the impact of international students on measured learning and standards in australian higher...
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Economics of Education Review 31 (2012) 587– 600
Contents lists available at SciVerse ScienceDirect
Economics of Education Review
jou rn al h om epa ge: www . elsev ier .com/ locate /econedurev
he impact of international students on measured learning andtandards in Australian higher education
igi Foster ∗
NSW School of Economics, Australian School of Business, Sydney 2052, Australia
r t i c l e i n f o
rticle history:eceived 16 May 2011eceived in revised form 21 March 2012ccepted 22 March 2012
EL classification:2115
a b s t r a c t
International students, who are also often from non-English language speaking back-grounds (NESB students), are an important source of revenue for Australian universities.Yet little large-scale evidence exists about their performance once they arrive. Do these stu-dents perform worse than other students in Australian undergraduate classrooms? Whathappens to other students’ performance when these students are added to classrooms?I provide new empirical evidence on these questions using recent administrative paneldata from the business schools of two Australian Technology Network universities. Results
eywords:ducational economicsigher educationpilloversnternational students
show strong and highly statistically significant main effects and spillover effects, raisingconcerns about the integration of international NESB students into the Australian tertiaryenvironment.
© 2012 Elsevier Ltd. All rights reserved.
ustralia
. Introduction
Foreign students make up an increasing share ofustralian universities’ revenues, particularly in busi-ess schools.1 April 2010 data from Australian Education
nternational show an 11.9% year-to-date increase innrolments of international students in Australian higherducation programs (AEI, 2010). Enrolment growth in the
Management and Commerce’ area, which attracts signif-cant numbers of international students, was 12.6% overhis same period. Estimates by Access Economics for the007–2008 financial year put the contributions of inter-
ational students at approximately 1% of Australian GDPAE, 2009). Travel services related to education, as a soleategory, totalled approximately 27% of Australian service∗ Tel.: +61 2 9385 7472; fax: +61 2 9313 6337.E-mail address: [email protected]
1 Throughout the paper, I use the term ‘school’ to refer to a collection ofepartments in related disciplines, known more commonly in Australias a ‘faculty’.
272-7757/$ – see front matter © 2012 Elsevier Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.econedurev.2012.03.003
exports in 2007–2008 according to the Australian Bureauof Statistics data cited in that same report.
Because international students pay full fees to the insti-tutions in which they enrol, they may be seen as a revenueboon for both Australian universities and the broadereconomy. Yet international students face several poten-tial disadvantages in relation to local students in terms oftheir likelihood of academic success. Differences in socialand academic culture, academic aptitude or preparation,as well as inadequate language fluency, may all contributeto worse performance by foreign students (Bradley, 2000;Cheng & Leong, 1993; Lebcir & Wells, 2008; Stoynoff, 1997;Zhang & Brunton, 2007). Speaking English in the home,while uncommon amongst the international student pop-ulation in Australia, overlaps with domestic student statusand hence varies independently of whether a student isinternational or not. Unlike being foreign, English fluencyis a clear academic skill, and as such is used directly in
writing papers, reading texts and understanding lectures.It would be appealing to partial out the effect of being ofnon-English-speaking background per se from the effectson university performance of other factors related to being
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588 G. Foster / Economics of Eduan international student (e.g., cultural factors, baseline aca-demic preparation, or underlying aptitude).
Beyond the question of whether there are sheerperformance differentials between international, non-English-speaking-background (NESB), and other studentgroups, educational equity concerns would also lead usto ask how the infusion of international and NESB stu-dents into Australian higher education impacts upon themarks of other students. Recent research (Gould, Lavy, &Paserman, 2000; Jensen & Rasmussen, 2011) has shownthat concentrations of immigrant children in classroomsare robustly negatively related to natives’ ultimate educa-tional outcomes.2 These authors do not offer an explicitcausal explanation for their finding, but do note that theimmigrants in question were socio-economically stressed,implying that this stress was somehow at play in the pro-cess(es) that led to the effect they find. In the Australiancontext, overseas students are newcomers too, and likelyto be ‘stressed’ compared to native students in a varietyof ways: almost certainly linguistically and culturally, andperhaps also in terms of their academic background and/orinherent academic ability. While increased cultural diver-sity may aid learning, sharing a discussion group with peersof lesser ability, worse preparation, or foreign cultures, orwho are less able to express themselves in English, mayproduce a lower grade due to lower-quality or less effi-cient peer-to-peer and/or tutor-to-student interactions inclass.3 These effects would most logically appear in an envi-ronment where interactive activities are common, such asa section, or ‘tutorial’ in Australian terminology.
There may also be spillover effects unrelated to learn-ing. In particular, it is well-known both from anecdote andin the education literature that many university courses aregraded on a curve.4 A strict course-specific grading curveimmediately implies that sharing a course with studentswho perform at a lower level than oneself – in the absence
of any other effects – will produce a mechanical rise inone’s own grade. This rise does not reflect better learn-ing outcomes, but is only a mechanical reflection of the2 The specific native/immigrant demographic dichotomy appears to beimportant to the generalizability of these findings. Research using black-white concentrations in U.S. schools (Guryan, 2004), for example, foundno effect of black student concentrations on white drop-out rates.
3 Many results in the literature on peer effects in higher educationclassrooms would imply this type of effect (see, e.g., Arcidiacono, Foster,Goodpaster, & Kinsler, in press; Foster & Frijters, 2010; Winston &Zimmerman, 2004). Naturally, teacher effects based on immigrant sta-tus may occur as well, and have been found empirically: using data from alarge public American university, Borjas (2000) finds that foreign-bornteaching assistants were associated with worse outcomes for domes-tic students, compared to native TAs. Jacobs and Friedman (1988) findsno overall effect, but some indication of the relevance of tutors’ Englishlanguage skills to students’ performance, when investigating the samequestion using different U.S. data. Finally, such effects may flow throughdifferential expectations held by teachers for different types of students,as contended by van Ewijk (2011). Teacher information is not available inthe data set used in this paper.
4 This was not always the case. See Small (1973), who also suggeststhat the popularity of ‘criterion-based’ assessment (e.g., graduating stu-dents who are alleged to possess certain ‘graduate qualities’ or ‘attributes’)reflects a desire to return from grading on the curve towards a set of moreabsolute assessment standards, as was more commonly used in the lastcentury and in antiquity.
eview 31 (2012) 587– 600
mix of co-students combined with lecturers’ needs to keepgrade distributions across years and across courses roughlyequivalent. Mechanical ‘spillovers’ of this sort would ariseeven from a more mild form of relative grading, such aswhen instructors hesitate to fail more than some thresh-old percentage of students enrolled in a course, perhaps inorder not to earn a reputation for being overly strict, or inorder not to be noticed and possibly called to account bythe university administration.
Hence, if international and/or NESB students performworse than other students and/or have negative spilloversin the tutorial classroom, and if additionally course gradesare set in a manner that involves any measure of rela-tive rather than absolute judgment, then the net impacton grades of adding these students to a given course istheoretically ambiguous.
