Sage Journals: Discover world-class research

Abstract

With an increasing number of students enrolling into Australian universities, developing an understanding of the variables underlying academic success, is growing in importance. In the present study, three first-year cohorts (2013–2015) studying towards five allied health or science degrees (n = 1,676 students) were examined to ascertain the impact of students’ entrance pathways and demographic background on academic performance and retention. Results identified gender and the Australian Tertiary Admissions Rank as significant predictors of academic performance, with female students (p < .05) and students entering with the highest quartile of Australian Tertiary Admissions Rank score (p < .001) performing strongest. Results also revealed that students from government secondary schools performed significantly higher in terms of first year grade point average (p < .05) compared with students from non-government schools and that secondary school tuition fees correlated positively with ATAR score but negatively with first year GPA (p < .05). The findings from this study have implications in the formulation of education policy, particularly highlighting the strength of students from government schools transitioning to university science and allied health degrees.

Keywords

First year students grade point average health sciences allied health education retention (of students)science education academic success

Introduction

With continuously growing numbers of students entering higher education, university educators, administrators and policy makers are faced with a greater focus towards identifying academic achievement, and limiting student attrition. In Australia, courses relating to science and broader aspects of health appear to have been at the forefront of Federal Government policy in recent years. For example successive previous Labour governments made efforts to promote allied health and medical research through significant infrastructure and grant-based investment schemes (Australian Bureau of Statistics, 2015). With this in mind, allied health and medical science-related sectors within Australia have steadily become a popular career path for prospective undergraduate students, with employment in the allied health sector alone projected to rise by 24.9% by 2018 (2014). Therefore, elucidating the demographic, psychological and social variables surrounding success in science education is becoming increasingly important within Australia.

Admission into Australian universities is typically from two distinct and continuously expanding pathways. The direct feeding from secondary education systems, through the completion of year 12 based certificates, is the common path adopted by the majority of undergraduate students. In Western Australia, the secondary education system has undergone multiple curricula transformations throughout the past decade, allowing students a broader choice in the selection of their subjects. Consequently, increased freedom in the choice of Australian Tertiary Admissions Rank (ATAR) subjects has led to a decline in enrolments in secondary science subjects, a phenomenon previously observed at the national level, with declines in biological sciences (10%) and chemistry (5%) over the period from 1992 to 2012 (Kennedy, Lyons, & Quinn, 2014). Likewise, students entering university from a non-direct alternative pathway (mature age or Technical and Further Education (TAFE)) may not have been exposed to any or little previous science curricula/study for a long period of time. For this reason, previous study in related ATAR science subjects have previously been shown as significant predictors for success in first year health science (Anderton, Evans, & Chivers, 2016).

Performance in high school has frequently been used as a benchmark for university performance. Studies throughout the world demonstrate that grade point average (GPA) in secondary education correlates to attrition at university (Battin-Pearson et al., 2000), university grades (Richardson, Abraham, & Bond, 2012), employment performance (Carr, Celenza, Puddey, & Lake, 2014), salary (Roth & Clarke, 1998), and future socio-economic status (Fischbach, Baudson, Preckel, Martin, & Brunner, 2013). However, GPA itself is heavily influenced by a number of factors, including parental education status, which itself can impact on household socio-economic status (SES), and the child’s subsequent educational facility (Crede, Wirthwein, McElvany, & Steinmayr, 2015). One of the frequently discussed pre-entry variables affecting university performance is the type of secondary education which in Australia is broadly classified as government and non-government. Australian non-government schools receive government funding, thereby contributing to the enhanced quality of facilities, resources and programs available to students attending non-government schools (Vickers, 2005). In recent times, non-government schools have become more affordable, attainable and popular, leading to a decline in confidence and enrolments in government schools (Cahill & Gray, 2010). Government schools in Western Australia continue to dominate the student intake from lower socioeconomic areas (Lamb & Teese, 2012), and struggle to compete with non-government school populations, when using median ATAR as a determinant for academic performance (Better Education, 2016).

As allied health and science enrolments at university increase, identifying additional demographic predictors of success and/or attrition will become increasingly important. A gender disparity in Australian schools stills exists, with National Assessment Program – Literacy and Numeracy (NAPLAN) results supporting the common stereotype that numeracy and literacy are male and female dominated domains, respectively (Thomson, De Bortoli, & Buckley, 2013). Other gender disparities exist at the higher education level with evidence indicating that female undergraduate students out-perform their male counterparts in health-related (Anderton et al., 2016; Mills, Heyworth, Rosenwax, Carr, & Rosenberg, 2008) and nursing degrees within Australian and international universities (Wan Chik et al., 2012). SES has also been shown to influence primary and secondary education outcomes in a number of education systems (Sirin, 2005). However, once at university, mixed results around the impact of SES have been reported, with a recent study demonstrating students from lower SES areas and less prestigious schools perform better (Li & Dockery, 2014; Puddey & Mercer, 2014). The lower rates of university participation among young people from lower socio-economic backgrounds have been attributed to several factors, including the cost of undertaking university study, lower educational aspirations, and a lack of awareness of the benefits tertiary education can provide (Bradley et al., 2008).

