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ARTICLE
Received 16 Oct 2014 |Accepted 1 May 2015 |Published 9 Jun 2015
A scalable goal-setting intervention closes both the
gender and ethnic minority achievement gap
Michaéla C Schippers1, Ad W A Scheepers2and Jordan B Peterson3
ABSTRACT The gender and ethnicity gap in academic achievement constitutes one of
today’s key social problems. The current study, therefore, assessed the effects of a brief,
evidence-based online intervention aimed at enhancing goal-directed conceptualization and
action among first year college students (N=703) at a large European business school. The
academic performance of these students was contrasted with that of three pre-intervention
control cohorts (N=896, 825 and 720), with particular attention paid to the role of gender
and ethnicity. The intervention boosted academic achievement and increased retention rates,
particularly for ethnic minority and male students (who had underperformed in previous
years). The gap in performance between men and women, and for ethnic minorities versus
nationals, became considerably smaller within the intervention cohort. After Year 1, the
gender gap closed by 98%, and the ethnicity gap by 38% (rising to 93% after the second
year). All groups in the intervention cohort performed significantly better than control
cohorts, but the effect was particularly large for males and ethnic minorities. The increase in
performance was largest for ethnic minority males: they earned 44% more credits, and their
retention rate increased 54%. Overall, the results indicate that a comprehensive goal-setting
intervention implemented early in students’academic careers can significantly and sub-
stantially reduce gender and ethnic minority inequalities in achievement.
DOI: 10.1057/palcomms.2015.14 OPEN
1Department of Technology & Operations Management, Rotterdam School of Management, Erasmus University, Rotterdam, The Netherlands 2Educational
Office, Rotterdam School of Management, Erasmus University, Rotterdam, The Netherlands 3Department of Psychology, University of Toronto, Toronto
Canada. Correspondence: (email: mschippers@rsm.nl)
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Introduction
The inequality in performance and life outcomes between
groups who differ in gender, ethnicity and socioeconomic
status (SES) constitutes one of the most pernicious current
social problems. Males, specifically, are not only under-
represented in post-secondary institutions, a phenomenon that
began to emerge in the 1980s, but complete their education at a
lower rate and obtain lower grades (Buchmann and DiPrete,
2008; Conger and Long, 2010). The gender gap first manifests
itself in high school, where females earn better grades and rank
higher in their classes. Several studies conclude that female
students tend to be more motivated when starting academic
programmes, spend more time studying than male students
and work harder (for example, Buchmann and DiPrete, 2008;
Richardson et al., 2012). Immigrant children in the European
Union (EU) and the United States perform more poorly in the
educational system regardless of grade, school type and age
(reviewed in Di Bartolomeo and Bonfanti, 2014), although
the data are sometimes equivocal. Finally, students from poorer
families obtain worse grades and lower standardized test scores,
miss more school, drop out of high school more frequently, and
are less likely to attend and/or graduate from post-secondary
institutions, although the effects are also dependent on timing,
depth and duration of poverty, as well as provision of learning
experiences at home (Brooks-Gunn et al., 2005). The existence of
ethnic-group-based inequality is often attributed to social factors
such as negative stereotyping (Cohen et al., 2009; Miyake et al.,
2010) and differences in peer and parental relations (Gore and
Aseltine, 2003). However, the relationship between such factors
and the gender gap remains unclear and, overall, the causes of
both gaps are poorly understood (cf. Buchmann and DiPrete,
2008).
Women reached parity with men in terms of college bachelor
degree completion in 1982. Since then, the gap favoring women
has grown, although the female advantage among minority
students appears to be much weaker (Buchmann and DiPrete,
2008). Many explanations have been offered for the former
phenomenon, such as growing gender egalitarianism and
declining discrimination. However, the causal factors have not
been identified with any real certainty (Buchmann and DiPrete,
2008). Women do enter college with higher high school grades,
and this might provide them with a head start (Conger and Long,
2010), but this fact cannot explain the origin of the gender gap
already extant in high school.
Despite the fact of this profound societal transformation,
research lags behind society, and the literature still often stresses
the female disadvantage in terms of education (Jacobs, 1996).
Much of it focuses on the increasingly small number of areas
where women are still under-represented, attributing this gap to
stereotype threat, moderated by gender identification, or the
importance placed on gender identity by women (Schmader,
2002). Thus, most interventions address the stereotype threat for
women regarding performance on subjects where men often excel
(for example, math, engineering), using such techniques as values
affirmation (writing about important values; for reviews see
Miyake et al., 2010; Cohen and Sherman, 2014; Walton, 2014),
and self-affirmation (writing about a valued self-relevant
characteristic such as sense of humour or social skills, Martens
et al., 2006). Both these particular interventions were effective in
enhancing female but not male performance (Cohen and
Sherman, 2014). Studies that do acknowledge the current
disadvantage of men, often propose that the under-
representation of men in teaching is a problem that needs to be
addressed by hiring more male teachers (for example, Arnot et al.,
1999), although there is no proof that this strategy has any effect
(Martin and Marsh, 2005; Carrington et al., 2008).
The achievement gap between European-American majority
and ethnic minority students remains large and concerning
(Jencks and Phillips, 1998). Ethnic minorities in the present
article are defined as the first and second generation population
with a non-Western (for example, African, Middle-Eastern,
Asian) foreign background (see method section for a more
elaborate description and definition; note that the majority in our
context are native Dutch students). The existence of this gap has
been attributed, in part, to psychological reasons, such as the
pervasive effect of negative stereotypes (as in the case of the
gender gap). Several studies have discussed the fact that
awareness of a negative stereotype about a given group’s
intelligence might depress academic engagement and perfor-
mance (for example, Aronson et al., 2002; Cohen et al., 2006;
Cohen et al., 2009). Such stereotype threat has apparently been
countered by informing students that intelligence is malleable,
rather than fixed (for example, Dweck, 1986; Aronson et al.,
2002), and by helping them reappraise the stereotype threat (for
example, Walton and Cohen, 2011). However, as Aronson et al.
(2002) indicate, many factors other than stereotyping may affect
academic performance.
Sociocultural factors, such as persistent economic inequality,
may also play a role. According to Reardon (2011), the income
achievement gap (academic performance differential between
children at the 90th versus 10th percentile of family earnings) is
now double that of the black–white gap. Five decades ago, by
contrast, the latter was 1.5 times the former. The stress produced
by such disparity, discussed in detail by Wilkinson and Pickett
(2009), appears to produce scepticism among those lower in the
SES hierarchy about the relationship between effort, ability and
life outcomes, with an attendant lack of motivation for
achievement. It is important to note, however, that neither the
psychological nor the sociocultural theories described above
account well for the emergence of higher female performance.
