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Predicting students' academic performance based on school and socio-demographic characteristics



Students' trajectories into university are often uniquely dependent on school qualifications though these alone are limited as predictors of academic potential. This study endorses this, examining associations between school grades, school type, school performance, socio-economic deprivation, neighbourhood participation, sex and academic achievement at a British university. Consistent with past research, large entry-level differences between students are generally narrowed by final year at university. Students from the most deprived areas performed less well than more affluent students. Asian and black students performed less well than white students. Female students performed better than their male counterparts. Contrasting with past research, though school performance was positively associated with entry grades, students from low-performing schools were more likely to achieve the highest degree classifications. Additionally, independent school students performed less well than comprehensive school students at final year despite entering with higher grades. These variations exemplify how patterns observed nationally may differ between universities.
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Predicting students' academic performance based
on school and socio-demographic characteristics
Tamara Thiele, Alexander Singleton, Daniel Pope & Debbi Stanistreet
To cite this article: Tamara Thiele, Alexander Singleton, Daniel Pope & Debbi Stanistreet
(2016) Predicting students' academic performance based on school and socio-
demographic characteristics, Studies in Higher Education, 41:8, 1424-1446, DOI:
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Predicting studentsacademic performance based on school and
socio-demographic characteristics
Tamara Thiele
*, Alexander Singleton
, Daniel Pope
and Debbi Stanistreet
Department of Psychological Science, University of Liverpool, Eleanor Rathbone Building,
Bedford Street South, Liverpool L69 7ZA, UK;
Department of Geography and Planning,
University of Liverpool, Jane Herdman Building, Liverpool L69 3GP, UK;
Department of
Public Health and Policy, Institute of Psychology, Health and Society, University of
Liverpool, Whelan Building, Liverpool L69 3GB, UK
Studentstrajectories into university are often uniquely dependent on school
qualications though these alone are limited as predictors of academic potential.
This study endorses this, examining associations between school grades, school
type, school performance, socio-economic deprivation, neighbourhood
participation, sex and academic achievement at a British university. Consistent
with past research, large entry-level differences between students are generally
narrowed by nal year at university. Students from the most deprived areas
performed less well than more afuent students. Asian and black students
performed less well than white students. Female students performed better than
their male counterparts. Contrasting with past research, though school
performance was positively associated with entry grades, students from low-
performing schools were more likely to achieve the highest degree classications.
Additionally, independent school students performed less well than comprehensive
school students at nal year despite entering with higher grades. These variations
exemplify how patterns observed nationally may differ between universities.
Keywords: education; attainment; contextual background; inequality
Despite a dramatic increase in higher education (HE) participation in England over the
last half century, the under-representation of students from socio-economically disad-
vantaged backgrounds remains a glaring reality (Blanden and Machin 2004; Breen and
Jonsson 2005; Croxford and Raffe 2013; Haveman and Smeeding 2006; Singleton
2010a). These students are known as Widening Participation (WP) students, who
along with students with disabilities and some ethnic minority groups are currently
under-represented in HE (Gorard 2008; Mason and Sparkes 2002). Differences in
HE participation are largely attributed to the poorer school-level academic qualica-
tions obtained by a large proportion of students within low socio-economic status
(SES) classications and are associated with educational disadvantage (Chowdry
et al. 2013; Steele, Vignoles, and Jenkins 2007; Sutton Trust 2005). Further, research
comparing the academic performance of students from different school types and
© 2014 The Author(s). Published by Taylor & Francis.
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org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
*Corresponding author. Email:
Studies in Higher Education, 2016
Vol. 41, No. 8, 14241446,
backgrounds in HE suggests that school qualications do not necessarily represent true
academic potential(Hoare and Johnston 2010; Ogg, Zimdars, and Heath 2009; Peers
and Johnston 1994; Zimdars 2007). In particular, the nding that students from inde-
pendent schools tend to enter university with higher grades than students from (non-
fee paying) state schools but perform less well once at university when entry grades
are held constant is regarded as rationale for utilising contextual data alongside
school grades in the university admissions process (HEFCE 2003,2014; Hoare and
Johnston 2010; Naylor and Smith 2005, Sutton Trust 2010a).
Contextual data places academic attainment into the context of the circumstances in
which the results were obtained, including comparative school and socio-economic
data, with the principal aim of promoting fair access to HE (Bridger, Shaw, and
Moore 2012). The implementation of this alongside school grades is often rec-
ommended on the grounds that currently university admissions systems in the UK
focus almost entirely on studentspast academic results, often without giving any con-
sideration to the context in which these were achieved (Chowdry et al. 2013; Gorard
2008; HEFCE 2014; Mullen 2011; Sutton Trust 2010b).
Though the usage of contextual data in admission has been historically controver-
sial, associated with positive discrimination and social engineering (Henry 2013), it has
progressively become more acceptable and is now being actively encouraged provided
that institutional policy is transparent and based on evidence (Department for Business,
Innovation & Skills 2011). A survey looking at the use of contextual data in admissions
carried out by SPA in 2012 found that out of 17 higher education institutions (HEIs)
approximately one-third (37%) were using contextual data in admissions and 57%
were planning to use it (Moore, Mountford-Zimdars, and Wiggans 2013). Though
the type of contextual information used varies widely between institutions, this gener-
ally includes information relating to studentspersonal details, school and college data,
and area-level data (Bridger, Shaw, and Moore 2012; Moore, Mountford-Zimdars, and
Wiggans 2013). However, research examining this and relationships between contex-
tual background characteristics and achievement in general is highly limited (Gorard
2008; Moore, Mountford-Zimdars, and Wiggans 2013; Zimdars 2007). Addressing
these issues is critical as it could help mitigate inequalities persistent in UK HE partici-
pation (Sutton Trust 2005). The extent of such inequalities is reected empirically with
more than two-fths of students studying at Oxbridge Universities being privately edu-
cated, despite the fact that just 7% of schools in the UK are independent (Sutton Trust
Greater degrees of socio-economic inequality and social stratication have been
associated with pervasive negative educational, health and crime-related outcomes (Fein-
stein 2003; Cabinet Ofce 2011; Wilkinson and Pickett 2009). Promoting fair access to
HE is considered imperative as a means to reducing these inequalities and associated det-
rimental repercussions (Haveman and Smeeding 2006; Kelly 2012). Concomitantly,
increasing equality of opportunity is important for raising skill levels, contributing to
national productivity and social mobility (Dorling 2010). These are considered priority
issues in the UK where currently the usage of contextual data as part of the university
admissions process is promoted as part of a broader widening participation policy
agenda (Cable and Willets 2011, Croxford and Raffe 2013; Milburn 2009). This is
specically promoted within government reports proposing HEIs should continue to
use, and where possible, expand the range of all the information available to them to
identify the best students with the greatest potential to reach the highest academic
achievement(Department for Children, Schools and Families 2008,12).
Studies in Higher Education 1425
The ever-increasing pressure to widen access to prospective students from socio-
economically deprived groups has been greatly augmented by a substantial increase
in tuition fees from £3600 to a maximum of £9000 per annum (Cable and Willets
2011; Harrison 2011). Consequentially, identifying and targeting people from socio-
economically disadvantaged areas with academic potential has become of even
greater nancial importance to HEIs, as potential to charge the full uncapped amount
is only permissible if the Ofce for Fair Access (OFFA) considers that programmes
are being made available to everyone with academic potential (Browne 2010;
Clayton 2012). Moreover, the usage of contextual data in the admissions process
could help widen participation and identify students who may require academic
support (Henry 2013). However, justifying the implementation of contextual data in
university admissions necessitates a robust evidence base, which can adequately
demonstrate the impact of studentsbackground characteristics on academic perform-
ance (Bridger, Shaw, and Moore 2012). This paper seeks to expand this evidence base
by examining relationships between a selected range of school and socio-demographic
factors identied as predictors of educational disadvantage and academic performance
at a British university.
Contextual background characteristics
The literature identies a range of background characteristics that inuence educational
disadvantage and differentiated performance including school effects, socio-economic
background and personal attributes.
In comparison to students from more afuent backgrounds, a disproportionate
number of students from socio-economically disadvantaged backgrounds attend
poor-performing schools and come from neighbourhoods with low participation in
HE (HEFCE, 2010), and this can impact on their chances of entering HE (Forsyth
and Furlong 2003; Gorard 2012; Leathwood 2004; Voigt 2007). Although there is
an overlap between school type and school performance, where fee-paying schools
are predominantly higher performing, the associations between school type and
school performance with academic performance at university has been found to
differ between studies (HEFCE 2003,2005,2014; Smith and Naylor 2001). Indeed,
the average performance of students at a school does not appear to have a consistent
effect on academic attainment in HE (Ogg, Zimdars, and Heath 2009). There is dis-
agreement regarding the direction of the effect of school performance on academic
attainment (HEFCE 2003; Smith and Naylor 2001) including whether or not school
performance has a signicant effect at all (HEFCE 2014; Hoare and Johnson 2010).