Results show that both international students and NESBstudents perform significantly worse than other students,even controlling for selection into courses. Both effectsare large and persistent. Adding international or domesticNESB students to a tutorial classroom leads to a reductionin most students’ marks, and there is a particularly strongnegative association between international NESB studentconcentrations in tutorial classrooms and the marks ofstudents from English-speaking backgrounds. Finally, con-ditional on student covariates, tutorial composition effects,course fixed effects, and many other controls, the impact onmarks of a high percentage of NESB students in a course ispositive. I argue that this finding may reflect supply-sideinfluences such as downward adjustments to the difficultyof material or grading standards. Taken as a whole, the evi-dence in this paper strongly indicates the need for betterintegration of students from non-English speaking back-grounds – particularly international NESB students.
1.1. International students in Australia
Australia’s geographical position makes it an attrac-tive higher-education option for Asian students wishingto obtain English-language degrees. However, none ofAustralia’s universities appear in the top twenty or soinstitutions worldwide, according to the most popularuniversity-ranking schemes (e.g., the Academic Ranking ofWorld Universities, the QS World University Rankings, andthe Times Higher Education World University Rankings).This means that the ‘best and brightest’ Asian studentsheading overseas for their undergraduate degrees are, onaverage, likely to matriculate at an institution in the U.S. orEurope rather than in Australia. As home-grown Asian uni-versities rise in stature, Asian students in the ‘second tier’of aptitude and/or general preparation for tertiary studyface the choice of whether to study domestically, or insteadto go abroad and enrol in a good but not elite institu-tion. Australia’s geographic proximity to Asia makes it anattractive target for students from that second tier down-ward. The approximately 41 universities in Australia rangegreatly in quality, and those whose data are analyzed in the
present study are part of the Australian Technology Net-work and are placed near the middle of the pack, accordingto rankings posted by the Australian Education Network.There is no clear a priori reason to expect that international
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speaks English in the home) as well as detailed informationabout which courses and tutorials each student took in eachcovered semester, and what final percentage marks wereachieved in each.6 Throughout all analysis presented in this
5 These are fixed-term and/or non-tenure-track academics, roughlyequivalent to the American ‘adjunct’ or ‘part-time’ faculty member.
6 The term ‘mark’ is used in Australia to denote the percentage out of100 that a student earns in a course. The term ‘grade’ is used to refer to
G. Foster / Economics of Edu
tudents studying in these institutions (the majority ofhom are Asian) will be innately better or worse-prepared
or university study than domestic Australian students.There has been very little large-scale quantitative anal-
sis of the academic performance of international studentsn the Australian context. In the largest study to date usingustralian higher education data, Olsen and Burgess (2006)nd no performance differentials between internationalnd local students. However, that study examined onlyass rates rather than the full distribution of marks, andas based on aggregated data where other factors, such
s additional demographics, course type, and learning con-ext, were not controlled.
The university performance of international studentselative to domestic students should logically relate tohree classes of phenomena, once English language back-round is controlled: (1) their basic academic aptitude,elative to that of domestic students; (2) the quality ofheir preparation for university in their home country, rel-tive to that provided domestically; and (3) the amount ofdditional effort required for an international student tovercome cultural and other obstacles that the domestictudent need not face in order to perform at an equivalentevel. The present paper does not attempt to disentanglehese three potential causes, but rather asks about asso-iations overall between international student status andarks in the case of Australia, while looking independently
t English language background effects. Any remainingssociation found between international student status andarks or classroom spillovers could be due to any or all of
he above causes, including the quality of prior preparationn English expression.
.2. English language skills
Insufficient language fluency has been proposed byany authors as the major barrier confronting inter-
ational students. Lebcir and Wells (2008) suggest thatnglish skill is one of the important drivers of interna-ional students’ academic performance. Using data from a.S. university, Lee and Anderson (2007) find that there is aositive correlation between general language proficiency,hich is measured by the TOEFL score (a standardized test
f English fluency similar to the IELTS, used in Australianndergraduate admissions decisions regarding foreign stu-ents), and students’ writing performance, an important
nput into success for much undergraduate assessment. Asnglish is used as the teaching language in the Australianndergraduate classroom, it is reasonable to expect thebility to speak and understand English to be positivelyorrelated with a student’s performance. Based on inter-iews with students at the University of Adelaide, Plewand Sherman (2007) find that both local and internationaltudents with good language skills blame students with rel-tively poor language skills for lack of creativity and slowrogress in their groups.
However, there is not complete agreement on this point.
ome researchers (Johnson, 1988; Light & Xu, 1987; Picard,007) argue that there is no significant correlation inndergraduate student samples between English languageroficiency as measured on international tests like theeview 31 (2012) 587– 600 589
TOEFL or IELTS and either performance or intelligence, par-ticularly when the threshold language requirement (e.g.,TOEFL score) is high. While the results of such tests offera convenient measure for educational institutions wish-ing to assess the language fluency of overseas candidates,standardized test results may not be a reliable indicatorof a student’s true English skills. The simple indicator thatis used in the present paper and is not mechanically tiedto intelligence is whether a student speaks English in thehome. Importantly, this variable (like a TOEFL score) willnot pick up differences between students that relate totheir level of practice or familiarity with the everyday useof English.
2. Data and methodology
This paper exploits a new panel data set on Australianstudents enrolled in undergraduate programs within thebusiness schools of two universities in the Australian Tech-nology Network. Data are available at the student-tutoriallevel for the universe of students enrolled and takingcourses in these programs at any point during the autumnand spring semesters of 2008, 2009, or 2010.
As noted briefly above, a ‘tutorial’ in Australia is equiv-alent to a ‘section’ in North America. Frequently taught bypeople termed ‘casual staff’5 or graduate students, the tuto-rial is an opportunity for students to discuss and work oncourse material and receive more personalized assistancethan is possible in lectures. Tutorials occur weekly, as dolectures, for a particular undergraduate course; a typicalcourse includes 3 h of total face time (e.g., 2 h of lectures and1 h of tutorials) each week for the duration of the semester,which is between 13 and 15 weeks. The final mark thatserves as the dependent variable in this paper is awarded atthe course level, but any given student was enrolled in boththe course itself and a particular tutorial. The multi-leveltertiary learning structure combined with the panel natureof the data make it possible to analyze simultaneouslythe effects on final course mark of both course-specificinformation and tutorial-specific information, in additionto student-specific controls like demographics.
To create the data set, information from the enrol-ment systems of each institution was merged with datafrom students’ applications to university, resulting in afinal data set that includes detailed demographics (such asage, gender, and other observable characteristics, includ-ing international student status and whether the student
the letter grade associated with that percentage. In Australia, the lettergrades most commonly used and the range of marks to which they applyare Fail (<50), Pass (50–64), Credit (65–74), Distinction (75–84), and HighDistinction (>84). ‘Marks’ (percentage scores) are used in all analysis inthis paper, and ‘grades’ (with the Australian meaning) are not used.
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prehensiveness with which the set of controls includedcapture the unobserved selection mechanisms at play.9
9 Although this would not permit the estimation of the association withmarks of being international or NESB oneself, another approach to puri-fying the composition effect estimates of selection bias in this context
590 G. Foster / Economics of Edu
paper, the outcome modeled is this final course mark, mea-sured out of 100.7
2.1. Methodology
From the data described above, all student-tutorialobservations are kept for which the student was notthe sole student enrolled; for which the course enroll-ment exceeded the enrollment in the tutorial (meaningthat courses with only one section were dropped); andfor which all core observable variables were available.A dummy indicating that university matriculation tookplace directly following graduation from an Australian highschool in the state of residence – as opposed to followingattendance at another high school, or as a consequence oftransferring from another institution or entering univer-sity at an older age – is available for a subset of students.This dummy along with a dummy indicating the absence ofthis information are both included as control variables inthe analysis. Other control variables include course-leveleffects (either varying across semesters, or fixed acrosssemesters, depending on the model); a dummy array forthe location of the tutorial;8 a dummy array for the exactday and time that the tutorial began each week; coursesize and tutorial size; a dummy array for the program ofstudy pursued by the student; and, as a general student-ability control in many models, the average GPA earnedby the student in courses that semester not including thecourse in which performance is being predicted. Furthercontrols that are constructed and included in the analysisare institution of study, semester-by-year effects, courseload, discipline group (for regressions excluding course-specific fixed effects), gender, age, and a dummy indicatingthat the student was new to university that semester.