Despite the fact that the causes of gender and ethnic
performance gaps remain poorly understood, many initiatives
have been set in place to reduce these inequalities. One such
programme, designed to boost the sense of belonging among
marginalized college freshman groups, appears to be particularly
effective, halving the academic performance gap characteristic of
ethnic minorities (African Americans) over 3 years (Walton and
Cohen, 2011). The majority of interventions are merely assumed
to be successful, however, and their effectiveness is often not
assessed (Morisano et al., 2010). When they are, meta-analyses
indicate that their positive effects range from small to moderate
(Robbins et al., 2009), and a small minority have a lasting impact
on students’academic performance (Wilson, 2011). Furthermore,
few if any of the current interventions are conceptualized or
structured so that they might address the growing gender gap
favoring women, and they are frequently designed with a single
relatively homogeneous minority group (for example, African
Americans) in mind.
Finally, practical difficulties in employing most evidence-based
interventions are manifold, preventing wide-scale implementa-
tion. Many require extensive contextualization and careful
introduction, are often designed to target only one specific group
of underperformers, and are difficult to scale widely and
effectively (Wilson, 2011). Thus, it comes as little surprise that
overall college completion rates have remained relatively stable
over the past 20 years (Carey, 2004; Tinto, 2010), while the
establishment of a widely adopted means of improving academic
achievement and decreasing drop-out in post-secondary educa-
tion has remained a task for the future.
It is for such reasons that our research team has been assessing
the broad-scale effects of structured techniques to help students
formulate and articulate comprehensive goals. Goal setting, an
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intervention most frequently employed in business and corporate
environments, appears of high potential effectiveness (Schmidt,
2013), and can be simple and direct enough to avoid the pitfalls of
excess complexity, specificity and cost.
Goal setting
Goal-setting theory, developed in the mid-1960s by Edwin Locke,
provides one of the most influential and practical accounts of
motivation, in managerial and academic contexts (for a review see
Locke and Latham, 2006). Hundreds of articles have supported
the theory’s main claims. The establishment of specific, challen-
ging goals clearly stimulates goal-directed behavior and boosts
performance (Locke and Latham, 1990; Zimmerman and Schunk,
2001). Conscious goal setting appears to increase the probability
of goal-directed action and goal-related outcomes, while the acts
of mentally contrasting positive and negative outcomes and
considering how goal-related obstacles might be overcome
appears to free up mental resources, improving student self-
discipline and performance (Duckworth et al., 2013). Goal setting
is clearly related to individual performance improvement (Locke
and Latham, 2013) and to overall organizational productivity.
Schmidt (2013), for example, used utility analysis during his
meta-analysis of studies including more than 20,000 participants,
concluding that employers could assume a 10% increase in
productivity following goal-setting exercises helping workers
establish realistic, but difficult and specific goals. Programmes
aimed at helping individuals set and elaborate on long-term
personal goals have proved particularly successful (Morisano
et al., 2010; Travers et al., 2014).
A well-designed goal-setting programme links specific subgoals
to more general, comprehensive goals and, likewise, proximal
subgoals to their more distal counterparts (for example, Latham
and Brown, 2006; Locke and Latham, 2006). Latham and Brown
(2006) demonstrated, for example, that goal setting enhanced
academic performance of MBA students who set their own
proximal and distal learning goals (compared with students who
merely set distal goals, or were urged to do their best). Other
researchers have shown that for students with clear goals, goal-
conflicting temptations seem to be able to strengthen goal-
directed behavior, instead of weaken it, since these temptations
activate the “larger”goals this information conflicts with and thus
tend to inhibit giving in to temptations. For instance, students
who have set clear goals are better able to withstand the
temptation to procrastinate or to distract themselves with other
activities (for example, watching TV; cf. Kruglanski et al., 2002).
Thus, goal setting seems to enhance what has been broadly
defined as self-regulation (Latham and Locke, 1991; Oettingen
et al., 2000; Zimmerman and Schunk, 2001). For this to occur,
goals must be challenging enough to inspire the quest for their
attainment, but not so difficult that failure is probable (Locke and
Latham, 2002). Levels of perceived self-efficacy increase, as
progress is made, and the sense of accomplishment rises
(cf. Latham and Seijts, 1999; Latham and Brown, 2006).
This is all in logical keeping with cognitive and neuropsycho-
logically predicated models of motivation. The establishment of a
goal tags behavior relevant to that goal with dopaminergically
mediated and rewarding incentive significance, motivating
approach behavior (Gray, 1982). Complex hierarchies of such
goals must be brought into being, for effective, complex
functioning (Powers, 1973; Carver and Scheier, 1998), and the
establishment of such a hierarchy tags specific, implementable
subgoals with the positive affective significance of the super-
ordinate goals they serve (Peterson, 1999).
The mental structures subsuming goal-directed action focus
perception, specify the objects of attention, determine the
emotional significance of ongoing events (as these are evaluated
in relationship to the current goal-framework; Deci et al., 1991;
Deci and Ryan, 2000; Hirsh et al., 2013). They also facilitate
employment of task-relevant knowledge and strategies, and direct
action towards goal-relevant activities, increasing energy, persistence
and motivation (Locke et al., 1981; Locke and Latham, 1990, 2002;
see also Smith et al., 1990). Well-organized and articulated personal
goal hierarchies also appear to constrain entropy and uncertainty,
decreasing the probability that anxiety and avoidance will
compromise both health and productivity (Hirsh et al., 2012). Thus,
such structures appear both to enhance positive meaning, in the
manner described above, as well as to delimit negative meaning, by
restricting threat and anxiety (Gray, 1982).
Morisano et al. (2010) put these ideas into practice by
contrasting the effects of a detailed, explicit and written goal-
setting intervention with those of an extensive written non-goal-
oriented intervention among university students on academic
probation. Goal setting markedly improved the grade point
average in the former group, as well as substantively increasing
the proportion of students who stayed enroled full-time. This
study indicated that goal setting can work effectively and
efficiently to enhance academic performance, and suggested that
it might be particularly effective among struggling students.