Furthermore, even though fee-paying schools tend to have better overall perform-
ance, a school type effecthas been documented whereby for a given set of A-level
results, the degree performance of students who attended state schools has been
found to be higher, compared to those who attend private schools, when all other
factors are held equal (HEFCE 2003,2005,2014; Hoare and Johnston 2010; Naylor
and Smith 2005;Sutton Trust 2010b). This school type effecthas been evidenced
in numerous studies, where it is considered to make a strong casefor making lower
offers to individuals from disadvantaged backgrounds as on average their performance
at HE would at least match that of an independent school student (HEFCE 2003,2005;
Henry 2013; Kirkup et al. 2010; Naylor and Smith 2005; Smith and Naylor 2001). The
justication is based largely on the assumption that independent school pupils are at an
advantage over students from state schools with a similar level of ability, who may be in
1426 T. Thiele et al.
an environment that prevents them from achieving grades reective of their true aca-
demic potential (McNabb, Pal, and Sloane 2002). School type differences in HE
achievement appear to be less marked between students with the highest A-level
achievement and HEIs with highest entry requirements. This has led researchers to
question whether the school type effectexists at these institutions in the past
(HEFCE 2003,2014; Parkes 2011).
A further variable that is associated with disadvantage and individual performance
is socio-economic background. This attribute also often interacts with school effects
given patterns of social selection associated with school admissions policy (Singleton
et al. 2011). Various studies have found that students from the least afuent socio-econ-
omic groups tend to perform less well than their more afuent peers (HEFCE 2014;
Hoare and Johnston 2010; Smith and Naylor 2001). However, much of the research
examining these socio-economic differences in HE attainment has used the National
Statistics Socio-Economic Classication (NS-SEC), the method currently used to ident-
ify SES during the university admissions process (Harrison 2011; Harrison and Hatt
2009; Hoare and Johnston 2010; Singleton 2010b). However, a number of aws
have been identied with the use of NS-SEC as a contextual background characteristic,
particularly as around 25% of students do not provide this self-identied non-manda-
tory information on application to HE, and those who omit this, often t into target
WP populations (Harrison and Hatt 2009,2010; Singleton 2010b).
An alternative approach to NS-SEC utilises postcodes, linking individuals to a dom-
icile location by geo-coding home postcode. However, in presenting such analysis, this
is accompanied by an important caveat that the measure relates to the context of an area
in which a student lived, rather than an attribute they personally possess (Gorard 2012;
Osborne and Shuttleworth 2004). That said, for the majority of undergraduate admis-
sions, NS-SEC is also not an individual measure, as this relates to parental occupation,
although geographic context could perhaps be considered applicable at a household
By attaching locations to the domicile postcodes of students, these can be linked to a
range of indicators of locational context, each of which pertains to a spatial unit of a
given zonal size. Such indicators include the Index of Multiple Deprivation (IMD),
which is a well-recognised measure of deprivation, comprising data pertaining to
seven different dimensions (Income, Employment, Health and Disability, Education,
Skills and Training, Barriers to Housing and Services, Living Environment and
Crime) (Flouri, Mavroveli, and Midouhas 2013). IMD scores are derived at the scale
of Lower Layer Super Output Areas (LSOAs), which are areas containing between
400 and 1200 households. The IMD has, however, received surprisingly little attention
in educational research compared to other elds, despite being recommended by the
HEFCE (2007) as a means of identifying people from NS-SEC groups 47 (Broecke
and Nicholls 2007; Feinstein 2003; Harrison 2011; Lupton 2004).
Afurther measure that has received relatively little attention in educational research,
despite it being devised by HEFCE to identify those from backgrounds with lower
levels of participation in HE, is the Participation Of Local Areas classication
(POLAR 3) (Corver 2010). POLAR 3 was created by HEFCE, by ranking 2001
Census Area Statistic (CAS) Wards by their young participation rates for the combined
20052009 cohorts. There are a total of 8850 CAS wards in England and Wales with an
average population of just under 6000 (Finney and Jivraj 2013). The POLAR 3 classi-
cation reports the rates of participation for those wards and is typically divided into
quintiles. There are also limited examples of research using the POLAR classication,
Studies in Higher Education 1427
which is particularly surprising considering this is used by HEFCE for calculating
widening participation funding, and by the Higher Education Statistics Agency
(HESA) to measure institutional performance (HEFCE 2012,2014). However, recently
HEFCE (2014) used both POLAR 3 and the Indices of Deprivation Affecting Children
as postcode-based measures of disadvantage and found that students from neighbour-
hoods with lower levels of participation in HE and students from less afuent areas,
respectively, were consistently less likely to achieve a 2:1 or a rst-class degree at
Finally, personal characteristics such as sex and ethnicity are also known to inu-
ence academic performance (Ackerman, Kanfer, and Beier 2013). Research suggests
that on average females generally achieve higher grades than males throughout edu-
cation, with some studies reporting that males may be more likely to achieve a rst-
class degree (Dayioğlu and Türüt-Aşik 2007; Gneezy, Niederle, and Rustichini
2003; Hu and Wolniak 2013; McCrum 1994,1996; McNabb, Pal, and Sloane 2002;
Mellanby, Martin, and ODoherty 2000; Pomerantz, Altermatt, and Saxon 2002;
Sheard 2009). Though the present study does not focus on ethnicity, signicant differ-
ences in performance and participation have been documented between ethnic groups.
In the UK, white students as an overall category have been found to perform slightly
better than students who were not self-identied as white (Broecke and Nicholls
2007; HEFCE 2014; Jacobs 2008; Richardson 2008).
Previous studies have examined associations between studentsbackground charac-
teristics and academic performance nationally and at individual universities (HEFCE
2003,2005,2014;Henry2013; Smith and Naylor 2001). However, no previous case
studies have been found which use both postcode-based measures of disadvantage
along with school background information to identify educational disadvantage
despite the limitations associated with measures such as NS-SEC and known differences
in student composition existent between HEIs (Gibbons and Vignoles 2012; Reay,
Crozier, and Clayton 2010;Reayetal.2001; Singleton 2010a,2010b). This is critical
from an admissions perspective as it is the responsibility of individual HEIs to ensure
that their fair admissions policies are grounded in empirical evidence and it is in their
interests to target those students with the academic potential to perform well in their
studies. The present study at a British university endorses this by investigating the
extent to which studentscontextual background characteristics inuence academic/
degree performance.
Measuring and modelling contextual background and achievement
This study examines data from a British university, one of the six original red brick
civic universities and a founding member of the Russell Group. Traditionally, such elite
universities in the UK have tended to have an over-representation of students from
more afuent backgrounds and are more selective, with higher entry requirements
(Sutton Trust 2010b). However, the fact that the university campus is based in a city
with some of the most socio-economically deprived areas in the country means that tra-
ditionally the university has attracted a relatively high proportion of applicants from
low SES backgrounds.
Table A1 shows how the University of Liverpool (UoL) compares in the recruit-
ment of under-represented groups both nationally and in relation to other Russell
1428 T. Thiele et al.
Group universities. Compared to other Russell Group universities, the UoL has a higher
intake of students from low participation neighbourhoods (LPN) (POLAR 3), students
from lower socio-economic groups (NS-SEC 47) and students from low-income
households. Despite this, compared to national averages, the intake of students from
disadvantaged backgrounds at the UoL is generally lower. However, the proportion
of students from state schools at the UoL is similar to the national average, in this
way in particular the UoL differs from other Russell Group universities.
Data for the study were obtained from the university central student database, which
includes all necessary student background information and tracks performance from the
point of application through to graduation. For the purposes of this study, only students
registered on full-time three-year classied degrees entering the university between
2004/2005 and 2009/2010, and then graduating three years after their entry were
included. This was the last entry year that allowed analysis of both entry and exit
points. There were no signicant changes to the universitys admission policies or
grading criteria during this time period, so data were stratied by year of entry but
also treated as a single data set. The data set contains socio-demographic (sex, age,
ethnicity and domicile), school attended, prior attainment (based on Universities and
Colleges Admissions Service [UCAS] Tariff Points), and HE performance
information for 5369 students.
