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[ Proceedings Contents ] |
The only thing that many prominent past researchers are prepared to say for sure about the factors that impact on student performance in first year economics is that student background characteristics are important as opposed to instructional, institutional or other factors. Overseas studies have emphasised the impact of age, gender, socioeconomic status, past studies, aptitude and the like. This paper investigates the impact of these and other student background characteristics in a contemporary Australian setting and over three different modes of instruction of essentially the same subject. By using quantitative techniques to compare the impact of student background characteristics in internal, external and Open Learning modes, and supplementing this with the use of qualitative methodologies, the reasons for the significance of these factors can be investigated. Knowledge about the importance of these factors and the reasons behind them can have unmistakable implications for teaching and learning in this and other first year subjects.
An extensive literature review had revealed that past studies have generally agreed that student background characteristics, rather than institutional and instructional factors, are important to success in Economics.
Most studies (Crowley and Wilton (1974); Siegfried (1979); Ferber et al (1983); Lumsden and Scott (1987) ; Soper and Walstead (1988); Heath (1989); Walstead and Soper (1988 and 1989); Gohman and Spector (1989) ; Watts and Lynch (1989); Myatt and Waddell (1990) ;Tay (1993) ; Van Scyoc and Gleeson, (1993); Anderson, Benjamin, and Fuss, (1994)) have found that males perform better than females at both high school and university level, and that the gender gap may actually widen as students progress through the various levels of their education. A smaller number of studies contradict this result and find that there is no difference in performance between males and females (Kelly, (1975); Rhine (1989); Ferber, Birnham and Green (1983); Buckles and Freeman (1983); Watts (1987); Williams, Waldauer, and Duggal (1992); Brasfield, Harrison and McCoy (1993); Douglas and Sulock (1995); Hirschfield, Moore and Brown (1995)). Others find that the females perform better than males (Lumsden and Scott (1987)), especially where essay tests are used to measure performance rather than multiple choice tests. Thus there is far from universal agreement about this factor but there seems to be a majority view that it does exist to some extent.
Most studies, and a great deal of anecdotal evidence, suggest that more mature students are more successful in economics; or that there is a positive relationship between age, used as a proxy for maturity, and performance (Atteyeh and Lumsden (1971); Buckles and McMahon (1971); Cohn (1972); Siegfried and Fels (1979); Gohmann and Spector (1989); Walstad and Soper (1988); Watts and Lynch (1989) ; Tay (1993); Douglas and Sulock (1995)). This result has been contradicted in other studies where no relationship has been found between age and performance, or a negative relationship has been found (Anderson, Benjamin and Fuss (1994)).
In most studies a positive relationship has been found between a student's general aptitude or intelligence, as measured by their achievement in other subjects, and their performance in economics (Clauretie and Johnson (1975); Brasfield, Harrison and McCoy (1993); Van Scyoc and Gleeson, (1993)). Thus if a student has learned to be successful in their study, it has been found that this success carries over to their first year economics classes.
The relationship between non-English speaking background and performance has received little attention in economic literature. Also there has been no evidence gathered to date which suggests that a student's employment situation has any significant effect on their performance.
Recent Australian evidence suggests that students from wealthier socioeconomic groups are more successful in gaining entrance into university, although documentation on the impact of socioeconomic status is sparse in the economic education literature. Anecdotal evidence, and a commonly held belief of many academic staff, suggests that economics students should perform better than non-economics students as they are specialising in the field and should be more motivated to do well, and more interested in the subject area. Other students in business degrees are obliged to do economics and often do so under considerable duress. A study by Petersen (1992) found that substantial differences exist between students who elect economics and those who do not. Douglas and Sulock (1995), found that economics majors tended to do better in the course, indicating stronger preparation and higher interest postulated above.
Some of the other factors that will be investigated are either unique to the (South) Australian situation and/or not covered in the American or other literature. They include whether a student is full or part time, their entry basis (matriculation, non-matriculation, tertiary transfer or mature age), mode of study (internal, external or open learning), disabilities, aboriginality and isolation of place of a students' home residence.
