mode. That the kinaesthetic modality predicts academic performances leads to the question ‘can learning preferences change?’ It has been shown that undergraduate learning preferences are susceptible to change over time (Kell and van Deursen, 2002), affected by curricular factors, and thus, if students were given a more hands on approach to learning, this may encourage change and enhance success. This susceptibility to change also impacts on future curriculum development. A VARK questionnaire follow-up at 12 months in the present study would have confirmed the durability of the learning preference once language skills were more established. Comparison of students' mean VARK scores with language spoken at home was also an interesting finding in that the mean score across two of the four categories of VARK sensory modalities, (visual and kinaesthetic), were lower in students with ESL, when compared with native English-speaking students. It has been shown that the language spoken at home is a proxy for language proficiency (Salamonson et al., 2007). When looking at the universal diverse orientation among nursing students, specifically the ‘comfort with differences’ dimension or the sense of connectedness with others, it was found that students who never spoke English at home had low scores in this dimension. However, the score increased as the years of residence in Australia increased, possibly as they become more acculturated with the Australian lifestyle, e.g., customs and values, and their English language proficiency improved. Similarly, it would be informative to administer the VARK questionnaire at the end of the accelerated course to see if, as the English language proficiency improves, the mean VARK mean scores or learning preference modalities profile changes. Although resource intensive, it would be interesting to compare the mean scores of the learning preference modalities when students with ESL completed the VARK questionnaire in their native language. However, as every aspect of the nursing course is completed in English, it can be seen that the importance of English language proficiency is paramount. Further research is needed to see if students with ESL improve English language proficiency more quickly if learning resources were provided in their preferred modality.
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Although the VARK questionnaire has not been used to compare with academic performance, it been used to look at the learning preferences of first year medical students in Michigan, United States of America (Lujan and DiCarlo, 2006), third year nursing students in Jordan (Alkhasawneh et al., 2008), first year nursing students in Australia (Meehan-Andrews, 2009) and medical students in Turkey (Isman and Gundogan, 2009). Consistent with our results where 62% of the students have more than a single mode of learning preference, three of the studies also reported this, ranging from 58 to 78%, although only 46% in the Meehan-Andrews study were multimodal. The multimodal preference is consistent with characteristics of adult learners, students being more flexible in taking in information than those who use a single modality (Fleming, 2007). It also has implications for teachers to use more multimodal learning resources and assessments. The most common unimodal preference for the medical students and the nursing students in the Meehan-Andrews study was kinaesthetic mode, whereas the unimodal preference of the students in this study and the Jordanian nursing students was read/ write. It is worth noting however, that as most assessment formats favour the student with the read/write learning preference, further research is needed to see if this disadvantages students with other learning preferences. Knowing their particular learning preferences (s) should encourage student self-development and further research is needed to see if students acted on the feedback received with their VARK scores and whether they perceived it to be effective. Based on their profile, the feedback suggested ways to help them to learn. However, knowing the learning preferences of accelerated graduate entry nursing students may provide the impetus to develop more multisensory resources and assignments, a more hands on approach, more web-based learning and computer simulation case studies (Kell and van Deursen, 2002; Walker et al., 2007) and possibly streamline or even shorten further the already shortened curriculum to help address the nursing shortage. Language proficiency was also measured by the ELAS. In this study, 85% were international students and the mean ELAS score of the total group was at the upper end of the low group (5–13). This is not surprising as many of them were new arrivals to Australia who would be expected to have lower scores. When comparing this group of accelerated graduate entry nursing students (12.8, SD 4) with students enrolled in a traditional undergraduate nursing course, the mean score was higher (16.42, SD 4.83) in the traditional nursing students, this being in the medium group (14–18) (Salamonson et al., 2008). Although the grade point average was not recorded in the Salamonson et al. study, they showed that students in the lower group (with lower language acculturation) did less well in all first year subjects. That this positive association between ELAS scores and academic performance was not shown to be a predictor of academic performance among the graduate entry nursing students is of note, particularly as many of these students had low levels of English language