Where data were missing for key variables, students were excluded from the analy-
sis. This was primarily socio-economic information as not all postcodes could be
matched to IMD and POLAR 3 scores, though a small proportion of academic infor-
mation was also missing. Analyses were designed to explore research questions
centred on the relationships between school type, school performance, socio-economic
background and academic performance at university. The full list of variables included
in the analysis is described in Table A2 (see Appendix).
In order to make comparisons between degree programmes and students as fair as
possible, students registered on four- and ve-year programmes including Veterinary
Science, Medicine and Dentistry were excluded from the data set. Secondly, only
students with a postcode within England were included in the analyses as the IMD is
produced separately in each of the four UK administrations. Students from outside
the UK were also excluded. Finally, only data for students who completed three-year
degrees programmes successfully were included in this study.
Univariable logistic regression was carried out summarising the association
between contextual background characteristics and academic performance; this was
dened as good (2:1, rst classication) versus other. Multivariable logistic regression
was carried out to identify which factors were independently associated with academic
performance. All analyses were undertaken using SPSS (version 21).
There was no evidence of collinearity between the explanatory factors used in the
analysis (p> .05).
Students in the data set were predominantly self-classied as white (91.5%) and
aged below 21 (92.4%). The percentage of males and females in the study was rela-
tively uniform (58.4% females) in aggregate, though differences in the proportion of
males and females across university faculties varied.
Table A3 presents a descriptive summary of the association between each of the
contextual background characteristics and academic performance. Signicant
Studies in Higher Education 1429
differences were observed in the Universities and Colleges Admissions Service
(UCAS) tariff points of students from different school backgrounds, quintiles of
socio-economic deprivation, neighbourhoods with different levels of participation in
HE, different ethnicities and between males and females.
The majority of students came from comprehensive schools and sixth form colleges
(3431, 75.2%). Students who attended grammar schools and sixth form colleges came
into university with the highest UCAS tariff points (Table A2). However, similar ndings
were not reected in university attainment, as students from comprehensive schools
achieved the highest average nal year grades, and generally performed better than stu-
dents from all other school types. Conversely, students from independent schools andstu-
dents from the category of schools state otherachieved the lowest average grades at
university compared to students from other school types. Moreover, these students
were also signicantly more likely to achieve degree classications below a 2:1.
Students who attended schools that were considered high performing in terms of
A-level performance/equivalent entered university with higher A-level (or equivalent)
grades compared to those who went to lower performing schools (p< .0005). However,
by nal year at university, differences in overall mark averages were no longer
statistically signicant. A similar pattern was observed when comparing the academic
performance of students with different levels of neighbourhood participation in HE
(POLAR 3). Here, students from high participation neighbourhoods (HPN) entered
university with signicantly higher UCAS tariff points than students from LPN;
however, by the nal year at university, differences between students from LPN and
HPN were no longer statistically signicant.
With respect to multiple deprivation (IMD), the number of students within each
quintile increased as deprivation decreased, so there were 2.17 times more students
in quintile 5 (least deprived) than quintile 1 (most deprived). There was also a positive
relationship between IMD quintile and UCAS tariff points such that students from the
least deprived areas entered university with the highest UCAS tariff points and conver-
sely, students from the most deprived areas entered with the lowest UCAS tariff points.
By contrast, material deprivation predicted only slight differences in academic achieve-
ment once students were at university. Indeed, only students from the most deprived
socio-economic quintile achieved slightly less well on average and were more likely
to achieve lower second class or a lower degree classication, but this was not statisti-
cally signicant.
With regard to ethnicity, there were signicant group differences in studentsUCAS
tariff points, which decreased but were largely consistent at university. Black students
entered university with the lowest number of UCAS tariff points and once at university
achieved the second lowest average attainment after Asian students. Students from both
of these ethnic groups were also more likely to get a degree of 2:2 or lower.
Finally, a consistent statistically signicant association was observed for sex in
relation to academic attainment in both school and university attainment. Males entered
university with signicantly lower grades than females, achieved lower average marks
at university and were also less likely to get a good degree(2:1 or above).
Table A4 summarises the results for contextual background factors in relation to nal
degree classication. Compared to students from the most deprived quintile (IMD), stu-
dents from all of the other IMD quintiles were slightly more likely to obtain a good
degree; however, this association was only statistically signicant for IMD quintiles 4
and 5. Secondly, compared to students who had attended comprehensive schools, stu-
dents from the four other types of school were less likely to obtain a good degree, but
1430 T. Thiele et al.
this association was only statistically signicant for students from independent schools.
Thirdly, ethnicity was signicantly associated with degree performance, where com-
pared to white students, Asian and black students were more than 50% less likely to
achieve a good degree. Finally, sex and UCAS tariff points were both found to predict
signicant differences in the probability of getting a good degree.
There were no signicant differences in the likelihood of achieving a good degree at
university between groups of students who came from neighbourhoods with low/high
participation and between those students who attended schools with low/high levels of
performance (Table A4).
Multivariable logistic regression was carried out to estimate how studentsbackground
characteristics including neighbourhood participation (POLAR 3), deprivation, edu-
cational background and personal characteristics inuenced their odds of getting a good
degree. Table A5 presents these results incorporating the seven background characteristics
simultaneously and degree performance as a binary outcome (1st and 2:1 versus all others).
Whilst the majority of associations between socio-economic deprivation and edu-
cational performance were initially found to be statistically signicant in the univari-
able analysis, in multivariable analysis socio-economic deprivation was observed to
exert less of an inuence on the chances of getting a good degree after allowing for
the effects of the other variables (Table A4). Compared to students from the most
deprived socio-economic quintile (quintile 1), students from quintile group 4 were
most likely to achieve a good degree (odds ratio (OR) = 1.34; 95% CI = 0.991.82);
comparisons with other quintiles did not achieve statistical signicance.
Compared to comprehensive school students, multivariable analyses revealed that
students from all other types of school had signicantly lower odds of achieving a
good degree (with the exception of the category state otherwhere the association
was not statistically signicant) (OR = 0.58; 95% CI = 0.271.24). The difference
was greatest between students from comprehensive schools and students from indepen-
dent schools who were found to be 40% less likely to achieve a good degree (OR =
0.61; 95% CI = 0.480.77).
Though performance of school did not signicantly predict differences in edu-
cational performance univariately, there was a signicant association in the multivari-
able analysis. Here it was found that students from schools that were high performing
were signicantly less likely to achieve a good degree than those from low-performing
schools (OR = 0.78; 95% CI = 0.620.98). Associations between neighbourhood par-
ticipation (POLAR 3) and degree classication remained non-signicant.
Ethnicity remained a signicant predictor of degree performance in the multivari-
able analysis. Compared to white students, Asian students were 48% less likely to
achieve a good degree(OR = 0.52, CI = 0.330.82) and similarly black students
were 53% less likely to achieve a good degree (OR = 0.47, CI = 0.240.89)
Studentssex also remained a signicant predictor in multivariable analysis. Com-
pared to males, females were more than 50% more likely to achieve a good degree (OR
= 1.52; 95% CI = 1.301.79). Finally, studentsUCAS tariff points (entry-level per-
formance) were also signicantly associated with university performance in the multi-
variable analysis (OR = 1.01; 95% CI = 1.011.01).
The principal aim of this research was to explore the relationship between studentscon-
textual background characteristics and academic performance at university in order to
Studies in Higher Education 1431
identify which characteristics were associated with studentschances of achieving a
good degree(upper second- or rst-class degree). No other case studies have been
found where this is explored using both postcode-based measures of disadvantage and
school background information. Hence, a critical part of this research involved investi-
gating whether patterns identied in previous studies were also evidenced at this British
university and exploring potential variations which could exist as a consequence of the
differences in student intake and performance which are known to exist even between
elite universities (Hoare and Johnston 2010; Singleton 2010b).
Principal ndings from results
A crucial part of the analysis involved addressing the extent to which school grades are
representative of true academicpotential by comparing group differences in attain-
ment at school compared to university. Statistically signicant associations were
observed between all of the contextual background characteristics: IMD, school type,
school performance, neighbourhood participation, sex and ethnicity and students
school grades (UCAS tariff points). With the exception of IMD and POLAR 3, all of
these variables were also signicantly associated with university attainment, though
compared to differences observed in entry grades, these associations differed substan-
tially in terms of size/direction. Additionally, consistent with other studies, school
grades were also found to be a strong and signicant predictor of academic performance
at university (HEFCE 2012,2014; Kirkup et al. 2010; McKenzie and Schweitzer 2001).