The online goal-setting intervention. The intervention reported
here, an elaborated goal-setting programme (see http://www.self
authoring.com; future authoring), was designed with all due sci-
entific and practical considerations in mind. It is a package
intervention, with elements derived from goal-setting theory, as
detailed above, and from the extensive published work on the
salutary effects of expressive writing (Pennebaker and Chung,
2011; for reviews see Smyth, 1998; Pennebaker et al., 2003). It also
included elements derived from Darwinian-inspired creativity
models (Simonton, 1999) such that students were asked, in
Stage 1, to formulate, step by step, a loose, inclusive initial vision
and counter-vision and then, in Stage 2, to articulate that vision
in a detailed, edited and prioritized fashion, and to fortify their
commitments with careful arguments, so they could deal with
opposition, doubts and practical difficulties. While completing
these two stages, participants were encouraged to consider the
nature and desired quality of their future experience on a number
of important life dimensions—family, intimate relationships,
activity outside of work, career, education and so on. In this
manner, participants emerged from the process with an imple-
mentable, practical, coherent and defensible plan. Later, partici-
pating students scheduled a 10 min session with a professional
photographer, for a picture combined with a motivational state-
ment. This was Stage 3, and the final step, of the process (see
Supplementary Material for a more elaborate description of the
intervention).
The intervention helped participants to produce clear and
specific goals, instead of more general “do your best”goals
(Austin and Vancouver, 1996; Locke and Latham, 2002), helped
them to avoid potential goal conflicts (Locke et al., 1994), and
encouraged them to assess whether their goals were practical and
attainable (Locke and Latham, 2002). It required them to make a
mental comparison of the future and the present and to develop
“if-then”strategies for dealing with potential obstacles
(Duckworth et al., 2013), and enabled them to form plans for
assessing and monitoring progress towards their goals (Schunk,
1990). Participants were also asked to produce and make public a
single summary goal statement, in an attempt to use social
pressure to increase goal commitment.
The programme is (1) cost-effective, fully scalable and available
online (Stages 1 and 2), (2) requires little if any potentially
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expensive and time-consuming contextualizing, (3) is equally
applicable, in theory, to all groups of underperforming students,
despite the putatively diverse origins of inequality and (4) has
previously been shown, in a randomized control study, to be
effective, specifically although not necessarily exclusively, for
struggling students (Morisano et al., 2010). Other researchers have
noted, similarly, that goal reflection may be particularly useful for
non-thriving students (Zimmerman, 2002). Underperforming
students may be in particular need of clear goals to self-regulate:
to increase on-task time and focused action, for example.
The male-skewed tendency towards resistance to authority
during childhood and adolescence (Hudziak et al., 2003; López-
Romero and Romero, 2010) may also make it more necessary for
males to set personally relevant goals to motivate themselves to
perform well. The problems of values, ambition and broader
context associated with the conflicts inherent in integration may
also increase goal-related conflict for visible ethnic minorities
attempting to adapt themselves to their new cultural surround-
ings. In consequence, we believed that the goal-setting exercise
might prove particularly useful for disadvantaged groups, and
expected reductions in both the post-intervention gender and
ethnicity gaps.
We hoped that the short-term effects of success during goal
pursuit might result in positive long-term effects, in a “recursive
virtuous cycle”of study success (Walton and Cohen, 2011). Given
that the intervention specifically targeted personal goal pursuit
(including that associated with academic performance), which is
logically related to important subsidiary factors such as time
on task, assignment completion and exam attendance, we also
expected that its effects would manifest themselves sooner
than those produced by other less direct interventions relating
to social belonging or values affirmation (Miyake et al., 2010).
We therefore report here the results of a structured written
goal-setting intervention, completed online; this was aimed at
enhancing the performance of lower-performing students, and
was applied to a large population of students, followed over a
2-year period.
Methods
The study made use of existing data derived from a larger data-gathering effort
(see Schippers et al., 2014). Its design was quasi-experimental, comparing the
academic performance of a post-intervention cohort with that of three pre-
intervention control cohorts. The methods were carried out in accordance with the
approved American Psychological Association guidelines and all (quasi-)
experimental protocols were approved by the research school’s (ERIM)
institutional review board.
“Ethnic minorities”, for the purposes of article, were defined as the first and
second generation population with a non-Western (for example, African, Middle-
Eastern, Asian) foreign background. This is the definition most commonly used by
the Dutch governmental Statistics Body (CBS) to distinguish between ethnic
majority (that is, Dutch) and ethnic minorities. It has been widely applied by
ministries, local governments and media in the Netherlands (Alders, 2001).
Subjects were recently enrolled first-year students (N=703 for the intervention
cohort and N=896, 825 and 720, respectively, for the pre-intervention control
cohorts). We could not specify the ethnicity of 44 students (6.3%). Thus, from the
659 students used in the analysis of the intervention cohort sample, 20% of the
students belonged to the non-Western ethnic minority group. The final samples for
the pre-intervention control cohorts were 841, 723 and 656 from pre-intervention
years 1, 2 and 3, with 18, 21 and 19% of the students, respectively, belonging to the
ethnic minority.1The remaining population in the Netherlands is considered
native Dutch and defined here as ethnic majority. As is typical of many business
schools, 72%, 69% and 71% of the pre-intervention cohorts at the Rotterdam
School of Management (RSM) were male, as well as 71% of the students in the
intervention cohort.
Since the study made use of existing data, obtaining prior consent was not
feasible. Therefore, and because the intervention was an integral part of the
academic programme, students were informed by e-mail of the purposes and
preliminary outcomes of the study, and were given the chance to opt out. Five
students indicated that they wanted to opt out; these students were removed from
the database.
The participating university relies on the European Credit Transfer and
Accumulation System (ECTS), a system that awards standardized course credits in
accordance with work required to achieve the objectives of a given higher education
programme. The system was designed to ease cross-institutional transfer and
academic progression across the EU and in certain other countries. ECTS credits
are awarded for successfully completed programs. Number of ECTS credits earned
is a primary measure of academic achievement, standardized across the EU
(Grosges and Barchiesi, 2007), within the ECTS (http://ec.europa.eu/education/
tools/ects_en.htm), designed to represent the student workload required to achieve
the objectives of a given study programme, and incorporating a standardized ECTS
grading scale. Within this system, credits can only be awarded when a pass grade is
obtained for a course of a specified time requirement. The full course load in a
standardized European academic year corresponds to 60 ECTS credits, equivalent
to 1,680 h of study.
Under the “Binding Study Advice”(BSA) system, in effect since 1999, students in
the business programme are required to achieve 40 out of 60 ECTS in their first year
to continue to the second. Furthermore, by the end of their second year, students
must have attained all 60 first-year ECTS otherwise they must leave the programme.