Therefore, this study sought to identify whether demographic predictors contribute to first year GPA and/or attrition amongst students in allied health and science degrees. Given the importance of allied health and science industries within Australia, elucidating the determinant of academic success or attrition during university education will have significant benefit to the broader sector, and provide a better understanding of how education policy can be adapted. In this study, variables such as gender, university entrance pathway, socio-economic area indexation and academic performance during the first year of university, were all considered. The main outcome of academic performance was measured in the form of GPA at the completion of first year, and used as the standard benchmark for university success. Based on previous literature, it would be hypothesised that students entering from suburbs indexed with lower area socio-economic indexation percentiles, as non-school-leaver, or public secondary high school educated, would perform poorer in their first year of university than those not possessing the aforementioned variables.

Methods

Participants

Study participants were first year students enrolled in a total of six degrees, including five allied health degrees, namely biomedical science, exercise and sports science, health and physical education, physiotherapy, nursing, and one general science degrees at the University of Notre Dame Australia from 2013 to 2015. Table 1 shows the number of participants, by degree, cohort and gender.

Table 1.

Number of male and female study participants by degree and cohort (2013–2015).

	2013 (n)		2014 (n)		2015 (n)
Degree	Males	Females	Males	Females	Males	Females
Biomedical science	16	36	19	23	16	30
Exercise and sports science	57	49	52	42	29	42
Health and physical education	40	38	25	47	33	32
Nursing	15	195	21	224	39	257
Physiotherapy	22	58	28	59	29	55
Science	7	9	6	11	8	7
Total (n)	157 (29%)	385 (71%)	151 (27.1%)	406 (72.1%)	154 (26.7%)	423 (73.3%)

Table 2.

Descriptive statistics on each of the variables used in the analysis.

Variable	Mean (SD) or n
Gender (n)
Male	462
Female	1214
SEIFA percentile	81.90 (20.90)
Mode of entrance
Direct	736
Alternative	848
School type
Government	126
Non-government	608
ATAR score^a	80.54 (11.77)
Tuition fees (AUD)	10,366 (8777)
First year GPA	2.00 (0.78)

Only students entering directly from secondary school with a valid ATAR score.

Data

Data were obtained for each participant from university records on student gender, age, pre-university admission data and mode of university entrance. Each participant was then classified has having entered university using either of two pathways: Participants who were direct school leavers and participants who were non-direct school leavers – the latter group consisting of alternative entry and mature age students.

Students were classified as school leavers on obtaining entry into university with a valid ATAR score. Students were classified as non-direct school leavers, if they had completed previously a semester at a tertiary institution, held a TAFE certification, and/or had completed a foundation year or tertiary enabling programs. Approval for the study in general and to obtain specific student academic and demographic data, such as pre-entry and residential postcode, was granted by the university human research and ethics committee.

Assessment of SES

The listed residential postcode of students were categorised according to the 2011 Australian Bureau of Statistics’ socio-economic indexes for areas (Australian Bureau of Statistics, 2013). This study used the index of relative socio-economic disadvantage as an indicator of the prevalence of disadvantaged people within a given geographical region. Postcodes were assigned a value of 1–100 corresponding to the Western Australian Socio-Economic Indexes for Areas (SEIFA) percentile for that postcode. To visualise SEIFA relative to first year GPA, a choropleth map of the Perth metropolitan region was created (see Figure 2). Maps were created using QGIS software (version 2.8.2), a free cross-platform geographic information system.

School fees and ATAR performance

For students entering directly from secondary school (n = 736), pre-university admission data relating to the secondary school (ATAR score) was obtained from university records. Data related to public and private school fees was accessed through the individual school website or by direct communication with the specific school. A variable indicating school fees was used as a proxy for family’s financial situation and parental education which, as shown in the literature review tends to be related to school performance. When used in a general linear model (GLM), tuition fees were displayed as quartiles (low, medium, high, highest).

First year academic performance measures

GPA at the completion of first year was used as an indicator of participants’ academic performance. Full-time students completing a standard enrolment within their degree for the first time (rather than subsequently repeating a unit) were included in mean GPA calculations for each year cohort. A student’s GPA is calculated by awarding for each unit, a “0” for a Fail (i.e. < 50%), a “1” for a Pass, (i.e. 50%–59%), a “2” for a Credit (i.e. 60%–69%), a “3” for a (Distinction (i.e.70%–79%) or a “4” for a High Distinction (i.e.80%–100%).