Socio-economic differences persisted in nal year performance at university, but
only approached statistical signicance between students from the most deprived
areas and those from the second least deprived group ceteris paribus. Additionally, stu-
dents from the most deprived areas were found to be more likely to achieve degree
classications of 2:2 or below. Unlike the IMD, POLAR 3, hence coming from neigh-
bourhoods with low or high levels of participation in HE, did not predict signicant
differences in nal year performance.
Conversely, the type of school that students attended had a signicant and differen-
tial impact on school achievement compared to university achievement. Overall, stu-
dents from comprehensive schools were more likely to achieve a good degree than
students from all other school types. Students from independent schools were found
to be less likely to achieve a good degreecompared to students from comprehensive
schools despite being more likely to enter university with higher grades. To an extent,
this is similar to the relationship between school performance and academic achieve-
ment as students from low-performing schools achieved slightly higher nal averages
than their counterparts from high-performing schools.
With regard to ethnicity, there were signicant differences in studentsUCAS tariff
points and similar differences were observed in university attainment where Asian stu-
dents and black students were signicantly less likely to achieve good degrees compared
to white students. Finally, one variable that did not follow any of the patterns evidenced
hitherto was sex. Males entered university with lower grades than females, and once at
university were also less likely to achieve either a rst or an overall good degree.
How do these ndings relate to the current evidence base/other studies?
Differences in academic achievement by SES have been reported in numerous studies
(Delaney, Harmon, and Redmond 2011; Hoare and Johnston 2010). These are typically
1432 T. Thiele et al.
evidenced from a young age and span across a number of subjects (Aikens and Barbarin
2008; Chowdry et al. 2013; Coley 2002; Evans 2008). Consequently, the differences in
academic performance observed in this study are not surprising, particularly as differ-
ences that persisted by the third year at university were primarily between students who
came from the most deprived areas and students from some of the most afuent areas.
Similar ndings have been reported in past research and attributed to differences in a
range of factors including family support, family history in HE (Allardice and
Blicharski 2000; HEFCE 2014; Richardson, Abraham, and Bond 2012), term time
working (Moreau and Leathwood 2006; Salamonson et al. 2012), and differences in
expectations and aspirations (Pampaka, Williams, and Hutcheson 2012; Thomas 2001).
In this research, the gap in academic attainment between students from neighbour-
hoods with low or high levels of participation in HE (POLAR 3) was only signicant at
entry level. Though slight differences prevailed even by nal year at university, these
were not signicant. HEFCE (2014) found little variation in academic performance
when POLAR quintiles were examined together and entry grades were taken into
account. However, they also found that in particular students from the areas with
lowest participation rates (POLAR quintile 1) performed signicantly less well and
achieved the lowest proportions of high degree classications.
Ethnic differences in achievement are widespread and have been reported in numer-
ous studies (Broecke and Nicholls 2007; Hoare and Johnston 2010; Woolf, Potts, and
McManus 2011). Though variations exist with regard to the particular ethnic groups
that perform less well, overall, white students have generally been found to perform
better than students from other ethnic minorities (HEFCE 2014; Jacobs 2008; Richard-
son 2008). The latter was not evidence in this study at entry level or in university attain-
ment. However, compared to white students, black and Asian students were particularly
less likely to achieve a good degree, and the proportion of black and Asian students
achieving degree classications of 2:2 or below was alarmingly high. Ethnic differ-
ences in achievement such as these are not atypical; these have been identied in
other studies and require further exploration.
Though previous studies have reported males as being up to 50% more likely to
achieve rst-class degrees than females (McCrum 1996; Mellanby, Martin, and ODoh-
erty 2000), in more recent studies females have been found to outperform males, con-
sistent with the ndings from this study (Dayioğlu and Türüt-Aşik 2007; Sheard 2009).
The gap in academic performance between males and females is alarming and though
this does not relate to WP per se requires further exploration, particularly, as differences
in subject choice between males and females are acknowledged and studies have found
that trends may vary by age, between subjects and have differential effects on employ-
ability outcomes (Ackerman, Kanfer, and Beier 2013; Hu and Wolniak 2013; Richard-
son and Woodley 2003). Consequently, future research should explore interactions
between sex, subject choice, SES and outcomes.
The ndings pertaining to the relationship between school performance and aca-
demic achievement are difcult to reconcile unreservedly with past research, as it is
not only highly limited, but ndings have been mixed and largely inconsistent
(HEFCE 2003; Hoare and Johnston 2010; Smith and Naylor 2001). According to
HEFCE (2003), ndings have been particularly mixed because the effect of school per-
formance varies largely, depending on factors such as A-level points, studentssex and
subject. The ndings of this research are consistent with those of Smith and Naylor
(2001) who also found evidence of a positive association between attendance at
lower performing schools and degree performance. They argue that when comparing
Studies in Higher Education 1433
two students with the same A levels, the student who is less advantaged, coming from a
state school with lower overall performance, is more likely to have greater underlying
ability. This suggests that the school qualications achieved by students from low-per-
forming schools may not represent their true/academic potential. However, these results
contrast with the ndings of HEFCE (2003) regarding the direction of the association
between school performance and degree attainment and other studies where no signi-
cant association was found (HEFCE 2014; Hoare and Johnston 2010). This highlights
the need for further research exploring variations in school performance.
Students from independent schools did not enter the university with the highest
grades, as was the case in other studies (HEFCE, 2003,2005,2014; Hoare and Johnston
2010; Naylor and Smith 2005; Smith and Naylor 2001). However, consistent with past
research, once at university, students from independent schools achieved lower results
than students from all but one of the other school types, including comprehensive
school students (Hoare and Johnston 2010). This effect is said to occur largely
because independent school students are at an advantage over students from state
schools and this advantage is reected in their qualications and progression to HE
(Dorling 2010). This advantage is associated with factors including the quality of edu-
cation students receive, types of subjects offered, a greater focus on preparing students
for university and indeed altogether better resourcing as educational spending (23%) on
privately educated children in Britain is more than almost any other rich nation in
Europe (Hoare and Johnston 2010). According to Ogg, Zimdars, and Heath (2009),
teaching effects at independent schools inate the qualications obtained by their stu-
dents. Either or both of these arguments could explain why comprehensive school stu-
dents enter university with lower results than independent school students but all other
factors held equal, nalise with higher results.
Implications of these ndings
Results from the present study support the notion that variables such as school grades
are not the only causal factors behind patterns in academic attainment in HE and should
be accompanied by information that puts this attainment into context. This is evidenced
by the fact that even though all of the variables, namely IMD, school type, school per-
formance, neighbourhood participation, sex and ethnicity, were signicantly associated
with entry grades (UCAS tariff points), overall, differences between groups largely
decreased at university. Additionally, associations between school performance and
school type differed at university compared to entry level. Specically, students from
low-performing schools and independent schools were less likely to achieve a good
degreecompared to students from comprehensive and high-performing schools,
despite entering university with higher grades. Hence, together, these results support
the implementation of contextual information in university admissions. This is
widely advocated in educational policy by the government and different HEIs where
similar ndings have been documented and considered to make a strong casefor
making reduced offers to students from particularly disadvantaged backgrounds
(HEFCE 2014; Henry 2013; Hoare and Johnston 2010; Kirkup et al. 2010; Naylor
and Smith 2005; Smith and Naylor 2001;Sutton Trust 2010b).
Despite the increased interest in contextualised admissions, there is little publicly
available research detailing why and how particular background characteristics are used
at individual HEIs (Moore, Mountford-Zimdars, and Wiggans 2013). Additionally,
critics argue that making reduced offers to students from socio-economically/
1434 T. Thiele et al.
educationally disadvantaged backgrounds discriminates against students from afuent
backgrounds/independent schools and may reduce academic excellence at HEIs (Sin-
gleton, 2010b). The ndings of the present study represent a powerful riposte to
such arguments, providing additional support for the school type effectand the
notion that school grades may not reect true academic potential.