The programme suffers from high drop-out rates, in part because differential
selection for programme entrance is strictly limited by Dutch law, and in part
because of the stringent nature of the BSA system. Drop-out rates approximate
50% in the first year, with substantial gender and ethnic differences (for example,
62% of the ethnic minority students dropped out in the year preceding the
intervention), but declines substantially after the first year. Despite the BSA system,
students typically complete the 3-year bachelor programme in 4 years, with outliers
requiring up to 7 years.
College academic records.Gender and ethnicity data were gathered using uni-
versity transcripts. Official university transcripts were collected for all participants
in the intervention cohort at the end of the first and second year, providing
information on the number of ECTS (credits) obtained and retention rate.
The goal-setting intervention.The goal-setting intervention was delivered as part
of the curriculum for a full cohort of students in the first trimester of their first
year. The effects of the intervention were assessed by contrasting that cohort’s
performance and enrolment status with that of the average performance and
enrolment of three previous pre-intervention control cohorts. We chose 3 years of
control cohorts for several reasons: (1) the curriculum had been substantively
altered and updated precisely 3 years before the intervention cohort, (2) the uni-
versity rules and the curriculum remained virtually unchanged during the 3-year
period after those changes, offering a nearly ideal window in which to compare the
different cohorts and (3) it allowed us to control for natural fluctuations in aca-
demic performance across different cohorts provided with the same curriculum.
The intervention required two sessions (Stages 1 and 2, described previously,
below, and in the Supplementary Material) of about 2 h each. This was followed by
a subsequent 10 min visit to a professional photographer (Stage 3) for a photo to be
combined with a single goal statement chosen by the student and then made public
(part of an “I WILL”motivational initiative already in place at the university).
Students were provided with an individual login code and information about how
and when to complete the homework assignment. They were explicitly instructed
to complete Stage 1 in one uninterrupted session, and Stages 2 and 3 in a second
session.
Stage 1 of the online intervention guided students to think in a structured way
about habits they would like to improve, what they might like to learn, their social,
leisure and family lives, and their future career. Then they were asked to write
freely for about 15 min (without worrying unduly about grammatical niceties),
about the life that would be good for them 3–5 years in the future, assuming that
things were going as well as realistically possible. Following this, they were asked to
write for the same length of time about the future they would truly want to avoid,
but that could arise if bad habits or lack of discipline took them in the wrong
direction. In Stage 2, students were asked to define and describe their overall plan
for the future, beginning by specifying, clarifying, defining and prioritizing eight
specific goals relating to their initial vision and counter-vision. They were then
asked to articulate their motives, to consider in detail the potential personal and
social impact of their goals, to describe detailed strategies for goal attainment and
to delineate clearly a strategy for monitoring progress towards those goals. Stage 3
comprised the photo and “I WILL”statement with their ambition and goals, as a
form of public commitment (Hollenbeck et al., 1989) transmitted through campus
posters and via social media such as Facebook. A more detailed description of the
intervention is available in the Supplementary Material.
Delivery of intervention (first semester of Year 1).Three weeks after the start
of Year 1, students received a login from their tutors as part of the introductory
course on Management Skills. The tutors described the purpose of the intervention
to their groups of 20–25 first-year students. Students were required to finish Stage 1
within 2 weeks, and Stage 2 within 4 weeks, so in effect Stage 1 was due 5 weeks
and Stage 2 7 weeks after college entry. Stage 3, the “I WILL”initiative was
completed 1–2 weeks after that. After completing the online part of the pro-
gramme, they were asked to print out their work and show it to their tutor, in
fulfillment of the course requirement. The tutor made a note of each student’s
participation. Participation in Stage 3 was obtained from the university records.
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Results
We analysed the progress of students using two primary
dependent variables: number of ECTS credits earned (as
described previously) and retention rate.
In line with our expectations, analysis of variance (ANOVA)
between cohorts revealed (1) a significant increase for
the intervention cohort in number of credits earned for
male majority students [M
control cohorts
=33.32 (SD =20.45),
M
intervention cohort
=40.90 (SD =18.04), Cohen’sd=0.39, F(1,
1506) =37.96, P=0.000] and (2) a significant increase in number
of credits earned for male minority students [M
control
cohorts
=26.42 (SD =20.01), M
intervention cohort
=37.95 (SD =
20.49), Cohen’sd=0.57, F(1, 336) =19.85, P=0.000].
No significant increase was apparent, however, for either
female majority students [M=40.77 (SD =19.09), M
intervention
cohort
=42.87 (SD =20.80), F(1, 544) =1.10, P=0.294] or
female minority students [M
control cohorts
=28.24 (SD =19.78),
M
intervention cohort
=34.06 (SD =22.26), F(1, 216) =3.22,
P=0.074].
Within the cohorts, separate univariate ANOVA revealed that
the intervention reduced or virtually eliminated many of the
academic achievement differences evident in the subgroups
within the control cohorts. In the control cohorts, for example,
there were significant main effects of gender [M
male
=32.07
(SD =20.54), M
female
=37.24 (SD =20.08), Cohen’sd=0.25, F(1,
2016) =16.30, P=0.000] and ethnicity [M
majority
=35.30 (SD =
20.36), M
minority
=27.13 (SD =19.92), Cohen’sd=0.41, F(1,
2016) =71.55, P=0.000], as well as a significant interaction
between gender and ethnicity [M
majority male
=33.32 (SD =20.45),
M
majority female
=40.77 (SD =19.09), Cohen’sd=0.38; M
minority
male
=26.42 (SD =20.01), M
minority female
=28.24 (SD =19.78),
Cohen’sd=0.09, F(1, 2016) =6.03, P=0.014].
As predicted, however, the gender effect was no longer
significant in the intervention cohort [M
male
=40.34 (SD =18.62),
M
female
=40.25 (SD =21.56), Cohen’sd=0.01, F(1, 592) =0.23,
P=0.633]. Furthermore, the effect for ethnicity was much
reduced, although it remained significant [M
majority
=41.43
(SD =18.88), M
minority
=36.40 (SD =21.21), Cohen’sd=0.25, F
(1, 592) =8.55, P=0.004]. The gender by ethnicity interaction
was also no longer significant [M
majority male
=40.90 (SD =18.14),
M
majority female
=42.87 (SD =20.80); M
minority male
=37.95 (SD =
20.49), Cohen’sd=0.10, M
minority female
=34.06 (SD =22.26),
Cohen’sd=0.18, F(1, 592) =2.12, P=0.146]. These results
indicate that both the gender and ethnicity gap were reduced.
More detailed analyses (see Table 1; Figure 1) revealed that the
remaining ethnicity effect in the intervention cohort was
significant only between the majority male and females on the
one hand, and the female minority students on the other.