Student attrition (withdrawal)

Student attrition (withdrawal from university) can be broadly defined as the number of students commencing a university study, minus the number completing the degree. In this study, student attrition was defined as a student commencing/enrolling, completing a semester, but not completing the first year of their chosen university degree.

Data analysis

Data analysis was conducted using SPSS version 22 (IBM corporation). To assess whether pre-entry variables affect university performance (GPA), independent t-tests were used for group comparisons of gender, mode of entrance and the type of school. Pearson correlation coefficients were calculated to examine the relationship between SES and ATAR score, with the university academic performance measure of GPA. Likewise, for non-parametric analysis, Spearman Rho correlation coefficients were used to assess the relationship between school tuition fees and GPA. The effect sizes (ES) of the correlation coefficients were evaluated in line with Cohen (1999) with values of r = 0.10 considered small, 0.30 considered medium and 0.50 regarded as large. A significant nominal p-value of < .05 was employed.

Cohen’s d ES were calculated for the mean differences, with an ES of 0.20 considered small, 0.50 medium and 0.80 large (see Cohen, 1999). A GLM or logistic regression model was used to examine factors predictive of first year academic performance, and first year attrition, respectively, from the 2013–2015 combined cohorts. The pre-entry variables used in the models included gender, SEIFA, school tuition fees, ATAR score and mode of university entrance. Non-significant factors were removed one at a time until the final model was determined.

Results

Female students perform at a higher level than male students in first year GPA

Data from 2013 to 2015 identifies a disproportional number of females to males within each year cohort. As such, female students outnumber male students in all but one of the degrees chosen for this study (Table 1). Overall, female students performed significantly better at the completion of first year allied health and science degrees than male students (Figure 1; p < .001; d = .26), with all but one year cohort reaching statistical significance.

Figure 1.

Analysis of gender and as a contributing variable for success in first year university science and health-related degrees. Values are mean ± SE; *p < .05; ***p < .001.

Figure 2.

A choropleth map representation of (a) Socio-Economic Indexes for Areas (SEIFA) in the Perth metropolitan area compared to (b) mean first year GPA by residential postcode.

SES weakly correlates with student academic performance

To examine whether or not SES was related to students’ academic performance within our sample, SEIFA percentile, based on students’ residential postcodes, were correlated with first year GPA. While results showed no correlation for the 2013 cohort between SES and GPA, an association emerged for the 2014 (r = .096, p = .04) and 2015 cohorts (r = .104, p = .03) (Table 3). A significant correlation between SES and GPA also emerged when the correlation was calculated using the three cohorts combined (p = .001), however it should be noted that the correlation strength is very weak (r = .091).

Table 3.

Assessment of correlation between relative SEIFA measures of socio-economic disadvantage and first year GPA.

	2013 (n = 386)	2014 (n = 466)	2015 (n = 468)	Combined (n = 1320)
Pearson correlation (r value)	.073	.096	.104	.091
Significance (two-tailed)	.155	.039	.025	.001

*p < .05 for all of the tables queried.

To illustrate better the relationship between SES and first year GPA, a choroplethic map of the Perth metropolitan area was created. The map illustrates mean first year GPA of the combined three cohorts used in this study, alongside the Australian Bureau of Statistics measure of socio-economic indexes for areas (Figure 2) which was the SES measure used in the analyses. As can be seen in the choropleth map, mean GPA distribution appears relatively sporadic, with punctate areas of higher GPA performance in more densely populated metropolitan suburbs of Perth (Figure 2(a)). Supporting the analysis in Table 3, when visualising SEIFA data (Figure 2(b)) there were no apparent broad areas of similarity/overlap with higher GPA in Figure 2(a).

Does the mode of university entrance and/or secondary schooling environment affect performance in first year?

While SES did not appear to be strongly related to the overall success of the student cohorts in first year, pre-entry factors such as mode of entrance and/or schooling environment may be important contributors. When determining if mode of university entrance could play a role in first year academic performance, school leavers were compared with students entering from alternative or mature age entry pathways. The overall mean first-year GPA of school leavers (M = 2.04, SD = .69) and alternative entry students (M = 1.95, SD = .84) were similar (p = .066), with mode of entrance not reaching statistical significance in any cohort (Figure 3).

Figure 3.

Analysis of the mode of entrance as a contributing variable for success in first year university allied health and science-related degrees. Values are mean ± SE.