As the primary regressors of interest in the analysis ofspillovers, four variables are constructed at the student-by-tutorial level: the percentages of international students andNESB students within the course, and the same percent-ages within the tutorial classroom, all of which exclude thestudent himself. As noted above, the dependent variablethroughout all analysis in this paper is final course mark,measured out of 100.
I first examine the performance differentials (bothuncorrected and regression-corrected for other observablecharacteristics) of international, NESB, and other studentsacross all observed courses. Then, using the computed per-centages of international and NESB students in each course
and each tutorial, I isolate the association of higher con-centrations of both types of students on the marks of otherstudents sharing their tutorial and their course.7 The final course mark is typically a composite of marks on ‘continuousassessment’ (e.g., papers, midterms, and quizzes) and the mark on the finalexamination. Although course-specific information about assessment mixis not available in the data used here, the standard convention in mostundergraduate courses run by Australian business schools is to hold afinal examination which is worth between 40 and 70% of the course mark.
8 These variables indicate the actual physical room in which the tutorialwas held – e.g., Building 5, Room 301. This array of controls is intendedto pick up effects on marks due to shared phenomena at the tutorial levelsuch as poor lighting, stuffiness, or drafts, that may influence final coursemarks.
eview 31 (2012) 587– 600
The possibility of selection into courses and/or tutorialsis an important concern. The mechanical fact that obser-vations in the data set disproportionately represent largecourses means that most of the data used in the estima-tion comes from core courses (for example, IntroductoryAccounting), which students by and large cannot avoidtaking at some point during their undergraduate careers.Different programs of study do require slightly differentcore courses, but each student’s program of study can beobserved and controlled using dummy variables. Course-specific fixed effects (e.g., a fixed effect for ‘IntroductoryAccounting’) can also be included in models featuringcourse composition variables on the right-hand side, dueto the variation across semesters in the composition of anygiven course.
Selection into tutorials is more problematic. There aresignificant constraints placed on most students’ schedules,and generally no information is available in advance aboutwhich tutorials will be taught by which teachers. However,students are by and large able to select their tutorials sub-ject to the constraints they face, with early enrollees likelyto have a wider array of choices than later enrollees. Thismay result in later enrollees, who may also be less con-scientious students, being clustered into certain ‘left over’tutorials. If it is furthermore the case that international orNESB students are also likely to be late enrollees, such thatthe percentage of such students within any given tutorialis negatively related to the average unobserved conscien-tiousness of its students, then we would expect downwardpressure on the estimate of the impact of tutorial-level con-centrations of international or NESB students on marks. Toaddress these concerns, the physical location of the tuto-rial, the day of the week it was held, and its exact starttime are all controlled using dummy variable arrays inthe models. However, tutorial-level fixed effects cannotbe controlled because doing so prevents the simultaneousinclusion of tutorial-specific composition variables, whichmultiply the estimation targets. Any causal interpretationof the results found in regard to tutorial composition effectsshould therefore be viewed as conditional upon the com-
would be to control for student-specific fixed effects, thereby estimatingthe effects of classroom composition only within student. While this dras-tically reduces the underlying variation in the explanatory variables usedto estimate the composition effects at both the course level and the tutoriallevel, it is conceptually appealing due to the much lower possibility of anysignificant selection issue remaining to contaminate the estimates. Fol-lowing the suggestion of a referee, I attempted to do this. With more than120,000 observations and close to 18,000 fixed effects (including student,course, and tutorial location, day, and time effects), however, the modelwas inestimable on the full data set using conventional software. Esti-mation was only possible by running the model separately for differentsub-samples of the data (defined by time-frames, genders, and demo-graphics). In these specifications, the positive linear course-compositioneffects shown in the main results below were in evidence for severalsub-samples, though they were not always significant and for some sub-samples they were of the opposite sign; and the tutorial-compositioneffects were highly variable in terms of both sign and significance. I take
G. Foster / Economics of Education Review 31 (2012) 587– 600 591
Table 1Student-tutorial sample: summary statistics.
Mean Std dev Min Max
Panel AMark 61.03 (14.80) 1 100International (yes = 1) .30 – 0 1NESB (yes = 1) .47 – 0 1NESB international (yes = 1) .29 – 0 1Percent international in tutorial .30 (.25) 0 1Percent NESB in tutorial .47 (.25) 0 1Percent NESB intl in tutorial .29 (.25) 0 1Percent international in course .30 (.19) 0 1Percent NESB in course .47 (.19) 0 1Percent NESB intl in course .29 (.19) 0 1Number of students in tutorial 25.95 (10.02) 2 102Number of students in course 346.15 (237.25) 3 1017
Mark % intl in tutorial % NESB in tutorial % intl in course % NESB in course
Panel BMark 1.00 – – – –Percent international in tutorial −.09*** 1.00 – – –Percent NESB in tutorial −.07*** .86*** 1.00 – –Percent NESB intl in tutorial −.09*** .99*** .87*** – –Percent international in course −.08*** .77*** .68*** 1.00 –Percent NESB in course −.07*** .68*** .76*** .89*** 1.00Percent NESB intl in course −.08*** .76*** .69*** 1.00*** .91***
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Moreover, these effects are not due to selection intocourses, nor do they disappear after a student’s firstsemester. Columns 2 and 3 of Table 3 include interactions
below 50 (a bare pass) and just above 50 for both international and NESBstudents than for other students, while marks of the latter groups cluster
tatistics are calculated across the 122,694 student-tutorial level observatnon-English language speaking background’; ‘intl’ stands for ‘internation*** p < 0.001.
.2. Data description
Table 1 shows simple summary statistics and correla-ions at the student-tutorial level for the main analysisample of 122,694 observations, representing 15,249 stu-ents. Panel A shows that the average final course mark inhe sample is just over 61%, and the percentages of inter-ational and NESB students experienced by students inlassrooms average 30% and 47% respectively. A tutorialonsists of 25 students, on average; the average enrolmentn a course is almost 350 students.
Panel B shows strong negative raw correlations betweenark and both international student status and NESB status
t the student-tutorial level. There are also strong negativeorrelations at this level between mark and all variablesapturing the percentages of international and NESB stu-ents in classrooms. Finally, it is clear from the patterns oforrelations in this panel that whereas virtually all inter-ational students in the sample are also NESB, there is aeasonable percentage of NESB students who are not inter-ational. This percentage is 35% in the sample.