No significant differences remained between male and female
majority students, between male and majority and minority
students, between female majority students and male minority
students, nor between male and female minority students (Fig. 1;
Table 1).
Between cohorts, χ2analysis with retention (percentage of
cohort) as the dependent variable revealed that retention was
significantly increased for the male majority students [Retention-
control cohorts
=56.5%, Retention
intervention cohort
=72.9%, χ2(1,
N=1509) =29.57, P=0.000] and for the male minority students
[Retention
control cohorts
=43.6%, Retention
intervention cohort
=67.1%,
χ2(1, N=338) =13.33, P=0.000]. There was no significant
increase for the female majority students [Retention
control
cohorts
=70.0%, Retention
intervention cohort
=77.2%, χ2(1,
N=546) =2.47, P=0.116]. Retention rate change among the
female minority students was marginally significant [Retention-
control cohorts
=44.6%, Retention
intervention cohort
=59.6%, χ2(1,
N=218) =3.59; P=0.058].
Table 1 |Comparison of differences between subgroups in number of credits and retention rate after Year 1 between the control cohorts (combined) and intervention cohorts
Control, N Intervention, N Control (mean and SD—no.
of credits)
Intervention (mean and SD—
no. of credits)
Performance increase
(%)
Majority
males
Majority females Ethnic minority
males
Ethnic minority
females
Majority males 1,169 339 33.32 (20.45) 40.90 (18.14) 23 —t=−6.75*** t=4.99*** t=3.08**
Majority females 423 123 40.77 (19.09) 42.87 (20.80) 5 t=−0.93 —t=9.25*** t=6.99***
Ethnic minority
males
259 79 26.42 (20.01) 37.95 (20.49) 44 t=1.66 t=1.66* —t=−0.92
Ethnic minority
females
166 52 28.24 (19.78) 34.06 (22.26) 21 t=−0.93 t=1.18 t=2.11* —
Control, N Intervention, N Control retention (%) Intervention retention (%) Increase(%) Majority males Majority females Ethnic minority males Ethnic minority females
Majority males 1,169 339 56.5 72.9 29 —χ2=23.37*** χ2=14.25*** χ2=8.41**
Majority females 423 123 70.0 77.2 10 χ2=0.87 —χ2=46.52*** χ2=32.92***
Ethnic minority males 259 79 43.6 67.1 54 χ2=1.09 χ2=2.53 —χ2=0.04
Ethnic minority females 166 52 44.6 59.6 46 χ2=3.90* χ2=5.63* χ2=0.76 —
*Po0.05; **Po0.01; ***Po0.001.
Notes: t-tests of academic performance differences are shown in the upper part of the table (no. of credits; equal variances not assumed; all df’s are 1); and chi squares of differences in retention rate in the lower part of the table. Values and significance levels of the control
cohorts comparison appear above the diagonals, comparison of intervention cohort below the diagonals. The differences in the intervention cohort between males and females, and between the majority and minority groups have become significantly smaller and are non-
significant for most subgroups.
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Within the cohorts, whereas retention rate differences between
the pre-intervention groups were significant [Retention
majority
male
=56.5%, Retention
majority female
=70.0%, Retention
minority
male
=43.6%, Retention
minority female
=44.6%), χ2(3, N=2017) =
58.34, P=0.001], the differences in the intervention cohort were
reduced below statistical significance [Retention
majority male
=72.9%, Retention
majority female
=77.2%, Retention
minority male
=
67.1%, Retention
minority female
=59.6%, χ2(3, N=593) =6.70,
P=0.082]. This indicated that in the intervention cohort, the
differences between the subgroups consisting of gender and
ethnicity with respect to retention rate were significantly reduced.
This was despite the fact that all subgroups in the intervention
cohort improved to some extent, with groups that previously
performed worst improving most. The increase in percentages
academic performance and retention rate were impressive,
ranging from 5 to 34% for number of credits earned and from
10 to 54% for retention rate (see Table 1).
Closing the gender and ethnicity gaps. Detailed analyses com-
paring each two subgroups within the control cohorts as well as in
the intervention cohort further indicated that the goal-setting
intervention reduced the gender and ethnic gap in retention. In
the control cohorts, the differences between every subgroup were
highly significant except for the difference between minority
males and minority females. There were no longer significant
differences between the subgroups In the intervention cohort,
with the exception of small differences between majority males
and females and minority females. However, even these differ-
ences were significantly smaller than in the pre-intervention
cohorts (see Table 1; Fig. 2).
After the first year, there was a marked reduction in the gender
gap in performance with respect to the number of credits earned
after 1 year. In the control cohorts, there was a difference of 5.17
ECTS between female (M=37.24 ECTS, SD =20.08) and male
students (M=32.07 ECTS, SD =20.54), Cohen’sd=0.26. The
intervention cohort difference shrank to a mere 0.09 ECTS
between female (M=40.25 ECTS, SD =21.56) and male students
(M=40.34 ECTS, SD =18.62), Cohen’sd=0.01, for a reduction
of 98.25% (Fig. 2a). To check if this effect was lasting—that
student performance did not decline during Year 2—we
calculated the difference in the number of credits earned in
Year 2. Analyses showed that this was essentially equivalent for
the control cohorts (the difference was 3.68 ECTS) and the
intervention cohort (the difference was 3.49 ECTS); a reduction
of 5%, meaning that the gap did not widen in Year 2.
Furthermore, there was a marked reduction in the gender gap
with respect to retention after 1 year. In the control cohorts, there
was a difference of 8.6 percentage points between female students
(Retention =62.8%) and male students (Retention =54.2%). The
post-intervention retention difference shrank to 0.2 percentage
points in the intervention cohort (Retention
female students
=72.0%;
Retention
male students
=71.8%), a gender gap reduction of 97.67%.
Thus, the goal-setting intervention virtually eliminated the gender
gap in retention and number of credits earned (a reduction of
approximately 98%). The effect on retention also seemed to be
lasting. In the control cohorts the difference between female
(59.3%) and male students (48.6%) was 10.7 percentage points
20.0
25.0
30.0
35.0
40.0
45.0
control cohort 1 control cohort 2 control cohort 3 intervention cohort
a
female male
20.0
25.0
30.0
35.0
40.0
45.0
b
control cohort 1 control cohort 2 control cohort 3 intervention cohort
majority ethnic minority
c
20.0
25.0
30.0
35.0
40.0
45.0
control cohort 1 control cohort 2 control cohort 3 interven tion cohort
majority male
majority female
ethnic minority male
ethnic minority female
Figure 1 |Number of credits (ECTS) earned after the first academic year
by gender, ethnicity and cohort. (a) While the three pre-intervention
control cohorts show a consistent gender gap, this gap closes almost
completely in the intervention cohort, even though all students in the
intervention cohort participated. (b) While the ethnicity gap seems to
widen rather than close in the pre-intervention control cohorts, in the
intervention cohort the gap closes significantly. (c) The interaction
between gender and ethnicity shows that while both gaps diminish in the
intervention cohort, the largest performance gain is achieved by male
minorities.