Limitations and directions for future research
The present research has various limitations that must be taken into consideration when
interpreting these ndings. Firstly, it is not possible to control for all factors that affect
university attainment. Some prominent factors that were not controlled for include
working during term time (Moreau and Leathwood 2006), living at home (Holdsworth
2006), student engagement (Hu and Wolniak 2013; Johnson and Reynolds 2013),
family history in HE (Allardice and Blicharski 2000; Delaney, Harmon, and
Redmond 2011) and individual characteristics including intelligence (Farsides and
Woodeld, 2003; Haworth et al. 2011; Mega, Ronconi, and De Beni 2014). Further-
more, a main limitation of this study is that it did not include students that entered uni-
versity via non-standard routes, even though research suggests that these students may
be more likely to have suffered from educational disadvantage (Broecke and Nicholls
2007; Gorard 2012). Secondly, this study included only those students who success-
fully completed their degrees; and not those who failed or dropped out; future research
should examine trends in academic achievement in these groups of students. Hence, it is
important to take into account that these ndings are not representative of all university
applicants. Another limitation of this research is that the IMD relates only to LSOAs
and not postcodes or smaller geographical units (Gorard and See 2009; Hoare and John-
ston 2010; Smith and Naylor 2001). Indeed, Gorard (2012) highlights that some of the
most deprived families actually live in heavily polarised areas, such as inner London
boroughs. Despite this, the IMD was found to be a useful tool for identifying signicant
differences in performance. Similarly, POLAR 3 is also restricted in this sense as it is
also based on aggregate data. Thus, it must be considered that trends relating to both
IMD and POLAR 3 do not necessarily relate to individuals themselves but rather to
the areas in which they are based. A nal and common limitation relevant to the
present study lies in the high proportion of missing data as this could signicantly
bias analyses and results, and is something that must be taken into account (Gorard
The need for further research exploring educational disadvantage and variations in
academic performance is indisputable, as a number of questions remain unanswered.
This is partly due to the strict exclusion criteria that were used to make comparisons
between students as fair as possible. Future studies should also focus on those students
who entered university via non-standard routes and compare performance of students
with different types of school qualications as trajectories throughout HE may be inu-
enced by these factors. Secondly, it is critical for future research to explore why differ-
ences in achievement at university occur in order to support students and identify those
at risk of dropping out, failing and/or not achieving a good degree. Thirdly, associ-
ations between contextual background and academic performance in programmes
extending beyond three years including medicine, dentistry and veterinary science
require further exploration. Addressing the paucity of research on this is essential as
these programmes are highly oversubscribed, selective and competitive, having
higher entry requirements than most other programmes. Finally, it is important to
Studies in Higher Education 1435
note that even within elite universities, there are major differences in student compo-
sition and student performance. Consequently, though the present case study illustrates
important differences between different groups of students at a British university, ana-
lyses must be expanded to include other universities. In particular, analysis should
focus on the most competitive and selective universities, known as elite universities
as these are often criticised for having comparatively less WP students to other univer-
sities (Singleton 2010b). The lack of research on this is problematic and must be
addressed as convincing evidence is necessary for guiding the decision-making
process in the implementation of contextual data alongside school qualications.
Concluding comments
Understanding factors which are associated with differences in HE participation and
performance is crucial, particularly given the expansion that the British HE system
has undergone in the last decade, changing nancial regimes, and the inequalities
which persist (Breen and Jonsson 2005; Sutton Trust 2010b). Though there is
general awareness that prior opportunities and social background impact on academic
performance and subsequently access to HE, utilising contextual data for admissions in
an evidence-based manner is less well understood (Bridger, Shaw, and Moore 2012;
Zimdars 2007). This may represent one of the underlying reasons why current contex-
tual considerations are so limited (Zimdars 2007).
However, the usage of contextual information in admissions can be regarded as a
mechanism for ameliorating the current admissions systems by addressing limitations
related to the usage of examination marks as these alone are not considered an appro-
priate proxy of an applicants true academic potential (Ogg, Zimdars, and Heath 2009).
The present study provides insight into the associations between different background
characteristics and academic outcomes, contributing to the evidence base that advocates
the implementation of contextual data alongside school grades during the admissions
process. This illustrates how contextual data can be utilised to identify students with
school qualications that may not reect the extent of their academic potential, but
also to help identify those at risk of underperforming once they are in HE (Bridger,
Shaw, and Moore 2012; Lupton 2004; Ogg, Zimdars, and Heath 2009). Thus, going
beyond purely theoretical analysis, the practical repercussions of the present research
could help raise academic attainment to higher levels, and more generally, improve
the student experience (Bridger, Shaw, and Moore 2012). Unfortunately, research on
this remains highly limited despite the fact it is critical to HEIs, the UK government
and, most importantly, to students themselves as changes in admissions may impact
on their life chances and subsequent career opportunities (Jacobs 2008; Mullen
2011). Further research is necessary to ensure that university policies are based on
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Table A1. UoL widening access performance.
2. Lower socio-economic
groups (NS-SEC 47)
3. State schools/
4. Low-income
UoL 8.5% 22.0% 87.6% 27.4%
English Russell
5.5% 18.3% 72.5% 21.4%
England 10.2% 30.9% 88.5% 31.6%
Note: 14 from the UoL 2014 Access Agreement.
Table A2. Description of outcome (educational performance) and predictor (contextual
factors) variables.
Variables Description
Outcome variables
Average performance Studentsuniversity academic performance was represented as a
percentage indicating the average mark achieved for each year of
their degree. Most analyses focused on nal year performance as this
has a 70% weighting on the overall degree
Final degree
Degrees were classied according to the UK undergraduate degree
classication system; rst class typically being awarded to those
who achieved 70% and above, 2:1 to those who achieved between
60% and 69%, 2:2 awarded to those achieving 50% and 59%, and
third-class degrees awarded to students achieving between 40% and
49%. For most analyses, a binary classication (1st and 2:1 versus
others) was used
Predictor variables
UCAS tariff points UCAS tariff points are a system used for allocating points to post-16
qualications in the UK (e.g. for A levels, A = 120, B = 100, C = 80,
etc.). This was calculated from studentsthree highest qualications
and used as a measure of prior achievement for entry to HE
School type The type of school studentsattended for their A levels were organised
into ve categories including independent schools, state grammar
schools, state comprehensives, sixth form colleges and the category
labelled state other (includes voluntary aided schools, voluntary
controlled schools, technical colleges and adults colleges)
School performance School performance data were used to contextualise prior attainment,
represented by the overall percentage of students gaining 5A*Eor
more at A levels or equivalent. Based on this, a binary classication
was created where high-performing schools, represented those
schools where 82.5% of students and above achieved 5A*Eor
more at A level or their equivalent. Low-performing schools were
those where less than 82.5% of students achieved 5A*E or more at
A level or their equivalent based on averages reported in Department
for Education performance tables
1442 T. Thiele et al.
Table A2. (Continued .)
Variables Description
POLAR 3 data were matched to the CAS wards to illustrate the typical
HE participation prole within which students were domiciled.
POLAR 3 data were reported as ve quintiles: ordered from 1
(lowest participation) to 5(highest participation). A binary
classication was created to compare performance of students
residing in areas of lowest participation (1 and 2) to others (3, 4 and 5).
Quintiles 1 and 2 are those areas, which attract additional widening
participation funding for each student domiciled within them
Multiple deprivation The IMD was used to identify the multiple facets of total deprivation.
Studentspostcodes were matched to LSOAs, which contain an
average of 1500 households. These were then used to append IMD
scores provided that students had a valid English postcode. There are
32,482 LSOAs in England. IMD ranks LSOA with 1 as most
deprived and 32,482 as least deprived. For the analyses IMD scores
were divided into quintiles, where quintile 1 includes the most
deprived areas and quintile 5 includes the least deprived
Sex/ethnicity Sex and ethnicity were self-reported by students during the university
application process. Studentsethnicities were categorised as one of
the following: white, Asian, black, Chinese, and mixed and other
Table A3. Descriptive breakdown of characteristics of study sample for students in all three-year
degree programmes.
Indicator of student performance
UCAS tariff
Final year
rst class
class 2:1
Variable No. MSD MSD No. % No. % No. %
School type:
Independent 564 359.40 74.92 61.59 6.48 53 9.40 345 61.17 166 29.43
Grammar 511 389.73 77.74 62.52 6.20 60 11.45 336 64.12 128 24.43
Comprehensive 2350 348.99 87.21 62.73 6.61 327 13.84 1506 63.73 530 22.43
Sixth form 1081 389.54 89.09 62.20 6.65 136 12.58 673 62.26 272 25.16
State (other) 55 335.64 82.07 61.79 6.53 4 7.27 34 61.82 17 30.91
p< .0005 p= .01 p= .01 p= .01 p= .01
School performance:
High 3526 375.03 83.62 62.58 7.03 439 11.84 2394 64.58 874 23.58
Low 1822 333.26 96.61 62.36 6.44 136 15.61 520 59.70 215 24.68
p< .0005 p= .42 p< .01 p< .01 p< .01
1 655 301.67 153.58 61.48 7.75 89 13.61 381 58.26 184 28.13
2 687 336.84 122.84 62.50 6.54 90 13.12 437 63.70 159 23.18
3 917 340.25 122.14 62.51 6.51 125 13.66 561 61.31 229 25.03
4 1153 350.81 113.98 62.83 6.21 150 13.01 753 65.31 250 21.68
5 1423 361.74 108.14 62.50 6.42 189 13.28 909 63.88 325 22.84
p< .0005 p< .01 p< .01 p< .01 p< .01
Studies in Higher Education 1443
Table A3. (Continued.)