As academic performance in allied health and science degrees did not seem to be related to mode of university entrance, school-leaver entrants were explored further, as they present a much more homogenous subset of students. Of these students, ATAR score showed a moderate positive correlation to student GPA (r = .518, p < .001) at the completion of first year (Table 4). As illustrated in Table 4, there are subtle differences in the strength of relationships between ATAR and first year GPA in each of the degrees studied. For example students in Nursing, Health and Physical Education and Biomedical Science degrees show a consistent moderate correlation between their ATAR score and first year GPA. In contrast, in first-year physiotherapy, with the exception of one year (i.e. 2014), no significant relationship emerged between ATAR and university performance.

Table 4.

Pearson correlation between ATAR and first year GPA.

Degree	2013 (n = 211)	2014 (n = 219)	2015 (n = 204)	Combined (n = 634)
Biomedical science (r value)	.659***	.646**	.774***	.460***
BESS Exercise and sports science (r value)	.389**	.516**	.469*	.179
Health and physical education PE (r value)	.652***	.530*	.781***	.504***
Nursing (r value)	.571***	.617***	.686***	.414***
Physiotherapy (r value)	.046	.523**	.252	.117
Science (r value)	.955*	.808	-.826	.451
Total (r value)	.474***	.523***	.562***	.518***

p < .05; **p < .01; ***p < .001.

Does the type of secondary education really matter?

In determining whether public or private schooling environments contribute to first year tertiary performance, the secondary institution was categorised as government or non-government (Catholic and Independent). The University of Notre Dame, a private university, typically has a larger portion of students entering directly from non-government secondary education institutions, when compared to public universities in Western Australia. Students entering university from a government secondary school performed significantly better in 2013 (p = .004; d = .6) and 2014 (p = .047, d = .36) at the completion of first year, when compared to those entering from a non-government secondary school (Figure 4). Overall, school type was a significant contributor, with first-year students entering university from a government secondary school having a mean GPA of 2.28, when compared to 1.99 for students entering from a non-government secondary school (p ≤ .001, d = .44).

Figure 4.

The effect of the secondary school type, government or non-government, on first year GPA in university science and allied health degrees. Values are mean ± SE; * p < .05; **p < .01; ***p < .001.

Tuition fees for year 12 education correlate with ATAR and first year university performance

As a disparity in GPA between government and non-government school entrants appeared to be present, and socio-economic indexation was not a major predictor, the monetary value of education at the secondary institution was explored further. Tuition costs were investigated in relation to ATAR score and first year GPA (Table 5). Overall, and in all three cohorts (2013–2015), a significant (p < .05) positive relationship between tuition fees and ATAR was observed (p < .001, r_s = .169). However, in the 2014 (p = .002, r_s = .200) and 2015 (p = .031, r_s = .158) cohorts, a significant negative relationship was observed between tuition fees and a greater first year GPA (Table 5; p < .001, r_s = −.183).

Table 5.

Correlation between school fees and first year GPA.

	2013	2014	2015	Combined
Tuition fees/ATAR (n)	189	241	216	646
Spearman’s rho correlation (r_s value)	.241**	.129*	.179**	.169**
Significance (two-tailed)	.001	.047	.008	<.001
Tuition fees/GPA (n)	194	233	188	615
Spearman’s rho correlation (r_s value)	−.127	−.200**	−.158*	−.183**
Significance (two-tailed)	.077	.002	.031	<.001

Can demographic and pre-entry variables predict first year GPA?

To ascertain whether or not these identified variables play an overall predictive role for success in first year allied health and science degrees, a GLM was used to identify significant predictors (Table 6). The results show female students are predicted to score .227 better in GPA than male students (p < .001), at the completion of first year. Results from the GLM showed students entering university with a higher ATAR score are predicted to achieve a significantly higher GPA (p < .001). For example a student entering directly from school with an ATAR score of 90 would perform .74 better in GPA at the completion of first year, when compared to a similar student with an ATAR score of 70. While SES was not a significant predictor of first year GPA, school fees appeared to be a significant predictor for students entering directly from secondary school. The lowest (p < .001) and medium (p < .001) tuition fee quartiles were predicted to perform .469 and .343, respectively, better than the highest quartile of tuition fees (Table 6). However, the two highest tuition fee quartiles did not show a statistical difference in predicting GPA.

Table 6.

Final model parameter estimates: predictors of academic performance (first year GPA).

Variable	β coefficient	Standard error	Significance
(Intercept)	−1.364	.069	<.001
Gender
Female	.227	.055	<.001
Male	0^a
School fees
Low	.469	.067	<.001
Medium	.343	.066	<.001
High	.073	.066	.227
Highest	0^a	.
ATAR	.037	.002	<.001

Comparison category set to zero.