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after 2 years. In the intervention cohort, by contrast, the
difference with regard to retention rate between female (68.0%)
and male students (66.8%) was 1.2 percentage points (a gender
retention difference of 1.8%). This is a reduction of 88.78%
(Fig. 2b).
The ethnic performance gap in number of credits earned and
retention was also reduced significantly in the intervention
cohort. In the control cohorts, after 1 year, there was a difference
of 8.17 ECTS between the majority (M=35.30, SD =20.36) and
minority students (M=27.13, SD =19.92), Cohen’sd=0.41. In
the intervention cohort, by contrast, there was a difference of 5.03
ECTS (Credits
majority
M=41.43, SD =18.88; Credits
minority
M=36.40, SD =21.21), Cohen’sd=0.25, a gap reduction of
38.43%. After 2 years, in the control cohorts, there was a
difference of 3.74 ECTS between the majority (M=47.84) and
minority students (M=44.10). In the intervention cohort, the
majority students underperformed slightly in comparison with
the minority students (by 0.27 ECTS) (Credits
majority
M=48.62;
Credits
minority
M=48.88). This was a change of 107.2% (Fig. 2a).
The retention in the control cohorts, after 1 year, differed by 16.1
percentage points (Retention
majority students
=60.1%; Retention
minority
students
=44.0%). In the intervention cohort, by contrast, the
difference was 10.0 percentage points (Retention
majority students
=
74.1%; Retention
minority students
=64.1%), a reduction of the ethnicity
gap by 37.88%. After 2 years, in the control cohorts, retention rates
differed by 16.4 percentage points (Retention
majority students
=55.2%;
Retention
minority students
=38.8%). After 2 years, retention in the
intervention cohort differed by 10.7 percentage points (Retention
ma-
jority students
=69.5%; Retention
minority students
=58.8%), a reduction of
34.75% (Fig. 2b). Overall, the difference regarding gender and
ethnicity seems to diminish significantly and disappear altogether
with respect to gender after the first academic year (Fig. 3).
The goal-setting intervention therefore appears to have closed
the ethnicity gap by approximately 38% in both retention and
number of credits earned after 1 year. The ethnicity gap took
somewhat longer to close than the gender gap, taking 2 years
instead of 1 to close almost completely, by 93%.
Additional analyses. There is often a gap between important
goals that people have set and their actual goal attainment (Webb
and Sheeran, 2007). A significant body of research has shown that
the relationship between goal setting and performance is medi-
ated by factors such as attention to goal-relevant activities, per-
sistence and the discovery of task strategies to facilitate goal
achievement (Zimmerman and Schunk, 2001). Locke and Kristof
(1996) showed, for example, that students who achieved higher
grades tended to use well-specified study methods and often
completed all their assigned work. These students seem to have
established the more specific achievement goals that typically
leading to better performance than vague or general goals, such as
try to “do your best”(Locke and Latham, 2002).
The current goal-setting intervention was aimed at getting
students to reflect on their general, higher-order life goals,
prioritize them, form implementation intentions and monitor
goal attainment. Presumably, self-regulation becomes easier when
the specifics of course work were viewed by students in the
context of such globally important, broader life goals. Indeed, the
study of Morisano et al. (2010) showed that it was participation in
the goal-setting programme, per se, rather than the number of
academic goals specified, that was important in relation to
academic achievement. We also attempted to determine what
more specific factors might have produced these changes in
number of credits attained and rate of retention.
University rules governing our participant students allowed
them to skip regular exams and wait for later, programmed exam
re-sits, instead. In the first year, students can take a maximum of 12
regular exams, but are allowed to defer these until later, if
necessary. These exam-related rules allow for flexibility with
regards to sudden, unexpected life events, but also enable counter-
productive avoidance behavior and procrastination. Since diploma
completion is an important goal for most students, they must stay
on track, instead of procrastinating (for example, waiting for the
re-sits instead of taking part in regular exams; cf. Bayer et al.,
2010). Thus, we hypothesized that any decrease in the number of
re-sits taken by the students after completing the intervention
might be a marker for increased commitment to achievement and
career (reflected in a decrease in procrastination). We also
hypothesized that struggling students, in particular—males and
ethnic minorities—would be better able to prioritize their goals,
after completing the intervention, and would therefore be inclined
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
gap credits
(ECTS) year 1
gap credits
(ECTS) year 2
gap credits
(ECTS) year 1
gap credits
(ECTS) year 2
gender ethnicity
a
combined control cohorts intervention cohort
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
b
gap retention
year 1
gap retention
year 2
gap retention
year 1
gap retention
year 2
gender ethnicity
combined control cohorts intervention cohort
Figure 2 |Closing of the gender and ethnicity gap as a function of
academic year, for number of credits (ECTS) earned and retention rate.
(a) With respect to gender, the gap in number of credits earned closes
altogether after the first academic year, and increases slightly after Year
2. For ethnicity, the gap closes considerably after Year 1 and even more
after Year 2. (b) With respect to gender, the gap in retention rate closes
altogether after the first academic year, and opens slightly after Year 2.
For ethnicity, the gap closes considerably after Year 1, and remains
stable in Year 2.
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to participate more regularly in the study programme, avoiding
re-sits or other means of postponing exams.2
Univariate ANOVA indeed revealed that in the control cohorts
there was a significant gender effect [M
male
=10.32 (SD =2.79),
M
female
=10.84 (SD =2.30), Cohen’sd=0.20, F(1, 1740) =11.99,
P=0.001] and a significant ethnicity effect [M
majority
=10.67
(SD =2.45), M
minority
=9.74 (SD =3.25), Cohen’sd=0.32, F(1,
1740) =41.05, P=0.000] in relation to number of exams taken.
There was no significant interaction effect between gender and
ethnicity [M
majority male
=10.49 (SD =2.62), M
majority female
=
11.16 (SD =1.85), M
minority male
=9.56 (SD =3.37), M
minority
female
=10.01 (SD =3.05), F(1,1740) =0.45, P=0.503].