acculturation. A possible explanation for this could be that the graduate students have previous experience of academia and have already developed the skills to help their performance. It may also be that the first year structure of the traditional and accelerated courses are different and fewer written assessment items may advantage the graduate entry students, but this warrants further investigation. Similarly, in this study sample, age and PAC were not shown to be predictors of academic performance. Most studies have shown that mature-age students achieve better grades than younger undergraduate nursing students (Houltram, 1996; Kevern et al., 1999; McCarey et al., 2007; Ofori, 2000; Salamonson and Andrew, 2006; van Rooyen et al., 2006). Korvick et al. (2008) and Carty et al. (2007) found, as in this study, that age was not associated with academic performance in accelerated graduate entry nursing students. Similarly, although Perry et al. (2005) found that students higher in academic control obtained higher GPAs, in a study examining the links between PAC and academic comparative optimism, the positive expectations about
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future performance in undergraduate psychology students, it was shown that PAC did not predict performance (Ruthig et al., 2009). There are limitations that need to be considered in our study. Firstly, the study sample of 61 is a small number, although of the total cohort, this represented as a response rate of 91%. The veracity of subgroup analysis is also limited by the small sample size and requires further replication. Also, as the majority of the sample was international students, this may influence the external validity of the findings. Further exploration of the findings of this study is warranted. A follow-up administration of the VARK questionnaire at 12 months may have confirmed the durability of the learning preference once the language skills of the students were more established. Secondly, that the VARK questionnaire lacked statistical validation (Fleming and Baume, 2006), but despite this, its strength is that questions are developed from real life situations and students identify with the results that they are given— that is, they affirm the face validity of the instrument. Fleming and Baume (2006), when developing the instrument, decided against testing for construct validity as the original intention of the instrument was to “...stimulate reflection and discussion.”(p.139). Leite et al. (2010) analysed 15,137 completed questionnaires delivered over the internet and found, for teaching and learning purposes, the VARK is an appropriate instrument to use yet caution its use in research due to the above discussed lack of validity testing. Despite these limitations, these data provide a useful contribution to exploring the relationship between learning preferences and academic performance, particularly in an accelerated graduate nursing program. In terms of curriculum development, incorporating more practically based activities may be an important innovation. Conclusion In graduate entry accelerated students we found that the kinaesthetic sensory mode was a predictor of academic performance at 6-months. Although the majority of these students were international students, many of whom were in the low English language acculturation group, the academic performance of this cohort was better than nursing students undertaking a traditional course. This may have been that their previous tertiary study resulted in skills that enabled them to overcome this disadvantage but requires further research. References Abriam-Yago, K., Yoder, M., Kataoka-Yahiro, M., 1999. The Cummins model: a framework for teaching nursing students for whom English is a second language. Journal of Transcultural Nursing 10 (2), 143–149. Aktan, N.M., Bareford, C.G., Bliss, J.B., Connolly, K., DeYoung, S., Lancellotti Sullivan, K., et al., 2009. Comparison of outcomes in a traditional versus accelerated nursing curriculum. International Journal of Nursing Education Scholarship 6 (1), 1–11. Alkhasawneh, I.M., Mrayyan, M.T., Docherty, C., Alashram, S., Yousef, H.Y., 2008. Problem-based learning (PBL): assessing students' learning preferences using VARK. Nurse Education Today 28 (5), 572–579. Anderson, P., Reberger, H., Doube, L., 2004. Language proficiency and academic outcomes for international postgraduate students at the University of AdelaideRetrieved May 2, 2010, from http://www.adelaide.edu.au/graduatecentre/redc/ redc/Item5_08_15C.pdf2004. Carty, R.M., Redmond, G.M., 1991. An analysis of the process for the development of a career option in nursing for college graduates. Journal of Nursing Education 30 (7), 330–332. Carty, R.M., Hale, J.F., Carty, G.M., Williams, J., Rigney, D., Principato, J.J., 1998. Teaching international nursing students: challenges and strategies. Journal of Professional Nursing 14 (1), 34–42. Carty, R.M., Moss, M.M., Al-zayyer, W., Kowitlawakul, Y., Arietti, L., 2007. Predictors of success for Saudi Arabian students enrolled in an accelerated baccalaureate degree program in nursing in the United States. Journal of Professional Nursing 23 (5), 301–308. Coffield, F., Moseley, D., Hall, E., Ecclestone, K., 2004a. Should we be using learning styles? What research has to say to practice. London: The Learning and Skills Research Centre. Coffield, F., Moseley, D., Hall, E., Ecclestone, K., 2004b. Learning styles and pedagogy in post-16 learning: a systematic and critical review. London: The Learning and Skills Research Centre.