Can first year attrition in allied health and science courses be predicted?

In the present cohort, the mean attrition rate across all degree cohorts in first year was 16%. A logistic regression was performed to ascertain the effects of gender, ATAR, mode of entrance and SEIFA on the likelihood that participants will withdraw from university. Males were 1.95 times more likely to withdraw from their studies prior to the completion of first year than female students (Table 7). Increasing ATAR score was also associated with an increased likelihood of remaining in first year university study (p = .042). However, SEIFA and mode of entrance were not statistically significant predictors of student attrition.

Table 7.

Final model predictors of student attrition in first year university using a binary logistic regression analysis.

Variable	β coefficient	Standard error	Exp (B)	Significance
Gender^a	.665	.227	1.95	.003
ATAR	-.018	.009	.98	.042
Constant	-.628	.700	.53	.369

Reference category is female.

Discussion

Science and allied health degrees represent a broad and growing sector within Australia. Therefore, understanding the demographic factors underpinning success in these tertiary education degrees will become increasingly important. One of the key findings from this study was that female students performed significantly better across all cohorts, and were nearly twice as likely to stay in tertiary education following enrolment, compared to their male counterparts. Previous studies have identified females as stronger performers in first year university, specifically in anatomy and physiology (Anderton et al., 2016), medical (Arulampalam, Naylor, & Smith, 2004) and nursing (Pitt, Powis, Levett-Jones, & Hunter, 2012) undergraduate degrees. However, this correlation has not been observed in post-graduate medicine (Puddey & Mercer, 2014), with males achieving greater average marks in the Australian medical entrance test, the Graduate Medical School Admissions Test (GAMSAT) (Mercer, Crotty, Alldridge, Le, & Vele, 2015). While the present findings may not appear surprising, these are the first to illustrate gender specific attrition patterns in Australian health science tertiary education. This trend follows a well-documented pattern of female academic dominance in all subjects, with males only performing better in formative tests for science and math subjects (Voyer & Voyer, 2014). However, despite these results supporting such a trend, this has failed to translate to increased female representation in senior roles in allied health, science and/or academic sectors (Bell, with assistance from Kate O’Halloran, & Yu, 2009). As such, the present findings need to be viewed with caution, as a plethora of confounding variables may take affect following the completion of first year study.

An important question posed by this study was whether area socio-economic indexation had a significant effect on the performance of first-year university students. Recent findings from a Curtin University study showed the SES of the student’s school was a moderate predictor of academic performance, with students entering from lower SES schools performing better (Li & Dockery, 2014). The cohorts used in the present study were unique, in that a larger proportion of students entering/enrolling in this university were from non-government and more affluent areas. Unlike the aforementioned study, the present results showed no convincing correlation to socio-economic indexation, rather showing a very small positive linear relationship (p = .001; r = .091). As illustrated in Figure 2, these results do not appear to show an overwhelming correlation between SEIFA values and first year GPA. One explanation for this is the unequal distribution of students entering this university, and the very small proportion entering from lower socio-economic/outer metropolitan suburbs.

As the findings relating to suburb socio-economic indexation revealed only a subtle correlation to academic performance in first year, the rational question remaining was whether secondary education institutions were significant determinants of first year GPA. In the Western Australian education system, non-government schools consistently perform stronger in the state’s school ATAR rankings. For example 75% or more of the top 20 ATAR ranking schools were non-government, in the years 2011–2015 (School Curriculum and Standards Authority, 2016). In this study, students entering university from a government high school performed significantly better overall in first year allied health and science degrees, when compared to students entering from a non-government schooling background (p < .001). These results are in line with recent findings, which show that students from more affluent areas and secondary education institutes perform worse at university (Li & Dockery, 2014). One possible explanation for the results given in the present study is that the demographic of government high school students entering this university is not typical, perhaps representing a high proportion of independent and ‘gifted’ entry government schools. However, as an ATAR score is an extremely strong predictor of university entrance and retention (Messinis & Sheehan, 2015; Wright, 2015) and non-government schools, typically located in more affluent areas, consistently perform better in ATAR scores, these results appear to be counterintuitive.