. Results: main effects
In the data used for this paper, international and NESBtudents generally fare worse in terms of raw marks.10
rom this exercise that insufficient within-student variation remains in theub-samples, after absorbing this large number of fixed effects, to estimatenterpretable composition effects.10 Fig. 3 in Appendix A shows simple histograms of marks, at thetudent-by-tutorial level, for international versus non-international stu-ents. Fig. 4 then shows analogous histograms for NESB versus non-NESBtudents. Both comparisons illustrate that the density is thicker both
the sample used to produce the final column of Table 4. ‘NESB’ stands for
Table 2 shows that raw marks vary significantly by inter-national and NESB status at the student level, with bothinternational and NESB students performing worse onaverage.
The patterns shown in Table 2 persist when otheraspects of the student and his learning environment arecontrolled. The first column of Table 3 shows robust evi-dence of lower marks for international and NESB students –by 2.5–3.5 points each on the 1-to-100 marking scale – evencontrolling for institution, semester, student demograph-ics (age and gender), new student status, university entrantstatus (same-state high school leaver or other entrant),course load, program of study,11 course and tutorial size,tutorial location and day/time, and discipline group of thecourse.
more heavily in the upper ranges.11 It is important to note that the Australian higher education system
by and large does not follow the liberal arts tradition. Australian univer-sity students come to university having already identified and enrolledin a particular program leading to a particular degree, such as Bachelorof Business (Marketing) or Bachelor of Accounting, and must enrol in afar narrower suite of courses, proscribed by their program, than a typicalAmerican undergraduate. Most Australian undergraduate degrees requirethree years of full-time study to complete, and require the student to enrolin very few courses managed outside the school (i.e., the collection ofdepartments, such as Economics, Marketing, and Management) runningthe program. This is why the sample used in this paper represents a fairlycomplete picture of the courses taken by students enrolled in undergrad-uate programs run by the business schools of the two institutions.
592 G. Foster / Economics of Education Review 31 (2012) 587– 600
Table 2Student-level averages.
Panel A International Domestic
Mean Std dev N Mean Std dev NMark 57.70 (11.32) 4872 61.55 (12.35) 10,377Age 22.45 (2.52) 4872 22.05 (4.97) 10,377Gender (female = 1) .52 – 4872 .54 – 10,377New student (yes = 1) .31 – 4872 .24 – 10,377Number of tutorials in sample 7.75 (4.25) 4872 8.30 (4.62) 10,377
Panel B NESB ESB
Mean Std dev N Mean Std dev NAverage mark 58.42 (11.51) 7291 62.07 (12.48) 7958Age 22.42 (3.61) 7291 21.95 (4.91) 7958Gender (female = 1) .54 – 7291 .53 – 7958New student (yes = 1) .27 – 7291 .25 – 7958Number of tutorials in sample 8.07 (4.40) 7291 8.17 (4.62) 7958
Panel C NESB international ESB domestic, NESB domestic, and ESBinternational
Mean Std dev N Mean Std dev NMark 57.75 (11.22) 4740 61.49 (12.39) 10,509Age 22.44 (2.50) 4740 22.05 (4.95) 10,509Gender (female = 1) .52 – 4740 .54 – 10,509New student (yes = 1) .29 – 4740 .25 – 10,509Number of tutorials in sample 7.79 (4.24) 4740 8.27 (4.63) 10,509
Statistics are calculated across the 15,249 student-level observations used to produce the final column of Table 4. ‘NESB’ stands for ‘non-English languagespeaking background’; ‘ESB’ stands for ‘English language speaking background’.
Table 3Baseline marks equations.
(1) (2) (3) (4) (5)
International −2.779*** −2.291*** −2.355*** −2.561***
(0.27) (0.29) (0.29) (0.28)International* −2.128*** −0.553new student (0.40) (0.41)NESB −3.315*** −3.484*** −3.368*** −3.387***
(0.24) (0.25) (0.25) (0.24)NESB* 0.722* 0.364new student (0.36) (0.35)NESB* −5.449***
international (0.24)NESB* −3.395***
domestic (0.24)Female 1.913*** 1.916*** 1.953*** 1.963*** 1.921***
(0.18) (0.18) (0.17) (0.17) (0.18)Age 0.073** 0.074** 0.079** 0.078** 0.056*
(0.02) (0.02) (0.02) (0.02) (0.03)New student −0.084 0.393 −0.641* −0.768*** −0.439*
(0.16) (0.22) (0.26) (0.19) (0.18)
Course-by-semester fixed effects? No No Yes Yes YesAdj. R-sq 0.096 0.097 0.162 0.161 0.366Obs 125,214 125,214 125,214 125,214 122,694
The dependent variable is final course mark. Institution, semester by year, program of study, high school leaver status, course load, and tutorial size, location,and starting time and day effects are controlled in all regressions. Discipline group is also controlled in columns 1 and 2, using nine categories (Accounting,Banking, Business, Economics, Law, Marketing, Mathematics, Management, and Other), as is course size. Columns 3–5 control for course-by-semester fixedeffects. Column 5 also adds a control for the GPA of the student calculated across all other courses in the current semester. ‘NESB’ stands for ‘non-Englishlanguage speaking background’. Standard errors are clustered at the student level. Full results appear in Appendix A.
* p < 0.05.** p < 0.01.
*** p < 0.001.
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students, and domestic). All regressions include coursefixed effects, as well as extensive arrays of controls at thetutorial and student levels. Table 4 presents these results.
Table 4Adding composition effects on marks.
(1) (2) (3) (4)
International −2.390*** −2.467***
(0.28) (0.28)NESB −3.359*** −3.346***
(0.24) (0.24)% intl −3.198***
in tutorial (0.28)% intl 5.227***
in course (0.84)% NESB −3.010***
in tutorial (0.29)% NESB 5.425***
in course (0.87)NESB* −5.456*** −5.456***
international (0.24) (0.24)NESB* −3.436*** −3.430***
domestic (0.25) (0.25)% NESB intl −3.674*** −3.404***
in tutorial (0.31) (0.75)(% NESB intl −0.220in tutorial)2 (0.81)% NESB intl 5.883*** −0.936in course (0.90) (2.24)(% NESB intl 8.261**
G. Foster / Economics of Edu
f international and NESB student status with whether thetudent is new to university. Working from the results inolumn 3, where student selection into particular coursess controlled using course-by-semester fixed effects, annternational student in his first semester at university isstimated to perform about 2.9 marks lower than a sea-oned non-NESB domestic student, and even after the firstemester, about 80% of this differential remains. For NESBtudents, the story is even worse: in the first semester theverage gap in marks between NESB and seasoned non-ESB domestic students is about 3 points, and the gapidens to almost 3.4 points after the first semester.