In the intervention cohort, however, neither the gender effect
nor the ethnicity effect remained significant: [M
male
=10.44
(SD =3.01), M
female
=10.05 (SD =4.03), Cohen’sd=0.11,
F(1, 574) =1.64, P=0.201] and [M
majority
=10.47 (SD =3.10),
M
minority
=9.83 (SD =4.06), Cohen’sd=0.18, F(1, 574) =3.66,
P=0.056]. Similar to the pre-intervention cohorts, the gender
ethnicity interaction effect in the intervention cohort was not
significant [M
majority male
=10.52 (SD =2.77), M
majority female
=
10.34 (SD =3.88), Cohen’sd=0.05, M
minority male
=10.12 (SD =
3.88), M
minority female
=9.40 (SD =4.33), Cohen’sd=0.18,
F(1574) =0.58, P=0.445].
Detailed analyses (see t-tests in Table 2) showed that in the
control cohorts there were significant effects between all
subgroups, except between majority males and minority females
and between minority males and minority females. In the
intervention cohort no significant differences remained between
any of the subgroups. This may help explain, practically, why the
gender and ethnicity gap closes after the intervention: the groups
of students that performed worse in previous, pre-intervention
cohorts now take exams at a rate equivalent to the previously
higher-performing groups. This suggests that these groups are
now characterized by enhanced self-regulation. As a result, their
academic integration increased (Rienties et al., 2012).
Discussion
In this article, we assessed the effects of an online goal-setting
programme on the academic performance of a full cohort of
undergraduate management students. We specifically concen-
trated on and contrasted the performance of primary gender and
ethnicity subgroups in the intervention cohort with those of
previous cohorts. In our research, we took a novel approach to
goal-setting theory and stated that formulating life goals will help
students enhance their academic performance. We hypothesized
that the performance enhancement produced by this programme
would be especially pronounced for previously poor-performing
students, and that it might help redress both the gender and
ethnicity gap. The results indicated that these hypotheses were
well-founded: substantive performance gaps can be closed,
apparently regardless of their origin, with a generic, scalable
online intervention. Furthermore, the effects of the intervention
manifest themselves within a single academic year. In addition,
although participants benefited, generally, in terms of academic
performance, the (comparatively underperforming) male and
ethnic minority students showed the greatest improvement. This
speaks well to the generalizability of the process, of which the
effectiveness has now been demonstrated in Canada (Morisano
et al., 2010)—as well as in the Netherlands, and in the present
study with males and females and an ethnically diverse student
population. After Year 1, the gender gap closed by 98%, while the
ethnicity gap closed by 38%, rising to 93% after Year 2. Ethnic
minority males earned 44% more credits, and their retention rate
increased 54%. Whereas the meta-analysis of Robbins et al.
(2009) concluded that most interventions show relatively modest
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
control cohort 1 control cohort 2 control cohort 3 intervention cohort
a
female male
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
b
control cohort 1 control cohort 2 control cohort 3 intervention cohort
majority ethnic minority
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
c
control cohort 1 control cohort 2 control cohort 3 intervention cohort
majority male
majority female
ethnic minority male
ethnic minority female
Figure 3 |Retention rate after the first academic year by gender,
ethnicity and cohort. (a) While the three pre-intervention control cohorts
show a consistent gender gap, this gap closes almost completely in the
intervention cohort, despite the participation of all students in the
intervention cohort. (b) While the ethnic minority gap seems to widen,
rather than close, in the control cohorts, in the intervention cohort the
ethnicity gap closes significantly. (c) The combination of gender and
ethnicity shows that both gaps diminish in the intervention cohort, while
the largest gain in retention is evident in the male ethnic minorities.
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effects, we found that our intervention cohorts showed substantial
increases, ranging from 5 to 44% for various subgroups in terms
of academic performance and from 10 to 54% in terms of
retention rates, compared with the pre-intervention cohorts.
It thus appears that the goal-setting intervention assessed in
this study has a strong salutary effect, improving academic
performance and decreasing drop-out, particularly among male
students, generally, and among ethnic minority male and female
students, more specifically. Given that students had to obtain a
minimum of 40 ECTS credits in Year 1 to continue to Year 2, the
performance improvement was of particular consequence.
Interestingly, the positive effect of the intervention continued to
increase in Year 2 among ethnic minority students.
Most previous studies reporting performance improvement
addressed either the gender or ethnicity gap (Miyake et al., 2010;
Walton and Cohen, 2011), but not both. The goal-setting
intervention appears to have addressed both simultaneously,
and relatively comprehensively. Furthermore, the current inter-
vention addressed a relatively heterogeneous minority group, as
opposed to a relatively homogeneous minority group (for
example, African-American students; Cohen et al., 2006; Cohen
et al., 2009; Walton and Cohen, 2011). Furthermore, the effects of
the intervention manifested themselves within a relatively short
period of time. Within a single year, the gender inequality gap
was almost closed, while the ethnicity gap was significantly
reduced after 1 year and virtually eliminated after 2.
The current results thus highlight the potential importance of
detailed, written goal setting for reducing performance inequal-
ities in higher educational settings. They also appear to indicate
that modifying students’conceptions of their futures (and,
simultaneously, teaching them that such planned modifications
are possible) producing effects powerful enough to override
gender and structural socioeconomic impediments (Walton and
Cohen, 2011; Walton, 2014). The question of why poorer-
performing students hypothetically had less effective future
conceptions to begin with still remains. Future research
investigating other psychological and sociocultural processing,
theoretically underpinning underachievement, should begin to
address this more specific and potentially mediating factor.
Perhaps an impoverished past, for example, produces an
impoverished view of the future—vague, lacking in detail and
pessimistic (cf. Walpole, 2003). Such a view would do very little to
increase motivation or control counter-productive anxiety.
It appears that the intervention succeeded partly because it
specifically improved exam-taking behavior, most likely because
of enhanced self-regulation. Indeed, self-regulation does appear to
be enhanced when specific goals are set (Latham and Locke,
1991), particularly when people contrast a fantasized ideal future
with present reality (Oettingen et al., 2001), as was required
during the current intervention. Guided reflection on goals as it
was used in the current intervention may help elicit meta-
cognitive awareness, help reflect on detailed goal-setting and goal-
monitoring strategies, and as such enhance self-regulation
(Lyke, 2009; Stein and Grant, 2014). Indeed, reflection can be
helpful in making sense of prior experiences and may improve
future functioning (for a review see Schippers et al., 2013;
Ellis et al., 2014).