Previous studies generally used relatively narrow measuring instruments, as they mainly used standardised multiple choice tests for the dependent variable, student performance. Furthermore only a relatively narrow range of independent variables were investigated, and previous studies tended to be confined mainly to American universities. This study has enabled both a comparison of relevant factors to those found to be important in other countries and an extension of that analysis to a wider range of dependent and independent variables, in an Australian setting. A further extension of the analysis will also been carried out as this study will ultimately include students studying off campus (External and Open Learning students). The focus of previous work has been on internal students only and as such there has been little research into these alternative modes of studying economics. This extension has also allowed a more in depth investigation into the reasons for the importance of various factors by comparing across all three modes of study. The reasons for the importance of the various background characteristics is something that many previous studies are only guessing at.
The subject involved in this study is called Economic Foundations which is a first year economics subject, mainly microeconomics in content, taught by economists from the School of Economics, Finance and Property. Most of the first year students who study this subject are from the Faculty of Business and Management, the largest of the six faculties at the University of South Australia. The majority of the students studying Economic Foundations are required to take this subject as part of the core requirements of a Business or Economics award. Other students take the subject as an elective or broadening education requirement in other awards. Overall in 1995 students come from 32 different awards and only 7.4% of the 1995 students are economics students (4% in 1994) enrolled in the Bachelor of Applied Economics. Economics 11, which is the equivalent microeconomics subject taken by open learning students, was co-authored by University of South Australia academic staff, and is very similar in terms of content, approach and standards, to Economic Foundations. At this stage there is no award that students can complete fully in open learning mode but students who pass Economics 11 can gain credit for this subject (in lieu of Economic Foundations) in the economics and business degrees offered at the University of South Australia.
The subjects for this paper included 1240 students who were correctly enrolled in either semester one and two after the H.E.C.S. census date, in Economic Foundations in 1995 at the University of South Australia. The study also investigated, albeit in a more limited manner, 1207 students from 1994 but for the sake of brevity, these results have not been included in this paper. The 1240 students in the 1995 component, ranged from those who completed all aspects of the microeconomics subject (both exam and continuous assessment) to those who were enrolled but scored zero as they did not attempt any of the course. Of these 1240 students, 97 (7.8%) were external students. Another 122 students additional to the 1240 who were correctly enrolled, actually began the subject but withdrew before the H.E.C.S. date, and thus do not appear on the universities records. Consequently they were omitted from the regression analysis due to the lack of available data, but were included in other qualitative aspects of the study, which have yet to be completed. These students only took the subject for a maximum of three to four weeks, and some did not attend classes at all, despite the fact that they initially enrolled and filled in tutorial preference lists. They were only to be found on the subject coordinators records of initial tutorial lists. These students were identified and are to be followed up in a separate section of the study, using telephone interviews. They will be asked about their background characteristics and reasons for early withdrawal.
Additionally student performance was measured by components of this final grade, namely exam performance alone and each of the three sections of the exam, sections A (multiple choice), B (short answer) and C (essay). These results were taken from subject coordinators records and added to the SPSS data base. It was then possible to compare how student background factors impacted on a students performance in the exam as compared to final grade, and separately in each of the three sections. Thus in total six different dependent variables were used to measure performance.
The independent variables were largely measured by taking the data from the SRIS system. This information primarily was processed from student's enrolment forms. The enrolment form includes 13 questions regarding student details (age, gender, course load etc.) and another 15 questions about racial origin, citizenship, country of birth and residence and previous studies. Students provide this information and it is generally not subject to checking.
Information regarding the students socioeconomic status and residency (rural, urban or isolated) was gleaned from a 1994 study carried out by L. Martin entitled "Equity and General Performance Indicators in Higher Education". Finally data on Open Learning students was obtained from the limited records kept by the Flexible Learning Centre located at the Underdale campus of the University of South Australia.