From these findings, we explored the discrepancy between government and non-government education systems, focusing on the monetary value of education, and the observed return on tuition fees. In the Perth metropolitan area, tuition fees from government and non-government schools can vary immensely, with government schools having no tuition fees and small voluntary contribution fees for year 12 students (up to $500), and subject specific costs depending on individual students. In contrast, non-government schools range from approximately $2000–$26,000 per annum. Not surprisingly, a significant positive correlation between tuition fees and year 12 ATAR score were observed, consistent with previous statements regarding non-government school rankings (School Curriculum and Standards Authority, 2016). However, a negative correlation in most year cohorts was observed between tuition cost and first year performance in allied health and science degrees (p < .001, r = .155). This is the first time tuition fees have been investigated in such a way, providing evidence that schooling environment can play a significant role in university success. Such results could be due to a student adjustment to pedagogy, reflective of a lack of a highly structured environment commonly observed in more affluent Australian schools, where an ‘immersion effect’ exists (Birch & Miller, 2007; Win & Miller, 2005). This is coupled with the knowledge that a large proportion of students in Australia are not fully prepared for tertiary education, ultimately affecting their motivation to achieve in their studies (McInnes, James, & Hartley, 2000).

Students leaving university courses prematurely results in considerable costs to the student, and university, in both resources and emotional cost, especially in an environment of limited financial and general resources (Stillman, 2009). A plethora of factors have previously been linked to student retention and/or performance, including student demographics, prior educational achievement, psychosocial factors and institutional factors (McKenzie & Schweitzer, 2001; Rienks & Taylor, 2009). In the present study, first year student attrition was found to be associated with gender, with male students nearly twice as likely to prematurely withdraw from science and allied health degrees.

Similar results have been observed across a range of university degrees (Smith, Therry, & Whale, 2012), with reduced female student attrition likely to be due to their higher academic achievement, which itself strongly correlates to student retention. Interestingly, within science disciplines, male students can perceive other males in their class as more knowledgeable than their female counterparts (Grunspan et al., 2016), despite having a higher probability of dropping out (Chen, 2013). Grunspan and colleagues (2016) suggest that this gender bias can influence student confidence and persistence in the STEM discipline, a long-term concern for high achieving female students. In the context of health sciences, male students that perform poorly in first year are more likely to fail academic writing tasks (Hoyne & McNaught, 2013). Introducing a compulsory support course to combat this poor literacy can successfully reduce the failure rate by 50%, providing one potential strategy for reducing male attrition (Hoyne & McNaught, 2013).

Previous studies have shown that students displaying the highest attrition levels were those who had not completed year 11 or 12 or any other prior tertiary study (Rienks & Taylor, 2009). This corresponds to evidence that high school grades and college admissions test scores consistently predict student retention (Li & Dockery, 2014; Richardson et al., 2012). Similar to previous findings, the present study found that a higher ATAR score would reduce the likelihood of student attrition. However, unlike some of the strong correlations observed in previous studies, the present results relating ATAR to student attrition were not as convincing. The relationship between ATAR achievement and first year retention is most strongly observed at the lower ATAR levels, and even more so in students entering from independent schools (Harvey & Burnheim, 2013). Students entering direct from school in the present study all achieved minimum ATAR scores of 70, equating to a score equal to and/or better than 70% of the students within Western Australia (M = 81.9), possibly explaining why ATAR was not as strongly associated with attrition. These results may also be due to the heavy emphasis on pastoral care and student support programs at this university, which positively influences student retention (Zepke & Leach, 2005).

Students from low socio-economic backgrounds typically face additional hurdles in achieving academic success, including reduced family support and access to resources. Consistent with the findings relating to first year GPA, area socio-economic indexation did not influence student attrition. Previous work has demonstrated that students from more educated and affluent families, who can afford university education, are more likely to persist with their degree (Bean & Metzner, 1985). The present results are therefore not that surprising, as intake into this university is skewed towards students from more affluent areas, with a very small proportion entering from suburbs with a lower socio-economic indexation.

In conclusion, this study has identified the demographic variables for the prediction of first year success and/or attrition in allied health and science university degrees. While presenting novel findings on the influence of tuition fees and academic grades, the encouraging results show that university performance may not be predicted solely by SES and/or prestige of the secondary school attended by students. Given the evolving importance of science and innovation within Australia, identifying predictors of academic performance and/or attrition in these fields will contribute towards an improved education delivery.

Footnotes

Acknowledgements

The author would like to acknowledge Miss Tess Evans, Dr Paola Chivers and the Admissions Office for assistance with data collection and analysis.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

Australian Bureau of Statistics. (2013). Census of population and housing: Socio-economic indexes for areas (SEIFA), Australia, 2011. Retrieved from http://www.abs.gov.au/ausstats/abs@.nsf/DetailsPage/2033.0.55.0012011?OpenDocument.

Australian Bureau of Statistics. (2015). Gross Expenditure on R&D (GERD), research and experimental development, businesses, Australian 2013–14 (8104.0). Retrieved from http://www.abs.gov.au/ausstats/abs@.nsf/Latestproducts/8104.0Main%20Features4201314?opendocument&tabname=Summary&prodno=8104.0&issue=2013-14&num=&view.