Indicator of student performance
UCAS tariff
Final year
rst class
class 2:1
Variable No. MSD MSD No. % No. % No. %
High 4010 364.70 87.83 62.46 6.37 510 12.72 2539 63.33 960 23.95
Low 1222 356.78 92.31 62.13 7.22 175 14.37 739 60.67 304 24.96
p= 0.01 p= .02 p= .18 p= .18 p= .18
Males 1985 351.80 88.94 61.77 7.15 281 12.7 1290 58.1 649 29.23
Females 2949 370.10 88.73 62.93 6.11 423 13.4 2082 66.2 641 20.38
p< .0005 p< .0005 p< .0005 p< .0005 p< .0005
White 4913 297.38 40.32 62.63 6.14 644 13.11 3125 63.62 1143 23.27
Asian 127 299.00 45.94 60.03 6.55 12 9.60 61 48.80 52 41.60
Black 65 266.84 52.66 60.76 5.87 7 10.77 32 49.23 26 40.00
Chinese 48 280.00 44.49 62.60 9.07 12 25.00 25 52.08 11 22.92
Mixed 111 361.74 108.14 62.67 7.13 17 15.32 68 61.26 26 23.42
Other 105 293.22 45.43 63.21 6.53 12 11.43 61 58.10 32 30.48
p< .0005 p< .0005 p< .0005 p< .0005 p< .0005
Dened by quintiles of IMD (1 = most deprived 5 = least deprived).
Neighbourhood HE participation.
Table A4. Unconditional bivariate logistic regression models for student characteristics with
nal degree performance (2:1 and 1st versus lower classication).
Indicator variable OR good degree
Variable No. % OR 95% CI p-Value
State comprehensive (reference) 2334 51.5 1
Sixth form college 1068 23.6 0.85 0.721.01 .07
State other 54 1.2 0.65 0.361.17 .15
State grammar 521 11.5 0.87 0.691.08 .21
Independent school 556 12.3 0.69 0.560.85 < .0005
School performance:
High (reference) 3663 81.0 1
Low 857 19.0 0.96 0.801.14 .62
1 (reference) 642 13.5 1
2 678 14.2 1.28 0.991.64 .06
3 907 19.0 1.134 0.901.43 .28
4 1145 24.0 1.37 1.091.71 .01
5 1401 29.4 1.33 1.071.65 .01
High (reference) 3964 76.8
Low 1198 23.2 0.97 .831.13 .65
1444 T. Thiele et al.
Table A4. (Continued.)
Indicator variable OR good degree
Variable No. % OR 95% CI p-Value
Males (reference) 2179 41.1 1
Females 3119 58.9 1.58 1.391.80 <.0005
White (reference) 4913 91.5 1
Asian 127 2.4 0.43 0.300.62 <.0005
Black 65 2 0.45 0.270.75 <.01
Chinese 48 0.9 1.07 0.532.15 .86
Mixed 111 2.1 1.02 0.651.61 .93
Other 105 2 0.75 0.481.17 .20
UCAS points (continuous) 4952 92.2 1.01 1.011.01 <.0005
Dened by quintiles of IMD (1 = most deprived 5 = least deprived).
Neighbourhood HE participation.
Table A5. Multiple logistic regression including all student characteristics (deprivation (IMD),
school grades, school type, school performance, neighbourhood participation and sex) and nal
year performance (2:1 and 1st versus lower categories).
Indicator variable OR good degree (1st or 2:1)
Variable No. % OR 95% CI p-Value
State comprehensive (reference) 1829 49.0 1
Sixth form college 968 26.0 0.67 0.550.82 <.0005
State other 35 0.9 0.58 0.271.24 .16
State grammar 416 11.6 0.71 0.540.94 .016
Independent school 482 12.9 0.61 0.480.77 <.0005
School performance:
Low (reference) 644 17.3 1
High 3086 82.7 0.78 0.620.98 .03
1 (reference) 452 12.1 1
2 520 13.9 1.25 0.921.70 .16
3 725 19.4 1.03 0.761.39 .85
4 918 24.6 1.34 0.991.82 .06
5 1115 30.0 1.17 0.871.59 .31
Low (reference) 820 22.0 1
High 2910 78.0 1.08 0.861.34 .52
Males (reference) 1520 40.8 1
Females 2210 59.3 1.52 1.301.79 <.0005
White (reference) 4913 91.5 1
Asian 127 2.4 0.52 0.330.82 <.0005
Studies in Higher Education 1445
Table A5. (Continued.)
Indicator variable OR good degree (1st or 2:1)
Variable No. % OR 95% CI p-Value
Black 65 2 0.47 0.240.89 .002
Chinese 48 0.9 1.33 0.523.38 .55
Mixed 111 2.1 1.33 0.732.40 .35
Other 105 2 0.84 0.471.53 .57
UCAS points (continuous) 4952 92.2 1.01 1.011.01 <.0005
Dened by quintiles of IMD (1 = most deprived 5 = least deprived).
Neighbourhood HE participation.
1446 T. Thiele et al.
... El acceso a la educación superior ha sido considerado una medida muy eficaz en torno a reducir las inequidades socioeconómicas de las poblaciones, ya que incrementa la igualdad de oportunidades y contribuye a dinamizar la productividad nacional y la movilidad social (Thiele et al., 2016). Teniendo en cuenta esta premisa, se considera que el desempeño académico de los estudiantes universitarios es un importante tópico de estudio para las instituciones de educación superior (Guney, 2009). ...
... Antes de profundizar en el método específico a utilizar, es válido anotar que la literatura identifica un rango de características que influyen en el desempeño escolar, tales características se dividen en tres grupos: factores sociodemográficos, factores socioeconómicos y factores educativos relacionados con la institución de egreso (Thiele et al., 2016). Los factores personales y/o sociodemográficos más determinantes en el rendimiento académico son: el género, la edad, el background familiar y los logros educativos previos (Anderson et al., 1994;Cappellari et al., 2012). ...
... Con relación a las características socioeconómicas, varios estudios han identificado que los estudiantes de grupos socioeconómicos menos favorecidos tienden a tener un desempeño inferior que el de sus homólogos de mejor estrato económico. Respecto a los factores institucionales, los análisis de regresión reportan que los estudiantes de las instituciones privadas tienen un mejor desempeño que los estudiantes de las universidades estatales (Thiele et al., 2016). Sin embargo, estos mismos autores al realizar un análisis más específico observan la existencia de un efecto tipología de la institución, que demuestra que en la escala de puntajes, en los cuartiles superiores los estudiantes de instituciones públicas obtienen los mejores resultados (Thiele et al., 2016;Cappellari et al., 2012). ...
... An important component of this study involved investigating whether the inconclusive findings in prior studies could be clarified using data mining techniques with accounting undergraduate student data. Further examination of variables such as prior academic performance and previously identified demographic variables was undertaken (Guarín et al., 2015;Singleton, 2010;Strecht et al., 2015;Thiele et al., 2016). In each test, classification was conducted by first examining the prediction of each predictor group and the possible combination of different groups. ...
... Even though all the predictors in our study (demographic and socio-economic information, previous academic performance, students' emotion and intellectual skills) have been found to be significantly associated with performance in first year university grades (Guarín et al., 2015;Hamsa et al., 2016;Howard et al., 2018), very few studies have tracked performance in subsequent accounting units (Bobe & Cooper, 2018;Duff, 2004;Tyson, 1989). Overall, the differences between subgroups largely decreased as students progressed through their university degree (Thiele et al., 2016). Specifically, students' scores from secondary schools, age, gender, socio-economic information differences were less likely to be major factors determining students' progress in accounting. ...
... The purpose of selecting new students is to select students who are ready and have the potential to do academic assignments. Through the process of selecting new students, information will be obtained about factors that affect student academic performance (Thiele et al., 2016). Researchers and educational practitioners pay attention to initial competence and about the potential of new students in order to predict academic success. ...
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In future, changes in science and society will increasingly demand interdisciplinarily prepared professionals and researchers. Inter/transdisciplinarity has been worked on theoretically and scientifically examined. The review study shows how both approaches are explained, how they are put into the practice of doctoral studies, what the results are of the interdisciplinary approaches applied, but also their limits and barriers.