In column 4 of Table 3, the same basic pattern isonfirmed using indicators constructed differently: onendicator for NESB international students, and one forESB domestic students, with the left-out category beingnglish-speaking students of all types. As expected, NESBnternational students perform even worse compared tonglish-speaking students than NESB domestic students,nd both groups of NESB students experience significantlyower marks than students who speak English in theome.12
Finally, in column 5 of Table 3, in addition to course-y-semester fixed effects, one final control variable isdded to the specification in column 1: the average finalark obtained by the student in all other courses in that
emester. This reduces the sample size slightly since alltudents enrolled in only one course in a given semesterre excluded, but the result is the same: international andESB students both perform significantly worse than other
tudents. The similar pattern of results in this column com-ared to column 1 is also suggestive of differences in thekills and abilities required to succeed in different coursesbserved in the sample. If the attributes required for suc-ess were identical across courses, then no fixed studentharacteristics should remain significant in this equationnce the balance of GPA that semester is controlled.13
. Results: composition effects
This section focusses on variables that capture theercentages of NESB and international students in the
utorial and course in which the student earning a givenark was enrolled.14 Student-by-course mark equationsre re-estimated including these additional covariates con-tructed at the level of the tutorial and course in which the
12 These significant downward performance effects persist if we insteadredict simple passage of courses, using a logistic MLE model.13 One other possibility is that marks are largely a function of course-pecific luck, but GPA in other courses is highly significant in the equationsee full results in Appendix A), making this seem unlikely. Student-by-emester ‘luck’ effects are certainly possible, however.14 See Appendix A for graphical distributions of these variables inigs. 5 and 6. It appears in the data that international student concen-rations in tutorials are more uneven (with zero international students in5% of tutorials) than NESB student concentrations because of program-f-study and course selection effects. Specifically, international studentsre far less likely to study law, and more likely to study accounting, bank-ng, business, and marketing, than other students. This may be the resultf visa regulations, university marketing messages to potential overseastudents, or other factors.
eview 31 (2012) 587– 600 593
final mark was observed. Tutorial concentration effects areincluded to capture any learning spillovers that may occurwithin classrooms, whereas course concentration variablesare intended to capture any course-wide spillovers frominternational and/or NESB student concentrations onto allstudents’ marks (for example, through relative marking).
Because of the significant overlap between internationaland NESB student status, it is impossible to estimate inde-pendent effects of both sets of concentration variables inthe same regression. To address this problem, additionalspecifications are then run with concentration variables atboth the tutorial and course levels constructed for NESBdomestic and NESB international students, with the left-out category being English-speaking students of all types(both international, of whom there are very few non-NESB
in course)2 (2.53)% NESB domestic −0.952* 3.643***
in tutorial (0.44) (0.87)(% NESB domestic −8.349***
in tutorial)2 (1.34)% NESB domestic 4.406** −3.928in course (1.54) (4.12)(% NESB domestic 20.231*
in course)2 (9.91)
Adj. R-sq 0.359 0.358 0.358 0.358Obs 122,694 122,694 122,694 122,694
The dependent variable is final course mark. Institution, semester by year,program of study, GPA of the student calculated across all other courses inthe current semester, course load, course fixed effects, and tutorial size,location, and starting time and day effects are controlled in all regres-sions. ‘NESB’ stands for ‘non-English language speaking background’; ‘intl’stands for ‘international’. Standard errors are clustered at the studentlevel. Full results appear in Appendix A.
* p < 0.05.** p < 0.01.
*** p < 0.001.
cation Review 31 (2012) 587– 600
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594 G. Foster / Economics of Edu
Column 1 of Table 4 shows the impact of concentrationsof international students in the tutorial and the course. Theresults in column 1 suggest that being in a tutorial with alarger percentage of international students is significantlyworse for one’s mark. Going from a tutorial with the aver-age percentage of international students (30%) to one with10 percentage points more international students is esti-mated to yield about a .32 point reduction in final coursemark. This implies that in-class learning may be diminishedwith the addition of international students to a tutorialclassroom.
The percentage of international students in the course asa whole, by contrast, is estimated to matter positively formarks. Column 1 of Table 4 shows that holding constantown international and NESB student status, the percent-age of international students in the tutorial, and all othercontrols, the marks of students in courses where the per-centage of international students is 10 percentage pointshigher than average are estimated to be .52 points higher onaverage than those of students in courses with the averagepercentage of international students. If we do not con-trol for course fixed effects, this buoying effect is not asstrong, implying that student selection into certain coursesis important in this context.15 This is preliminary evidencein support of a relative marking effect or other supply sideinfluence on student performance.
An identical analysis is undertaken next on the impacton final course marks of NESB student concentrations,and results are shown in column 2 of Table 4. Resultsindicate similarly-sized negative effects on marks fromhigher concentrations of NESB students in tutorials, aswell as similarly-sized positive effects on marks from highconcentrations of NESB students in courses. Naturally how-ever, since a large percentage of NESB students are alsointernational, either the English language aspect or theinternational student aspect may be driving these effects.This pattern again appears to support a supply-side effecton marks.
To further investigate these patterns, an analogous setof regressions is run where concentration variables fortwo sets of students – international NESB students, anddomestic NESB students – are included simultaneously.Results are reported in columns 3 and 4 of Table 4. Thenegative association of own NESB status with marks,regardless of whether one is international or domestic, isonce again clearly apparent in column 3. Furthermore, thenegative effect of international and NESB students within atutorial remains, and is estimated to be driven by studentswho are both international and from non-English languagespeaking backgrounds. High domestic NESB student per-centages within tutorials still negatively impact studentmarks, but international NESB students yield a particularlystrong negative spillover within the tutorial classroom. In
regard to course-wide concentration effects, the results incolumn 3 of Table 4 indicate that both international and15 Specifically, this pattern indicates that international students arepresent in higher concentrations in courses where worse marks areobserved.
Mark by percent international (course level)
Fig. 1. Average marks in courses, by percent international students.
NESB student percentages within the course appear toproduce an upward buoying effect on marks.
Finally, to examine whether there are significant nonlin-earities in these effects, column 4 of Table 4 shows resultsfrom a model in which squared concentration variablesare included in addition to allowing main linear effects.16
Results show that the negative effect of the percentage ofNESB international students within the tutorial is fairly wellcaptured by a linear model: the squared term is insignifi-cant. By contrast, the positive effect on marks associatedwith a higher percentage of international NESB studentsin the course as a whole is indeed nonlinear: the squaredterm is positive and highly significant, whereas the maineffect is not. This pattern suggests that the effect of course-level percentages will be lower when only a small fractionof the course’s enrolment is made up of international NESBstudents than when the course already has a large frac-tion of such students, whereas adding such students totutorials has the same negative effects on marks no mat-ter if it is the first or the last such student to be added.This makes intuitive sense under the interpretation thateffects stemming from tutorial percentages reflect endoge-nous learning dynamics resulting from piecemeal changesin the tutorial classroom environment, whereas effectsstemming from course-wide concentrations are reflectingwholesale changes to material, lecture delivery, or markingthat become more and more necessary to implement withthe presence of more and more students who warrant theirimplementation.
If international and NESB students perform worse on anindividual basis than other students, then in the absenceof grading to a curve one would logically expect thatcourses in which there are large proportions of such stu-dents should post lower average marks. Figs. 1 and 2 showaverage marks in courses plotted against the percentage ofeach type of student in the course. The size of each bub-ble is proportional to the total enrolment in the course.
These figures show little evidence of a downward adjust-ment in average marks as the percentage of internationalor NESB students in a course rises above about 20%. One16 I thank an anonymous referee for suggesting this specification.
G. Foster / Economics of Education R
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psoacstmmei
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TA
I4esboi‘
Fig. 2. Average marks in courses, by percent NESB students.
ossible explanation for this, consistent with the regres-ion results reported above, is that the downward pressuren the grade distribution that results when internationalnd/or NESB students are added to classrooms is partlyompensated for by downward adjustments to gradingtandards or delivered material, buoying up the marks dis-ribution as a whole. The desire to keep certain aspects of
arks distributions looking similar across course offerings
ay provide a free ride to students – potentially, both for-ign and domestic – in courses with high percentages ofnternational and/or NESB students.
able 5verage marks equations for student subgroups.