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These variables are described in more detail in Table 1 on the next page.
| Independent variable in hypothesis | Meaning |
| GENDER | Male/female |
| ATSI | Aboriginal or Torres Strait Islander |
| DISAB | Disabled |
| ISOL | Living in isolated (rural/country) locations |
| NESB | Non-English speaking background |
| SOCIO | Socioeconomic status |
| AGE | Age - proxy for maturity |
| ECON | Economics students, enrolled in B.Economics |
| APTITUDE | Aptitude or ability |
| LOAD | Part or full time student |
| MODE | Internal, External or Open Learning student |
| ENTRY | Entry basis to university e.g. matriculation |
| REPEAT | Repeating students |
| EMPLOY | Employed students |
Several regression analyses were undertaken to test for the impact of the various student background characteristics on student performance. Six different Multiple Linear Regressions were run, one for each of the six different dependent variables. As an extension to this analysis, further Multiple Linear Regressions, for each of the dependent variables will also be run, after separating out internal and external students into separate data bases. The purpose of these latter regressions is to investigate the difference between the factors affecting each of the two groups of students. Ultimately it is hoped that differences will emerge which will enable investigation of the reasons for the impact of the various factors.
| Student characteristic | Internals and externals combined | Internals only | Externals only |
| Numbers | 1240 | 1143 | 97 |
| ATSI (%) | 1.4% | 1.4 % | 1.7% |
| Disabled | 0.8% | 0.7% | 2.1% |
| Economics students | 7.2% | 7.7% | 4.1% |
| Employed | 43.3% | 40.25 | 79.4% |
| Entry basis | Continuing = 22.5% Commencing - from high school = 49.2% - other = 28.3% | Continuing = 19.8% Commencing - from high school = 52% - other = 28.2% | Continuing = 54.6% Commencing - from high school = 16.5% - other = 28.9% (mainly incomplete higher education) |
| Gender | 45.2% female | 44.1% female | 57.7% female |
| Load | 69% full time 31% part time | 73.1% full time 26.9% part time | 21.6% full time 78.4% part time |
| NESB | 18.9% | 19.5% | 11.3% |
| Repeaters | 3.9% | 3.1% | 12.4% |
| Residence | urban = 81.1% rural = 8.7% isolated = 2.5% overseas = 7.6% |
urban = 81.5% rural = 8.3% isolated = 2.0% overseas = 8.2% |
urban = 77.3% rural = 13.4% isolated = 8.2% overseas = 1.0% |
| Socioeconomic status | H = 36.3% M = 43.4% L = 20.3% | H = 36.8% M = 43.6% L = 19.6% | H = 30.2% M = 41.7% L = 28.1% |
| Age | From 16 to 54 Average = 22 | From 16 to 54 Average = 21.5 | From 17 to 50 Average = 27.6 |
| Aptitude | From 0 - 87% average = 52.2% | From 0 - 87% average = 52.9 | From 0 - 76% average = 43.8% |
There are important differences between internal and external students. External students are much more likely to be employed (79.4% compared to 40.2%). They are more likely to be continuing rather than commencing students, and more are female. They are also much more likely to be part time students (78.4% compared to 26.9%). Surprisingly enough almost as many live in the city as internal students, and they are only a little more likely to be rural or isolated. They are more likely to be in the lower socioeconomic group, slightly older than their internal counterparts and achieved lower results in other subjects, as measured by their course weighted mark.
| Grade | Internals and externals combined | Internals | Externals |
| HD | 0.3% | 0.3% | 0% |
| D | 2.6% | 2.7% | 1.0% |
| C | 17.5% | 18.4% | 7.2% |
| P1 | 33.4% | 34.1% | 24.7% |
| P2 | 17.5% | 17.3% | 19.6% |
| NP | 2.7% | 2.9% | 1.0% |
| F1 | 11.1% | 11.4% | 7.2% |
| F2 | 11.6% | 9.9% | 32.0% |
| W & WF | 3.2% | 2.9% | 7.2% |
There are substantial differences in grades achieved by the two groups. Very few externals receive Distinction or above grades, compared to internals; and many more externals receive the lowest score of F2 (almost a third).