Anderton

R. S.

Evans

Chivers

P. T.

(2016) Predicting academic success of health science students for first year anatomy and physiology. International Journal of Higher Education 5.

Arulampalam

Naylor

Smith

(2004) Factors affecting the probability of first year medical student dropout in the UK: A logistic analysis for the intake cohorts of 1980–92. Medical Education 38: 492–503. doi:10.1046/j.1365-2929.2004.01815.x.

Battin-Pearson

Newcomb

M. D.

Abbott

R. D.

Hill

K. G.

Catalano

R. F.

Hawkins

J. D.

(2000) Predictors of early high school dropout: A test of five theories. Journal of Educational Psychology 92(3): 568–582. doi:10.1037/0022-0663.92.3.568.

Bean

J. P.

Metzner

B. S.

(1985) A conceptual model of nontraditional undergraduate student attrition. Review of Educational Research 55: 485–540. doi:10.3102/00346543055004485.

Bell, S. (2009). Women in science: Maximising productivity, diversity and innovation. Federation of Australian Scientific & Technological Societies (FASTS). Retrieved from http://hdl.handle.net/11343/28877.

Better Education. (2016). WA School Ranking – 2016. Retrieved from https://bettereducation.com.au/results/WA/wace.aspx.

Birch

E. R.

Miller

P. W.

(2007) The influence of type of high school attended on university performance. Australian Economic Papers 46: 1–17. doi:10.1111/j.1467-8454.2007.00302.x.

10.

Bradley, D., Noonan, P., Nugent, H., & Scales, B. (2008). Review of Australian higher education: Final report. Canberra: Department of Education, Employment and Workplace Relations.

11.

Cahill

Gray

J. R.

(2010) Funding and secondary school choice in Australia: A historical consideration. The Australian Journal of Teacher Education 35: 121–138.

12.

Carr

S. E.

Celenza

Puddey

I. B.

Lake

(2014) Relationships between academic performance of medical students and their workplace performance as junior doctors. BMC Medical Education 14: 1–7. doi:10.1186/1472-6920-14-157.

13.

Chen

(2013) STEM attrition: College students’ paths into and out of STEM fields. Statistical analysis report (NCES 2014-001), Washington, DC: US Department of Education, National Center for Education Statistics.

14.

Crede

Wirthwein

McElvany

Steinmayr

(2015) Adolescents’ academic achievement and life satisfaction: The role of parents’ education. Frontiers in Psychology 6. Retrieved from http://www.frontiersin.org/Journal/Abstract.aspx?s=305&name=developmental_psychology&ART_DOI=10.3389/fpsyg.2015.00052.

15.

Department of employment. (2014). Employment Outlook to November 2018: Australian Government.

16.

Education, D. o. (2014). Employment Outlook to November 2018: Australian Government.

17.

Fischbach

Baudson

T. G.

Preckel

Martin

Brunner

(2013) Do teacher judgments of student intelligence predict life outcomes? Learning and Individual Differences 27: 109–119. doi:10.1016/j.lindif.2013.07.004.

18.

Grunspan

D. Z.

Eddy

S. L.

Brownell

S. E.

Wiggins

B. L.

Crowe

A. J.

Goodreau

S. M.

(2016) Males under-estimate academic performance of their female peers in undergraduate biology classrooms. PLoS One 11: e0148405, doi:10.1371/journal.pone.0148405.

19.

Harvey

Burnheim

(2013) Loosening old school ties: Understanding university achievement and attrition by school type. Professional Voice 9: 29–36.

20.

Hoyne

G. F.

McNaught

(2013) Understanding the psychology of seeking support to increase Health Science student engagement in academic support services. A practice report. The International Journal of the First Year in Higher Education 4: 109–116.

21.

Kennedy

J. P.

Lyons

Quinn

(2014) The continuing decline of science and mathematics enrolments in Australian high schools. Teaching Science 60: 34–46.

22.

Lamb, S. & Teese, R. (2012). Development of a school funding model for Western Australian public schools: Report on funding and options. Centre for Research on Education Systems, University of Melbourne, Victoria.

23.

Dockery

(2014) Socio-economic status of schools and university academic performance: Implications for Australia’s higher education expansion, Perth: National Centre for Student Equity in Higher Education, Curtin UniversityRetrieved from https://www.ncsehe.edu.au/wp-content/uploads/2014/12/Li-and-Dockery-Schools-SES-Final.pdf.

24.

McInnes

James

Hartley

(2000) Trends in the first year experience in Australian universities (No. 00/06), Canberra: Evaluations and Investigations Programme. Higher Education Division, Department of Education, Training and Youth Affairs.

25.