(1) (2) (3)
Dep. var: Average marks of ESB students Average ma
% NESB intl −5.563*** −5.489*** −1.015***
in tutorial (0.14) (0.33) (0.19)
(% NESB intl −0.146
in tutorial)2 (0.42)
% NESB intl 3.826*** −1.509 8.375***
in course (0.41) (0.95) (0.54)
(% NESB intl 7.307***
in course)2 (1.16)
% NESB domestic −1.934*** 0.737 −0.412
in tutorial (0.20) (0.40) (0.32)
(% NESB domestic −5.101***
in tutorial)2 (0.67)
% NESB domestic 5.931*** 1.858 1.288
in course (0.71) (1.82) (1.15)
(% NESB domestic 10.321*
in course)2 (4.68)
Adj. R-sq 0.673 0.674 0.657
Obs 66,580 66,580 36,608
n all cases, the dependent variable is the average (within tutorial) final course ma, and 6 are respectively equivalent to those reported in columns 1, 3, and 5, exceffects are controlled in all regressions. Also controlled are effects for institutiontart time, and tutorial-wide averages of the following items across all students oeing predicted: gender; age; new student status; course load; program of studyf international students; indicators for high school leaver status and the absencen other concurrent courses. ‘NESB’ stands for ‘non-English language speaking baintl’ stands for ‘international’.
* p < 0.05.*** p < 0.001.
eview 31 (2012) 587– 600 595
Yet does this buoying effect apply equally to all typesof students? To examine this question, Table 5 presentsestimation results from models of average marks withina tutorial amongst different groups of students, regressedagainst the percentages of international NESB and domes-tic NESB students in the tutorial and the course of which itwas part. Columns 1 and 2 use average marks of students ofEnglish-speaking backgrounds within the given tutorial asthe dependent variable; columns 3 and 4 use average marksof NESB international students; and columns 5 and 6 useaverage marks of NESB domestic students. Aggregated ver-sions of all variables used in prior regressions are included,and columns 2, 4, and 6 additionally include squared con-centration variables in order to capture nonlinearities inthe effects of interest. All regressions include course fixedeffects, course and tutorial size variables, and tutorial day,time and location variable arrays, and are weighted by sizeof tutorial.
The previous negative spillover effect of internationalNESB students at the tutorial level is still in evidence, butthis effect is strongest by far on the marks of other typesof students: those from English-speaking backgroundsand those who are domestic NESB students. Further-more, underscoring previous results, high concentrationsof domestic NESB students in the tutorial are found to beassociated with lower marks, but this effect is far smallerthan the effect of international NESB student concentra-
all three student types, international NESB students appearto feel the smallest degree of negative spillovers from high
(4) (5) (6)rks of NESB intl students Average marks of NESB domestic students
−0.250 −5.310*** −3.313***
(0.54) (0.33) (0.79)−0.633 −2.370*
(0.53) (0.94)8.563*** −0.670 4.067
(1.55) (1.08) (2.67)−0.212 −6.033(1.59) (3.10)5.162*** −0.797 3.923***
(0.63) (0.45) (1.03)−10.699*** −7.360***
(1.00) (1.42)−12.417*** 4.171* −2.937
(3.08) (1.92) (6.50)35.010*** 13.488(7.76) (15.06)
0.659 0.575 0.57636,608 21,913 21,913
rk for the indicated group of students. Regressions reported in columns 2,pt that the former include squared concentration variables. Course fixed, semester-by-year, course size, tutorial size, location, day of week and
f the given demographic type whose average mark within that tutorial is indicators for the seven programs of study with the largest percentages
of high school information as used in prior regressions; and average GPAckground’; ‘ESB’ stands for ‘English language speaking background’; and
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596 G. Foster / Economics of Educoncentrations of NESB students in tutorials. When addedto tutorials, they also deliver the largest negative spilloversonto other students.
Course-wide concentration effects display an interest-ing pattern. The percentage of international NESB studentsin a course is estimated to have a very large positive impacton the tutorial-level average marks of international NESBstudents themselves, and a smaller but still significant pos-itive impact on the marks of ESB students. The course-widepercentage of international NESB students does not mattersignificantly for the marks of domestic NESB students. Thisimplies that the upward buoying effect, presumably dueto downward adjustments in marking standards, materialselection, or delivery when more international NESB stu-dents are added to courses only affects the average marksof the NESB international students themselves (primarily)and of English-speaking students (to a lesser extent). A sim-ilar pattern of influence is found for the concentrations ofdomestic NESB students in the course: a positive effect forstudents of the same type (i.e., domestic NESB) and for ESBstudents, with international NESB students far less affectedby concentrations of domestic NESB students in thecourse.
Turning to the even-numbered columns of Table 5, thenonlinear pattern found in previous results is also in evi-dence here for domestic ESB students. Such students feela similar negative effect from each international NESB stu-dent added to a tutorial, whereas the upward buoying effectof the course-wide percentage of international NESB stu-dents becomes stronger the more of such students areadded to a course. By contrast, international NESB studentsthemselves feel a fairly linear upward buoying effect astheir percentage in a course increases.
Overall, these patterns point to an upward buoyinginfluence on most students’ marks when a given course hasa high enrolment of students from non-English speakingbackgrounds. Logic suggests that this is not due to learning– after all, these effects are found from variation in stu-dent percentages at the course level, not at the tutoriallevel, and NESB students of all types exhibit lower base-line marks than other students. Instead, two explanationsseem most congruent with the facts. First, it may be thatlecturers adjust the presentation style or the difficulty ofmaterial endogenously when faced with large concentra-tions of such students in a given course, making it easierto obtain a higher mark in the course. Second, at the stagewhere assignments, final exams or term papers are marked,a large number of papers that are poorly written may makeit difficult for markers to uphold the same standards thatthey would apply if papers were better written. This maymean both that all poorly-written papers are marked moreeasily than they would be otherwise, and that a relativelywell-written paper (most likely from a student of Englishspeaking background) is given a higher mark than it wouldhave been given, had it not stood in comparison to so manypoorly-written papers.
5. Conclusion
Using new multi-institutional panel data on Australianundergraduates studying in business schools, I find strong
eview 31 (2012) 587– 600
statistical evidence that both international students andstudents from non-English speaking backgrounds earnpersistently lower marks than other students. Both inter-national students and NESB students perform significantlyworse than other students, even controlling for selectioninto courses. Both effects are large and persist into students’later semesters at university, but NESB status predictsmore of a reduction in own mark than international sta-tus and becomes even more of a hindrance after the firstsemester.
The paper’s second main result is that adding interna-tional or domestic NESB students to a tutorial leads to areduction in most students’ marks, and this is particularlythe case for international NESB student concentrations.This effect is strongest as felt by students from English-speaking backgrounds. Given that the main effect on one’sown marks of being an international or NESB student is neg-ative, strong, and statistically significant, a logical inferenceis that placing these lower-performing students into tuto-rial classrooms with other students would have, if not anegative effect, at least a non-positive effect on those otherstudents’ learning. I therefore interpret the negative effectof tutorial-level percentages of international NESB studentsas a direct negative spillover within tutorial classrooms,that may stem from changes in tutorial dynamics due to themix of students present that, on balance, hinder learning.While English fluency problems could partly explain thisresult, the pattern of estimates – i.e., far stronger negativespillover effects from international NESB student concen-trations than from domestic NESB student concentrations– is consistent with the hypothesis that cultural factorsassociated with NESB status other than English languagecompetency play an important role in shaping the learningcontext at the tutorial level.