The only variable that was significant over all six regressions was APTITUDE. The beta coefficient for aptitude was positive over all of the regressions indicating the positive relationship between performance and aptitude in other subjects. This is consistent with other studies, and thus an expected result, as students who do well in other subjects also do well in economics.
The only two variables significant over 5 of the 6 regressions were MODE and NESB. Mode had a positive beta coefficient for all 5 dependent variables, with the exception of section C of the exam. Thus indications are that internal students performance is superior to that of external students in all areas except for the essay section of the examination, where the relationship is not significant. Once again this result confirms expectations gleaned over many years experience in teaching the two modes. Anecdotal evidence, including many telephone calls from distressed distance students, suggests that external students have more difficulty with Economics than internal students because of the lack of face-to face assistance, feelings of isolation and quality of materials, amongst other factors.
The beta coefficient was negative in all cases for NESB indicting the negative relationship between students with a non-English speaking background and performance, or that NESB students performance is consistently worse than students with an English speaking background. Once again this is an expected result, from the perspective of both anecdotal evidence and other studies. Language difficulties do tend to inhibit student performance.
GENDER was the only variable significant over 4 of the regressions, with a negative beta coefficient for all sections apart from section C of the exam. Males have performed better than females in total exam and section B (short answer article responses) and A (multiple choice) of the exam. However females perform better in section C of the exam (essay). There is no significant difference between the performance of males and females when overall mark is considered, either with or without withdrawals. Evidence from other studies with respect to gender is mixed, as outlined earlier in the paper, and is mirrored by these results. However there seems to be more evidence that females are disadvantaged in their economics studies, rather than the reverse, especially where short answers are required.
Four variables were significant with respect to one dependent variable only. Using total grade with withdrawals missing, ECON (economics) students performed significantly better than non economics students (a positive beta coefficient). This perhaps is a little surprising given the generally lower cut off scores of economics students as compared with other students. For example in 1995 Applied Economics (IBEN code with 92 students) had a cut off score of 44 compared to 49 in Banking and Finance (IBBF code with 114 students), 47 in Administrative Management (IBAM code with 81 students), 50 in Management (IBMA code with 243 students) and 45.5 in Accountancy (IBAY code with 349 students). Perhaps this finding reflects the increased interest or better preparation for the subject by economics students, as suggested by previous studies in the earlier part of the paper.
Using Section C of the exam (essay), younger students performed better than older students (a negative beta coefficient with AGE); and using Section B of the exam higher socioeconomic groups performed better than students from low socioeconomic groups ( a positive coefficient with SOCIO).
LOAD was only significant using total grade, with withdrawals as zero (a positive relationship). Thus full time students perform better than part time students using the total grade with withdrawals as zero, but not using withdrawals as missing. Mixed evidence with respect to the above four variables is difficult to interpret.
The remainder of the variables, namely ATSI (Aboriginal Torres Strait Islanders), DISAB (disabled), EMPLOY (employment), ENTRY (entry), REPEAT (repeaters) and ISOL (by residence) were not significant at the 95% probability level for any of the dependent variable measures. It may be that the numbers of disabled students (only 0.8% of the students in 1995) is too small to achieve any result in this regard, as it would be expected that these students would be at a disadvantage due to their disability. The findings for employment and entry are as expected from other studies. However it may have been expected that repeaters would perform better than those studying the subject for the first time. It appears that those students who find economics difficult are not helped a great deal by repeating the subject. Alternatively the low number of repeaters may have affected this result or the fact that it was only possible to detect repeaters from first semester to second semester of 1995, and not those who had repeated from previous years.
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| Author: Vicki Feast, School of Economics, Finance and Property, University of South Australia Fax: 302 1512 Email: Vicki.Feast@Unisa.edu.au Please cite as: Feast, V. (1996). The impact of student background characteristics on performance in first year microeconomics. Different Approaches: Theory and Practice in Higher Education. Proceedings HERDSA Conference 1996. Perth, Western Australia, 8-12 July. http://www.herdsa.org.au/confs/1996/feast.html |