McKenzie

Schweitzer

(2001) Who succeeds at university? Factors predicting academic performance in first year Australian university students. Higher Education Research & Development 20: 21–33. doi:10.1080/07924360120043621.

26.

Mercer

Crotty

Alldridge

Vele

(2015) GAMSAT: A 10-year retrospective overview, with detailed analysis of candidates’ performance in 2014. BMC Medical Education 15: 31, doi:10.1186/s12909-015-0316-3.

27.

Messinis, G., & Sheehan, P. (2015). The academic performance of first year students at Victoria University by entry score and SES, 2009–2013. Report submitted to the National Centre for Student Equity in Higher Education (NCSEHE), Australia. Melbourne, Australia: Victoria Institute of Strategic Economic Studies (VISES), Victoria University.

28.

Mills

Heyworth

Rosenwax

Carr

Rosenberg

(2008) Factors associated with the academic success of first year Health Science students. Advances in Health Sciences Education 14: 205–217. doi:10.1007/s10459-008-9103-9.

29.

Pitt

Powis

Levett-Jones

Hunter

(2012) Factors influencing nursing students’ academic and clinical performance and attrition: An integrative literature review. Nurse Education Today 32: 903–913. doi:10.1016/j.nedt.2012.04.011.

30.

Puddey

I. B.

Mercer

(2014) Predicting academic outcomes in an Australian graduate entry medical programme. BMC Medical Education 14: 31, doi:10.1186/1472-6920-14-31.

31.

Richardson

Abraham

Bond

(2012) Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin 138: 353–387.

32.

Rienks, J., & Taylor, S. (2009). Attrition and academic performance of students identified as at-risk using administrative data alone. Paper presented at First Year in Higher Education Conference.

33.

Roth

P. L.

Clarke

R. L.

(1998) Meta-analyzing the relation between grades and salary. Journal of Vocational Behavior 53: 386–400. doi:10.1006/jvbe.1997.1621.

34.

School Curriculum and Standards Authority. (2016). Year 12 student achievement data and state statistics 2010–2015. Online reports. Retrieved March 22, 2016 from http://www.scsa.wa.edu.au/internet/Publications/Reports/Statistical_Reports/School_Comparison_Statistics/School_comparison_Statistics.

35.

Sirin

S. R.

(2005) Socioeconomic status and academic achievement: a meta-analytic review of research. Review of Educational Research 75: 417–453. doi:10.3102/00346543075003417.

36.

Smith

Therry

Whale

(2012) Developing a model for identifying students at risk of failure in a first year accounting unit. Higher Education Studies 2: 91–102.

37.

Stillman

(2009) Making the case for the importance of student retention. Enrollment Management Journal 3: 76–91.

38.

Thomson

De Bortoli

Buckley

(2013) PISA 2012: How Australia measures up, Camberwell, Australia: ACER.

39.

Vickers

(2005) In the common good: The need for a new approach to funding Australia’s schools. Australian Journal of Education 49: 264–277. doi:10.1177/000494410504900304.

40.

Voyer

S. D.

(2014) Gender differences in scholastic achievement: A meta-analysis. Psychological Bulletin 140: 1174–1204. doi:10.1037/a0036620.

41.

Wan Chik

W. Z.

Salamonson

Everett

Ramjan

L. M.

Attwood

Weaver

Davidson

P. M.

(2012) Gender difference in academic performance of nursing students in a Malaysian university college. International Nursing Review 59: 387–393. doi:10.1111/j.1466-7657.2012.00989.x.

42.

Win

Miller

P. W.

(2005) The effects of individual and school factors on university students’ academic performance. Australian Economic Review 38: 1–18. doi:10.1111/j.1467-8462.2005.00349.x.

43.

Wright

V. J.

(2015) Is ATAR useful for predicting the success of australian students in initial teacher education? Australian Journal of Teacher Education 40: 1–15.

44.

Zepke

Leach

(2005) Integration and adaptation: Approaches to the student retention and achievement puzzle. Active Learning in Higher Education 6: 46–59. doi:10.1177/1469787405049946.

Identifying factors that contribute to academic success in first year allied health and science degrees at an Australian University

Abstract

Keywords

Introduction

Methods

Participants

Data

Assessment of SES

School fees and ATAR performance

First year academic performance measures

Student attrition (withdrawal)

Data analysis

Results

Female students perform at a higher level than male students in first year GPA

SES weakly correlates with student academic performance

Does the mode of university entrance and/or secondary schooling environment affect performance in first year?

Does the type of secondary education really matter?

Tuition fees for year 12 education correlate with ATAR and first year university performance

Can demographic and pre-entry variables predict first year GPA?

Can first year attrition in allied health and science courses be predicted?

Discussion

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

References