The third main result in the paper is that the presenceof more NESB students in a course buoys up the marks ofall students in that course. Conditional on student covari-ates, tutorial composition effects, course fixed effects, andan array of other student and tutorial-level controls, theimpact on marks of a high percentage of NESB studentsin a course is positive. Based on the foregoing evidenceregarding negative performance differentials and negativespillovers within tutorial classrooms, this positive effectof course percentages seems unlikely to reflect a learn-ing effect. Rather, it may reflect supply-side influences,such as downward adjustments to the difficulty of materialor grading standards applied when large concentrationsof such students are present in a course. Both materialselection and final decisions about course marks typicallyoccur for the entire course all at once, rather than tutorial-by-tutorial. In either of those activities, an adjustment bythe teacher to accommodate a larger fraction of lower-performing students is quite plausible.
Taken as a whole, the evidence in this paper stronglyindicates the need for better integration of students fromnon-English speaking backgrounds – particularly inter-national NESB students – into the Australian tertiary
education environment. I conclude that Australian univer-sities should consider carefully whether they are directingsufficient attention and resources towards improving theEnglish fluency and cultural assimilation of these students.
cation Review 31 (2012) 587– 600 597
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G. Foster / Economics of Edu
cknowledgments
I thank the University of South Australia, the Universityf Technology Sydney, and the Australian Research CouncilGrant number DP0878138). I acknowledge the researchssistance of Fei Qiao and Sam Trezise. Special thanks areue to Peter Antony and Graeme Poole for extracting theata used in this paper.
ppendix A.
Fig. 3 shows simple histograms of marks, at the student-y-tutorial level, for international versus non-internationaltudents. Fig. 4 then shows analogous histograms for NESBersus non-NESB students.
Fig. 5 shows the distribution of the percentage of inter-ational students in tutorials, across all tutorials in the
ain analysis sample; Fig. 6 shows the analogous distri-ution for the percentage of NESB students in tutorials.Tables 6–8 show complete results of the regressions
ppearing in Tables 3–5, respectively, with the exception
Fig. 3. Marks: international (bottom) versus non-international (top) stu-dents.
able 6aseline marks equations: full results.
(1) (2) (3) (4) (5)
International −2.779*** −2.291*** −2.355*** −2.561***
(0.27) (0.29) (0.29) (0.28)International* −2.128*** −0.553new student (0.40) (0.41)NESB −3.315*** −3.484*** −3.368*** −3.387***
(0.24) (0.25) (0.25) (0.24)NESB* 0.722* 0.364new student (0.36) (0.35)NESB* −5.449***
international (0.24)NESB* −3.395***
domestic (0.24)Female 1.913*** 1.916*** 1.953*** 1.963*** 1.921***
(0.18) (0.18) (0.17) (0.17) (0.18)Age 0.073** 0.074** 0.079** 0.078** 0.056*
(0.02) (0.02) (0.02) (0.02) (0.03)New student −0.084 0.393 −0.641* −0.768*** −0.439*
(0.16) (0.22) (0.26) (0.19) (0.18)In-state −0.698 −0.709 −0.770 −0.758 −0.760high school (0.63) (0.63) (0.62) (0.62) (0.62)No high school 0.885 0.831 0.338 0.145 0.476information (0.59) (0.59) (0.60) (0.60) (0.60)GPA in other −2.309***
courses (0.02)Tutorial size 0.033*** 0.034*** 0.043*** 0.043*** 0.053***
(0.01) (0.01) (0.01) (0.01) (0.01)Course size −0.005*** −0.005*** 0.645 0.643 0.047
(0.00) (0.00) (0.33) (0.33) (0.30)Courseload 0.841*** 0.820*** 0.827*** 0.789*** 0.806***
(0.09) (0.09) (0.09) (0.09) (0.09)Institution −8.674* −9.044* 288.055* 287.341* 7.703
(4.21) (4.21) (139.61) (140.18) (5652.85)Constant 63.642*** 63.653*** −249.535 −248.711 43.864
(3.58) (3.58) (154.27) (154.89) (7412.93)
Course-by-semester fixed effects? No No Yes Yes YesAdj. R-sq 0.096 0.097 0.162 0.161 0.366Obs 125,214 125,214 125,214 125,214 122,694
his table replicates Table 3 but provides estimation results for more variables. All coefficient estimates except for those associated with fixed effect arraysre reported. Standard errors are clustered at the student level.
* p < 0.05.** p < 0.01.
*** p < 0.001.
598 G. Foster / Economics of Education Review 31 (2012) 587– 600
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05
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Fig. 5. Proportion of international students in tutorials.
01
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erce
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Fig. 6. Proportion of NESB students in tutorials.
Table 7Adding composition effects on marks: full results.
(1) (2) (3) (4)
International −2.390*** −2.467***
(0.28) (0.28)NESB −3.359*** −3.346***
(0.24) (0.24)% intl −3.198***
in tutorial (0.28)% intl 5.227***
in course (0.84)% NESB −3.010***
in tutorial (0.29)% NESB 5.425***
in course (0.87)NESB* −5.456*** −5.456***
international (0.24) (0.24)NESB* −3.436*** −3.430***
domestic (0.25) (0.25)% NESB intl −3.674*** −3.404***
in tutorial (0.31) (0.75)(% NESB intl −0.220in tutorial)2 (0.81)% NESB intl 5.883*** −0.936in course (0.90) (2.24)(% NESB intl 8.261**
in course)2 (2.53)% NESB domestic −0.952* 3.643***
in tutorial (0.44) (0.87)(% NESB domestic −8.349***
in tutorial)2 (1.34)% NESB domestic 4.406** −3.928in course (1.54) (4.12)(% NESB domestic 20.231*
in course)2 (9.91)Female 1.920*** 1.921*** 1.931*** 1.930***
(0.18) (0.18) (0.18) (0.18)Age 0.057* 0.056* 0.056* 0.056*
(0.03) (0.03) (0.03) (0.03)New student −0.441** −0.476** −0.515** −0.533**
(0.17) (0.17) (0.17) (0.17)In-state −0.676 −0.675 −0.673 −0.679high school (0.63) (0.63) (0.63) (0.63)No high school 0.617 0.651 0.470 0.485information (0.59) (0.59) (0.59) (0.59)GPA in other −2.345*** −2.345*** −2.345*** −2.346***
courses (0.02) (0.02) (0.02) (0.02)Tutorial size 0.043*** 0.042*** 0.043*** 0.038***
(0.01) (0.01) (0.01) (0.01)Course size 0.001 0.001 0.001 −0.000
(0.00) (0.00) (0.00) (0.00)Courseload 0.798*** 0.804*** 0.761*** 0.762***
(0.09) (0.09) (0.09) (0.09)Institution 2.628 2.543 2.742 2.705
(.) (3689.93) (4875.37) (.)Constant 53.547 52.500 52.514 53.786
(3968.09) (1453.61) (1890.37) (.)
Adj. R-sq 0.359 0.358 0.358 0.358Obs 122,694 122,694 122,694 122,694
This table replicates Table 4 but provides estimation results for more vari-ables. All coefficient estimates except for those associated with fixed effectarrays are reported. Standard errors are clustered at the student level,and the lack of clustered standard error estimates on some coefficientsresults from the saturation of the model. Standard errors can be recov-ered by assuming independence within student, and on the variables ofmain interest, they are roughly one-third the size of the clustered standarderrors reported above.