... Thus, the social inequalities in the HE system have remained, although in a different and more complex form. It has to be said, however, that there are exceptions to this general pattern with some students from disadvantaged backgrounds being high level achievers (Thiele et al., 2016). ...
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The issue of social class related inequalities in access to Higher Education (HE) has been high on the political agenda for nearly two decades. In spite of significant funding, channelled through university-led outreach activities to encourage disadvantaged young people into university, the social gap in HE participation persists. As a result, universities are under increasing pressure to provide hard evidence of 'what works' in terms of the outreach they deliver under the Government's Widening Participation (WP) agenda. Recent large-scale research identifies prior attainment at Key Stage 4 (GCSE) as the main barrier to HE access for disadvantaged students, and as a result the Office for Students (OfS) now require universities to raise students' pre-entry attainment. This research examines the potential for university-led outreach activities to help disadvantaged students over this attainment hurdle. Two of the three research questions posed draw on big data collected through HEAT, a system whereby universities in England record data on the students engaged in their outreach activities, tracking their subsequent progress in terms of school attainment and eventual HE entry. Research question one examines the extent to which outreach delivered in the past has been targeted towards the 'right' students, most in need of assistance with this level of attainment. I find a considerable amount of resource has been mis-targeted. In the second research question, I devise a quasi-experimental method that makes the best use of HEAT's collective tracking data to explore whether outreach activities are able to raise students' attainment. Results show a positive impact on attainment, although this is accompanied with a 'health warning' regarding the important unresolved issues of epistemology associated with my approach. The third research question moves away from HEAT's quantitative data and draws on qualitative methods to understand the specific activities universities are delivering to raise attainment, and how these might be expected to work. Content analysis of institutional Access Agreements provides a good starting point, and from this I generate a typology of attainment-raising activities being delivered by universities. This line of enquiry is extended through interviews with WP managers from 30 universities where Academic Tutoring delivered by student ambassadors emerges as the most common attainment-raising activity. This choice is seemingly driven by the demanding requirements on universities to show hard evidence of impact on exam results. However, closer examination of the processes and mechanisms through which Academic Tutoring activities are expected to work are not sufficiently theoretically convincing. ii I conclude the research with a series of recommendations for policy. These include lessening the strict requirements on universities to demonstrate impact when it comes to raising attainment in schools. This may encourage more creative activities, less reductionist in their approach than Academic Tutoring which appears to replicate what is already happening in schools. I also suggest that HEAT should be utilised for its monitoring capacity rather than being a 'scientific' predictor of impact evaluation. Government should investigate using HEAT as a mechanism to provide the OfS with data on the types of students receiving outreach and where they live in the country. Further research is also needed to better understand the circumstances under which Academic Tutoring outreach activities, which are already being delivered by universities, may be able to add value to the complex issue of raising attainment in schools. iii
... As in previous studies (e.g., (Richardson and Woodley, 2003); (Robbins et al., 2004); (Hattie, 2009); (Cassidy, 2012); (Richardson et al., 2012); (Dupont et al., 2016); (Thiele et al., 2016); (Schneider and Preckel, 2017)), Table 9 shows that being a female student, higher mother's and father's education levels and higher levels of student prior performance are significantly and positively related to GPA. We note the significant and positive link between missing values and GPA for the prior performance. ...
The impact of student networks on academic performance has gained importance as a research subject. In addition to well-known centrality measures (i.e., degree and closeness centralities), this study tests indices that received less attention to predict student performance. This set of measures allows for distinguishing the effects on performance of being connected, being located in advantageous position(s), being connected to central peers and being located within connected neighborhoods. Besides, studies on links between network density and student achievement are rare. This research investigates the combined impacts of student centrality and network density on academic performance. We asked 574 college students about their friendships, and drew the network from the collected information. We used the Exponential Random Graph Models to impute missing friendship ties. Then, we applied a hierarchical clustering approach that identified sub-communities within the student network and we computed the density within each sub-community to study density. Finally, we used hierarchical modeling to predict student performance, i.e., by centrality at the student level and by density at the network level. Results demonstrate a positive impact of the geodesic k-path and of the closeness centralities on GPA, together with a positive impact of cluster’s density on performance, which seems, however, bounded by a ceiling effect.
... High school academic performance has been studied as a key factor in first year university success. Despite rising interest in non-traditional factors influencing academic success, studies continue to show that high-school GPA is an excellent predictor of university academic success in the US (Saunders-Scott, Braley, and Stennes-Spidahl 2018), the UK (Thiele et al. 2016) and Australia (Hattie 2009). The standard metric for entry into higher education in Australia is the Australian Tertiary Admissions Rank (ATAR), which has been found to be predictive of grade-point average (GPA), controlling for a student's socioeconomic background and the educational institution (Anderton and Chivers 2016;Messinis and Sheehan 2015). ...
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Background Self-regulated learning (SRL) encompasses the strategies and behaviours that allow students to transform cognitive abilities into task-specific academic skills. Research in higher education has found a relationship between SRL and academic outcomes. However, whether SRL improves as students gain educational experience in undergraduate science has not been adequately studied. It is also unclear whether traditionally strong predictors of academic performance, such as the Australian Tertiary Admissions Rank (ATAR), and science background, remain strong in the later stages of a science degree. Purpose The purpose of this study was to investigate whether SRL changes over time in undergraduate science, and whether SRL, the ATAR, or a student’s science background predicted their academic performance. Sample The sample comprised a cohort of agricultural science students (n = 213) from a large Australian University followed longitudinally from 2018 to 2020. Design and methods Students completed a questionnaire to assess SRL in their first and third years of undergraduate study. They also completed a knowledge survey at the start of first year to assess confidence in scientific material. Analyses revealed that students’ SRL increased over the degree, but not over a single semester of first year. Additionally, it was found that students’ average semester marks were related to knowledge survey scores, and with students’ ATARs, but not with their SRL. Conclusion These results indicate that the academic aptitudes that contribute to academic success in high school continue to be advantageous through to the end of undergraduate study, but also suggest that students’ initial scientific confidence may be particularly important to long-term university success.
Abstract Business management is a priority course in most universities given the thrust to develop entrepreneurs. Motivational profile of business management students in a state university in Cavite, Philippines was assessed and analyzed its relation to their academic performance. Achievement, affiliation and power based on McClelland’s theory (1960), were compared across sex groupings and determined the most dominant motivation per year level. Cochran’s sample size with .05 alpha level was used to obtain a total of 311 participants, descriptive statistics (mean, percentage, frequency count) and correlational analysis (Pearson r correlation and t-test) were used in analyzing the data. Outcomes show a substantial relationship between achievement motivation and students’ academic performance. Most participants are females, junior level and have very good academic performance. Results show a dominant motivational profile in the desire to achieve, followed by affiliation, lastly, power. Achievement motivation is highest for freshmen, juniors are affiliation-oriented and seniors are power-motivated. There is no significant difference between sex and motivational profiles, there is a weak negative correlation between achievement motivational profile and academic performance, no significant difference between affiliation and power motivational profiles and academic performance. Results imply that while studies show a positive correlation between motivational profiles and academic performance, other factors that may define academic performance, like teaching methodologies, can be maximized by schools. As student’s motivation shifts from achievement to affiliation to power, teachers may ensure that students have a healthy combination of activities that foster a sense of achievement, good interpersonal relationships, collaboration and healthy competition.
The purpose of this paper is to study whether Swedish admission policies successful in selecting the best-performing students. The Swedish universities select students based on two different instruments, which each form a separate admission group. A regression model is recommended to estimate the achievement differences for the marginally accepted students between the admission groups and is applied to a sample of 9024 Swedish university entrants in four different fields of education. Marginally accepted students in the group selected by school grades on average perform better than students accepted by an admission test, suggesting that a small reallocation of study positions towards the grade admission group may increase overall academic achievement. However, the achievement difference appears to vary concerning university programme selectivity. We found that increasing selection by grades in less competitive programmes would improve overall achievement, while we do not find any effect for highly competitive programmes.
In higher education, many students do not complete their studies within the term allotted. A Dutch university implemented an intervention aimed to reduce this form of academic procrastination. The intervention consisted of three measures: (1) requiring students to acquire all first-year credits within their first year in university, (2) reducing the number of resits, and (3) introducing compensation opportunities for insufficient grades. In this study, we investigated which groups of students (if any) benefited most from this intervention. We divided 29,629 students entering the university between 2009 and 2015 into subgroups based on their gender, ethnic background, and level of achievement during pre-university education. For each subgroup, we determined both first-year completion and three-year bachelor graduation rates, both before and after the introduction of the intervention. It was demonstrated that almost all subgroups profited from the intervention. Particularly students from subgroups that in the past performed less well showed much better first-year completion rates and much lower study delay rates. Dropout rates did not change significantly. For most subgroups, an effect of the intervention was still visible after three years: Three-year graduation rates were higher, although the effect was smaller than completion rates in the first academic year.