As a result, students might attain internal state awareness of
their preferred goals and future and may therefore be better able
to self-regulate and direct energy towards their goals (cf. Grant
et al., 2002). Cybernetic/behavioural models of motivation (Gray
and McNaughton, 2000) strongly indicate that positive emotion
and approach motivation occur primarily when cues indicate
progress towards a desired goal occur (with satiation, by contrast,
occurring when the goal is achieved). Furthermore, adopting clear
goals and belief structures helps to constrain the experience of
Table 2 |Comparison of differences in number of regular exams between the control cohorts (combined) and intervention cohorts
Control, N Intervention, N Control (mean and SD—no. of
regular exams)
Intervention (mean and SD—no. of
regular exams)
Majority males Majority females Ethnic minority
males
Ethnic minority
females
Majority males 998 329 10.49 (2.62) 10.52 (2.77) —t=−5.29*** t=3.85*** t=1.77
Majority females 378 118 11.16 (1.85) 10.34 (3.88) t=0.47 —t=6.50*** t=4.20***
Ethnic minority
males
222 76 9.56 (3.37) 10.12 (3.88) t=0.85 t=0.39 —t=−1.32
Ethnic minority
females
143 52 10.01 (3.05) 9.40 (4.33) t=1.80 t=1.34 t=0.96 —
***Po0.001; equal variances not assumed.
Notes: The t-values and significance levels of the control cohorts appear above the diagonal, t-values for the intervention cohort below the diagonal. The differences in the intervention cohort between males and females, and between the majority and minority groups have
become significantly smaller and are non-significant for all subgroups in the intervention cohort, suggesting that especially the group that performed worse pre-intervention (ethnic minority males) took significantly more exams after the goal-setting intervention was
introduced.
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uncertainty, and writing about goals can clarify and strengthen
self-regulation and cognitive integration (Hirsh et al., 2012, 2013).
If people experience positive affect in relationship to desired
goals, it stands to reason that articulating and delineating those
goals will sharpen the connection between specific on-task actions
in the achievement of present and long-term goals, and that
people will therefore experience a heightened sense of accom-
plishment when moving towards or achieving their goals.
Such cybernetic/behavioural models also suggest that uncer-
tainty about goals itself can have a powerful inhibiting effect on
current effort-intensive behavior, and can also impair overall
physical and psychological health, as Pennebaker and his
colleagues have demonstrated (for a review see Pennebaker
et al., 2003). Thus, clarifying goals, and carefully articulating the
relationship between action and outcome, should increase the
positive felt consequences of productive behavior in the present,
as well as decreasing uncertainty-related negative emotions such
as anxiety, disappointment and frustration. However, full
differentiation of these two potential motivating effects (enhance-
ment of positive and decrease of negative emotion) is not yet
possible, and comprehensive understanding of the effects of
expressive writing and goal setting, has not yet been attained (cf.
Pennebaker, 2004).
As well as documenting the utility of the programme,
particularly to underachieving students, it is worthwhile bringing
attention to its ease-of-use, inexpensive nature and minimal
disruption during implementation. It was made available online,
at low per-student cost. Its implementation required very little
additional contextual or explanatory information to be provided,
except for that offered in text form online during the process
itself. Its demands on teaching staff were therefore low, as was
intended during its design. Finally, this intervention is fully
scalable, and could be expanded in its present form to serve
thousands or tens of thousands of users.
We do strongly believe, however, that the probability of
students participating in the intervention and completing it was
increased by the fact that it was a required component of the first-
year curriculum itself, complete with deadlines. The goal-setting
intervention, although not particularly time-consuming, can be
sufficiently demanding to potentially dissuade students—particu-
larly those who are likely to perform poorly—from completing it
as a mere extracurricular activity.
Conclusion
Overall, the results demonstrate that an inexpensive, scalable,
written online goal-setting programme can be used effectively
and efficiently to increase educational quality and equality, by
promoting improvement in academic performance and retention
among students struggling in comparison to their peers,
particularly if those students are male and/or from a visible
ethnic minority.
Notes
1 We do not include results for the “Western minority groups”, commonly defined as
first and second generation individuals with a foreign but Western background
(country of birth is Europe, excluding the Netherlands and Turkey), North America,
Oceania, Japan or Indonesia (former Dutch East Indies; Alders, 2001). This group was
negligible in size (5–8% of the student body) and analyses revealed performance and
results midway between the Dutch majority and non-Western ethnic minority group.
Detailed analyses regarding this group can be obtained from the first author.
2 As in most Dutch bachelor programmes, first year students in the current study (up to
2010), had the opportunity to re-sit all 12 exams in the summer, if they missed any or
all of them during the regular school year. This practice had adverse effects, as students
commonly procrastinated when provided with such an opportunity. In consequence,
the business school implemented a re-sit limit of four for the 2011/2012 and following
cohorts, as suggested by several authors (Judge et al., 2001; Clark et al., 2014; Yperen
et al., 2014), in an attempt to decrease procrastination and increase study performance.
This may have limited our variance in regular exam participation, but should be seen
as rendering our analyses more conservative.
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Data Availability
The datasets generated during and/or analysed during the current study are not publicly
available due to the privacy of these data, but are available from the corresponding author
upon reasonable request.
Author Contributions
MS wrote the main manuscript text; AS performed and reported the analyses; AS and MS
prepared all figures; JP designed the intervention; all authors discussed the results,
commented on and extensively reviewed and edited the manuscript.
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Acknowledgements
The authors thank participating students, the Dean and programme management
of an anonymous university for their support in carrying out this research. They
also thank JH Bouwman and J van Zeeland for their assistance, and F Anseel
for his helpful and constructive feedback on an earlier version of this manuscript.
The authors would like to thank E Locke and D Morisano for discussions and
comments.
Additional Information
Supplementary Information: accompanies this paper at http://www.palgrave-journals
.com/palcomms
Competing interests: The authors have no competing interests as defined by Nature
Publishing Group, or other interests that might be perceived to influence the results
and/or discussion reported in this article. The authors received no financial support for
this research. A small fee per student was paid for the online intervention by the
participating business school to Dr JP. Dr MS and Dr AS declare no potential conflict
of interest.
Reprints and permission information is available at http://www.palgrave-journals.com/
pal/authors/rights_and_permissions.html
How to cite this article: Schippers M C, Scheepers W A and Peterson J B (2015)
A scalable goal-setting intervention closes both the gender and ethnic minority
achievement gap. Palgrave Communications. 1:15014 doi: 10.1057/palcomms.2015.14.
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