* p < 0.05.** p < 0.01.
*** p < 0.001.
G. Foster / Economics of Education Review 31 (2012) 587– 600 599
Table 8Average marks equations for student subgroups: full results.
(1) (2) (3) (4) (5) (6)Dep. var: Average marks of ESB students Average marks of NESB intl students Average marks of NESB domestic students
% NESB intl −5.563*** −5.489*** −1.015*** −0.250 −5.310*** −3.313***
in tutorial (0.14) (0.33) (0.19) (0.54) (0.33) (0.79)(% NESB intl −0.146 −0.633 −2.370*
in tutorial)2 (0.42) (0.53) (0.94)% NESB intl 3.826*** −1.509 8.375*** 8.563*** −0.670 4.067in course (0.41) (0.95) (0.54) (1.55) (1.08) (2.67)(% NESB intl 7.307*** −0.212 −6.033in course)2 (1.16) (1.59) (3.10)% NESB domestic −1.934*** 0.737 −0.412 5.162*** −0.797 3.923***
in tutorial (0.20) (0.40) (0.32) (0.63) (0.45) (1.03)(% NESB domestic −5.101*** −10.699*** −7.360***
in tutorial)2 (0.67) (1.00) (1.42)% NESB domestic 5.931*** 1.858 1.288 −12.417*** 4.171* −2.937in course (0.71) (1.82) (1.15) (3.08) (1.92) (6.50)(% NESB domestic 10.321* 35.010*** 13.488in course)2 (4.68) (7.76) (15.06)Female 3.578*** 3.539*** 1.678*** 1.658*** 1.194*** 1.198***
(0.11) (0.11) (0.14) (0.14) (0.18) (0.18)Age 0.308*** 0.309*** −0.202*** −0.200*** −0.083*** −0.084***
(0.01) (0.01) (0.03) (0.03) (0.02) (0.02)New student −0.635*** −0.717*** −0.587*** −0.600*** −0.839*** −0.824***
(0.12) (0.12) (0.15) (0.15) (0.24) (0.24)In-state −0.191 −0.266 3.125*** 3.166***
high school (0.30) (0.30) (0.51) (0.50)No high school 0.180 0.144 5.000*** 4.957***
information (0.26) (0.26) (0.46) (0.46)GPA in other −2.534*** −2.534*** −2.602*** −2.609*** −2.462*** −2.461***
courses (0.01) (0.01) (0.02) (0.02) (0.03) (0.03)Tutorial size 0.040*** 0.038*** 0.049*** 0.043*** 0.046*** 0.039***
(0.00) (0.00) (0.00) (0.00) (0.01) (0.01)Course size −0.001** −0.001*** 0.002*** 0.002*** −0.000 −0.000
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)Course load 1.170*** 1.185*** 2.684*** 2.579*** 1.645*** 1.645***
(0.05) (0.05) (0.13) (0.13) (0.09) (0.09)Institution 13.817*** 13.065*** −1.707 −2.518 −8.478 −8.751
(2.09) (2.09) (5.73) (5.72) (8.77) (8.77)Constant 44.784*** 46.038*** 49.574*** 51.246*** 75.834*** 76.052***
(0.97) (0.98) (2.12) (2.15) (7.67) (7.69)
Adj. R-sq 0.673 0.674 0.657 0.659 0.575 0.576Obs 66,580 66,580 36,608 36,608 21,913 21,913
This table replicates Table 4 but provides estimation results for more variables. All coefficient estimates except for those associated with fixed effect arraysare reported.
om
R
A
A
A
B
B
C
* p < 0.05.** p < 0.01.
*** p < 0.001.
f results for the arrays of fixed effects which are too volu-inous to report.
eferences
E. (2009). The Australian education sector and the economic contributionof international students. Access Economics Pty Ltd.
EI. (2010). AEI International Student Data YTD April 2010. Australian Edu-cation International.
rcidiacono, P., Foster, G., Goodpaster, N., & Kinsler, J. Estimating spilloversusing panel data, with an application to the classroom. QuantitativeEconomics, in press.
orjas, G. (2000). Foreign-born teaching assistants and the academic per-formance of undergraduates. American Economic Review Papers andProceedings, 90(2), 355–359.
radley, G. (2000). Responding effectively to the mental health needs ofinternational students. Higher Education, 39(4), 417–433.
heng, D., & Leong, F. T. L. (1993). Cultural differences in psychologi-cal distress between Asian and American college students. Journal ofMulticultural Counseling & Development, 21(3), 182–189.
Foster, G., & Frijters, P. (2010). ‘Students’ beliefs about peer effects. Eco-nomics Letters, 108(2010), 260–263.
Gould, E., Lavy, V., & Paserman, D. (2000). Does immigration affect thelong-term educational outcomes of natives? Quasi-experimental evi-dence. Economic Journal, 119, 1243–1269.
Guryan, J. (2004). Desegregation and black dropout rates. American Eco-nomic Review, 94(4), 919–943.
Jacobs, L. C., & Friedman, C. B. (1988). Student achievement under foreignteaching associates compared with native teaching associates. Journalof Higher Education, 59(5), 551–563.
Jensen, P., & Rasmussen, A. W. (2011). The effect of immigrant concentra-tion in schools on native and immigrant children’s reading and mathskills. Economics of Education Review, 30(6), 1503–1515.
Johnson, P. (1988). English language proficiency and academic perfor-mance of undergraduate international students. TESOL Quarterly,22(1), 164–168.
Lebcir, R., & Wells, H. (2008). Factors affecting academic performance of
international students in project management courses: A case studyfrom a British post 92 university. International Journal of Project Man-agement, 26(3), 268–274.Lee, H., & Anderson, C. (2007). Validity and topic generality of a writingperformance test. Language Testing, 24(3), 307–330.
cation R
600 G. Foster / Economics of EduLight, R., & Xu, M. (1987). English proficiency and academic per-formance of international students. TESOL Quarterly, 21(2),251–261.
Olsen, A., & Burgess, Z. (2006). The comparative academic performanceof international students. International Higher Education, 42(42),11–12.
Picard, M. (2007). English entrance requirements and language supportfor international postgraduate students. In Enhancing higher educa-
tion, theory and scholarship: Proceedings of the 30th HERDSA annualconference.Plewa, C., & Sherman, C. (2007). Dimensions of diversity: Group workwithin marketing courses. In ANZMAC 2007 conference: Reputation,responsibility, relevance.
eview 31 (2012) 587– 600
Small, A. (1973). Marking practices in historical perspective. EducationalStudies, 4(4), 189–197.
Stoynoff, S. (1997). Factors associated with international students’ aca-demic achievement. Journal of Instructional Psychology, 24(1), 56–68.
van Ewijk, R. (2011). Same work, lower grade? Student ethnicity andteachers’ subjective assessments. Economics of Education Review,30(5), 1045–1058.
Winston, G., & Zimmerman, D. (2004). Peer effects in higher education. In
College choices: The economics of where to go, when to go, and how topay for it. University of Chicago Press.Zhang, Z., & Brunton, M. (2007). Differences in living and learning: Chineseinternational students in New Zealand. Journal of Studies in Interna-tional Education, 11(2), 124–140.