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Prediction of academic success at postsecondary institutions is an enduring issue for educational psychology. Traditional measures of high-school grade point average and high-stakes entrance examinations are valid predictors, especially of 1st-year college grades, yet a large amount of individual-differences variance remains unaccounted for. Studies of individual trait measures (e.g., personality, self-concept, motivation) have supported the potential for broad predictors of academic success, but integration across these approaches has been challenging. The current study tracks 589 undergraduates from their 1st semester through attrition or graduation (up to 8 years beyond their first semester). Based on an integrative trait-complex approach to assessment of cognitive, affective, and conative traits, patterns of facilitative and impeding roles in predicting academic success were predicted. We report on the validity of these broad trait complexes for predicting academic success (grades and attrition rates) in isolation and in the context of traditional predictors and indicators of domain knowledge (Advanced Placement [AP] exams). We also examine gender differences and trait complex by gender interactions for predicting college success and persistence in science, technology, engineering, and math (STEM) fields. Inclusion of trait-complex composite scores and average AP exam scores raised the prediction variance accounted for in college grades to 37%, a marked improvement over traditional prediction measures. Math/Science Self-Concept and Mastery/Organization trait complex profiles were also found to differ between men and women who had initial STEM major intentions but who left STEM for non-STEM majors. Implications for improving selection and identification of students at-risk for attrition are discussed. (PsycINFO Database Record (c) 2013 APA, all rights reserved)
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The authors propose a theoretical model linking emotions, self-regulated learning, and motivation to academic achievement. This model was tested with 5,805 undergraduate students. They completed the Self-Regulated Learning, Emotions, and Motivation Computerized Battery (LEM–B) composed of 3 self-report questionnaires: the Self-Regulated Learning Questionnaire (LQ), the Emotions Questionnaire (EQ), and the Motivation Questionnaire (MQ). The findings were consistent with the authors’ hypotheses and appeared to support all aspects of the proposed model. The structural equation model showed that students’ emotions influence their self-regulated learning and their motivation, and these, in turn, affect academic achievement. Thus, self-regulated learning and motivation mediate the effects of emotions on academic achievement. Moreover, positive emotions foster academic achievement only when they are mediated by self-regulated learning and motivation. The results are discussed with regard to the key role of emotions in academic settings and in terms of theoretical implications for researchers. (PsycINFO Database Record (c) 2014 APA, all rights reserved)
In the five years since the first edition of Injustice there have been devastating increases in poverty, hunger and destitution in the UK. Globally, the richest 1% have never held a greater share of world wealth, while the share of most of the other 99% has fallen in the last five years, with more and more people in debt, especially the young. Economic inequalities will persist and continue to grow for as long as we tolerate the injustices which underpin them. This fully rewritten and updated edition revisits Dorling’s claim that Beveridge’s five social evils are being replaced by five new tenets of injustice: elitism is efficient; exclusion is necessary; prejudice is natural; greed is good and despair is inevitable. By showing these beliefs are unfounded, Dorling offers hope of a more equal society. We are living in the most remarkable and dangerous times. With every year that passes it is more evident that Injustice is essential reading for anyone concerned with social justice and wants to do something about it.
Using innovative methdology, this book presents a consolidated review and interpretation of interdisciplinary research which examines how best to represent the multiple dimensions of the social, spatial and temporal processes that shape access to higher education. It sets out some relevant aspects of the changing HE policy-setting arena and presents a systematic framework for broadening participation and extending access in an era of variable fees.
The community cohesion agenda in Britain has focused attention on the ethnic character of neighbourhoods and how population change affects cohesion. This paper examines the relationship between neighbourhood ethnic group population change and belonging. The paper measures population change as immigration, gross internal migration and with a categorisation of ethnic group population dynamics that combines migration and natural change. Pooled 2005 and 2007 Citizenship Survey data are analysed using multilevel logistic regression models. The paper does not find evidence for relationships between immigration or local population turnover and levels of neighbourhood belonging; nor is there evidence that ethnically differentiated population change matters. However, belonging does vary by individual’s ethnicity; and strong belonging is associated with high co-ethnic density for minorities. In addition, the overall population change of an area may be significant: highest levels of belonging were found in areas of White and Minority population growth driven by migration.
The Spirit Level: Why More Equal Societies Almost Always Do Better is a landmark reference on the health of populations. This new book by Richard Wilkinson and Kate Pickett catalogues the accumulated research on income inequality. The premise of The Spirit Level is that well-being is patterned on something other than individual wealth. Rather, physical and social health is spread among particular groups according to a recurring pattern—that of income inequality. Where there are bigger differences in the distribution of wealth, undesirable health and social outcomes are more prevalent. Wilkinson and Pickett characterize income inequality in two ways. In analyses comparing “rich countries,” income inequality is the ratio between the average household income of the richest 20 percent and that of the poorest 20 percent; in the comparisons of US states, income inequality is measured using the Gini coefficient. The authors illustrate their argument regarding the importance of income inequality using ten different indicators of health and social well-being. According to this model, addressing a common determinant addresses the roots of multiple forms of disadvantage; by reducing income inequality, a society can reduce the incidence and prevalence of various social problems. In the book, the US and the UK are the most common examples as they are among the most unequal of unequal societies. Albeit to a lesser extent than in these other countries, income inequality is a growing concern in Canada; recent research has shown that income inequality in Canada increased between 1990 and 2000 (Picot and Myles 2005, 9). The implication is that income inequality is affecting individual and community well-being—the spirit level—in our society too. Because of the breadth of the topic and the diverse nature of the intended audience, the authors have necessarily abridged the research. The result is an accessible reader on what is known about income inequality. This accessibility is necessary for the stated goal of The Spirit Level. Wilkinson and Pickett have assumed the task of raising awareness about this body of evidence. They are convinced that raising awareness is a key stage in a process of change—that the evidence exists to be shared, and that informed actors are empowered actors. In describing their purpose, Wilkinson and Pickett engage the discourse of political action; they name the political nature of their goal and, in so doing, reinforce the strength of their contribution. In the face of all the evidence about problems that reduce quality of life, the narrative reflects a spirited can-do, even must-do, attitude toward reducing inequality: “Far from being inevitable and unstoppable, the sense of deterioration in social wellbeing and the quality of social relations in society is reversible. Understanding the effects of inequality means that we suddenly have a policy handle on the wellbeing of whole societies” (p. 33). Yet, without disparaging either the inspiring tone or the important connection to official policy action, the references to action are ambiguous and somewhat superficial. Based on the accumulated evidence, income inequality is, indeed, a focus for policy at multiple levels and by myriad actors. To “suddenly” have a “handle” on this issue, however, would require substantial additional information about successful interventions. And while it is useful to know that the level of trust is low where inequality is high, perhaps the more actionable analysis would be to examine where the level of trust is high and then ask why and how. Admittedly, these details are beyond the scope of an overview text like The Spirit Level. In the preface to their text, Wilkinson and Pickett propose Evidence-Based Politics as an alternate title. “At this stage,” they explain, “creating the political will to make society more equal is more important than pinning our colours to a particular set of policies to reduce inequality. Political will is dependent on the development of a vision of a better society which is both achievable and inspiring” (p. 264). The idea is that people will respond to the evidence with political action—be it partisan “Political” action or a civic “political” movement. Undoubtedly, political will is necessary to begin a process of change. Capacity and will to act are not uncomplicated, however. The authors acknowledge the...
Using longitudinal data from the 2001 cohort of applicants to the Gates Millennium Scholars (GMS) program, the authors examined scaled measures of academic and social engagement in relation to labor market earnings to test whether the economic value of student engagement among high-achieving students of color differs by student characteristics. Results confirm that academic and social engagement during college had differential effects on early career earnings. Findings suggest conditional effects of student engagement on labor market outcomes, providing evidence for individual and institutional decisions and theory building related to the lasting influence of student engagement in college.
Notwithstanding an ongoing concern about the low representation of certain groups in higher education, there is reluctance on the part of politicians and policy makers to adopt positive discrimination as an appropriate means of widening participation. This article offers an account of the different objections to positive discrimination and, thereafter, clarifies and criticises the view that universities ought to select those applicants who are expected to be most successful as students. It distinguishes arguments from meritocracy, desert, respect, and productivity and shows how these arguments are compatible with the use of positive discrimination in higher education.