616
J. Koch et al. / Nurse Education Today 31 (2011) 611–616
Desmedt, E., Valcke, M., 2003. Learning style awareness: why would it work? Paper presented at the Proceedings of the Eighth Annual European Learning Styles Information Network Conference, University of Hull, UK. Faul, F., Erdfelder, E., Lang, A.-G., Buchner, A., 2007. G*Power3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods 39 (2), 175–191. Fielding, M., 1994. Valuing difference in teachers and learners: building on Kolb's learning styles to develop a language of teaching and learning. Curriculum Journal 5 (3), 393–417. Fleming, N., 2007. Frequently asked questions: VARK a guide to learning stylesRetrieved May 2, 2009, from http://www.vark-learn.com/english/page.asp? p=faq2007. Fleming, N., Baume, D., 2006. Learning styles again: VARKing up the right tree! Educational Developments, SEDA 7.4, 4–7. Fleming, N.D., Mills, C., 1992. Not another inventory, rather a catalyst for reflection. To Improve the Academy 11, 137–149. Houltram, B., 1996. Entry age, entry mode and academic performance on a Project 2000 common foundation programme. Journal of Advanced Nursing 23 (6), 1089–1097. Howard-Jones, P., 2009. Neuroscience and Education: Issues and Opportunities. Economic and Social Research Council, London. Isman, C.A., Gundogan, N.U., 2009. The influence of digit ratio on the gender difference in learning style preferences. Personality and Individual Differences 46 (4), 424–427. Kell, C., van Deursen, R., 2002. Student learning preferences reflect curricular change. Medical Teacher 24 (1), 32–40. Kevern, J., Ricketts, C., Webb, C., 1999. Pre-registration diploma students: a quantitative study of entry characteristics and course outcomes. Journal of Advanced Nursing 30 (4), 785–795. Korvick, L.M., Wisener, L.K., Loftis, L.A., Williamson, M.L., 2008. Comparing the academic performance of students in traditional and second-degree baccalaureate programs. Journal of Nursing Education 47 (3), 139–141. Leite, W.L., Svinicki, M., Shi, Y., 2010. Attempted validation of the scores of the VARK: learning styles inventory with multitrait-multimethod confirmatory factor analysis models. Educational and Psychological Measurement 70, 323–340. Lujan, H.L., DiCarlo, S.E., 2006. First-year medical students prefer multiple learning styles. Advances in Physiology Education 30 (1), 13–16. Marin, G., Sabogal, F., Vanoss Marin, B., Otero-Sabogal, F., Perez-Stable, E.J., 1987. Development of a short acculturation scale for Hispanics. Hispanic Journal of Behavioral Sciences 9 (2), 183–205. McCarey, M., Barr, T., Rattray, J., 2007. Predictors of academic performance in a cohort of pre-registration nursing students. Nurse Education Today 27 (4), 357–364. McHorney, C.A., Tarlov, A.R., 1995. Individual-patient monitoring in clinical practice: are available health status surveys adequate? Quality of Life Research 4 (4), 293–307. Meehan-Andrews, T.A., 2009. Teaching mode efficiency and learning preferences of first year nursing students. Nurse Education Today 29 (1), 24–32. Norman, G., 2009. When will learning style go out of style? Advances in Health and Science Education 14, 1–4.
Ofori, R., 2000. Age and ‘type’ of domain specific entry qualifications as predictors of student nurses' performance in biological, social and behavioural sciences in nursing assessments. Nurse Education Today 20 (4), 298–310. Perry, R.P., Hladkyj, S., Pekrun, R.H., Pelletier, S.T., 2001. Academic control and action control in the achievement of college students: a longitudinal field study. Journal of Educational Psychology 93 (4), 776–789. Perry, R.P., Hladkyj, S., Pekrun, R.H., Clifton, R.A., Chipperfield, J.G., 2005. Perceived academic control and failure in college students: a three-year study of scholastic attainment. Research in Higher Education 46 (5), 535–569. Raines, D.A., Sipes, A., 2007. One year later: reflections and work activities of accelerated second-degree bachelor of science in nursing graduates. Journal of Professional Nursing 23 (6), 329–334. Ruthig, J.C., Hanson, B.L., Marino, J.M., 2009. A three-phase examination of academic comparative optimism and perceived academic control. Learning and Individual Differences 19 (4), 435–439. Salamonson, Y., Andrew, S., 2006. Academic performance in nursing students: influence of part-time employment, age and ethnicity. Journal of Advanced Nursing 55 (3), 342–349. Salamonson, Y., Everett, B., Andrew, S., Koch, J., Davidson, P.M., 2007. Differences in universal diverse orientation among nursing students in Australia. Nursing Outlook 55 (6), 296–302. Salamonson, Y., Everett, B., Koch, J., Andrew, S., Davidson, P.M., 2008. English-language acculturation predicts academic performance in nursing students who speak English as a second language. Research in Nursing & Health 31 (1), 86–94. Salamonson, Y., Koch, J., Weaver, R., Everett, B., Jackson, D., 2010. Embedded academic writing support for nursing students with English as a second language. Journal of Advanced Nursing 66 (2), 413–421. Sanner, S., Wilson, A., 2008. The experiences of students with English as a second language in a baccalaureate nursing program. Nurse Education Today 28 (7), 807–813. Seldomridge, L.A., DiBartolo, M.C., 2005. A profile of accelerated second bachelor's degree nursing students. Nurse Educator 30 (2), 65–68. Twisk, J., Rijmen, F., 2009. Longitudinal tobit regression: a new approach to analyze outcome variables with floor or ceiling effects. Journal of Clinical Epidemiology 62 (9), 953–958. van Rooyen, P., Dixon, A., Dixon, G., Wells, C., 2006. Entry criteria as predictor of performance in an undergraduate nursing degree programme. Nurse Education Today 26 (7), 593–600. Walker, J.T., Martin, T.M., Haynie, L., Norwood, A., White, J., Grant, L., 2007. Preferences for teaching methods in a baccalaureate nursing program: how second-degree and traditional students differ. Nursing Education Perspectives 28 (5), 246–250. Wink, D.M., 2005. Accelerated nursing programs for non-nursing college graduates. In Annual Review of Nursing Education 3, 271–297. Youssef, F.A., Goodrich, N., 1996. Accelerated versus traditional nursing students: a comparison of stress, critical thinking ability and performance. International Journal of Nursing Studies 33 (1), 76–82.