What factors contribute to higher grades in a first-year undergraduate management unit: an exploratory study at an Australian university

ABSTRACT

There are multiple challenges associated with participating in a first-year undergraduate management unit at the university level. Students’ performance in such units is a critical milestone in their learning journey and can create positive/negative inertia for their future performance and continuation in a program. Through exploratory abductive analysis of 990 undergraduate students’ performance in a first-year, first-semester management unit at an Australian university, we find that tutor’s qualification, tutor’s gender, student’s age, entry score, number of simultaneous degrees undertaken, and number of tutorials attended are critical factors for success. Our data indicates tutor’s experience, student’s gender, and engagement with the learning management system and discussion forum do not have a statistically significant influence. As the massification of higher education has led to a growing body of international students in Australian universities, we also compare and contrast the effect of these factors between international and domestic cohorts. Our results indicate that critical factors influence domestic and international undergraduate students differently (positive vs. negative vs. non-linear). We propose a theoretical model by connecting empirical constructs, current theories, and emerging theoretical constructs. The practical implications relevant to business school leadership teams and faculty members in Australian and other similar universities are also discussed.

KEYWORDS:

• Undergraduate education
• Australia
• student’s success
• international students

Introduction

Student motivations for achieving an undergraduate university degree in Australia include the importance of employability and the need to improve their career prospects (Bunch, 2019). Before the COVID-19 global health crisis, degree-level education in countries such as Australia, the UK, and the USA was projected to increase from 27% in 2020 to 37% of the eligible population by 2050 (Roser & Ortiz-Ospina, 2013). However, as the Australian government reviews the rising job market crisis and tightens its belt as recession hits, means of support are limited or not available to international students even if they can arrive in the country (Grozinger & Parsons, 2020). This disruption to the Australian higher education sector means that it is imperative for Australian universities to look for ways of attracting, retaining, and promoting both domestic and international student success (Mok et al., 2021).

Despite the high numbers of students entering the higher education sector, many find the transition from high school to university challenging and sometimes insurmountable (Kift & Nelson, 2005). This is especially true in the initial stage of undergraduate business programs where students need to take generic management units, a common occurrence in Australian universities. Performance in this unit is a critical milestone for the students as it creates a positive inertia for their future performance and continuation in the program and the university.

There are a range of factors that influence student success along a continuum from intrinsic (such as motivation, self-determination, and sense of belonging) to extrinsic (such as demographic background, class size, study hours, and institutional support mechanisms). These factors are important to understand as the costs to the individual, the institution, and society as a whole, of not supporting student success can be wide ranging. It is important, therefore, that we comprehensively interrogate these factors, particularly in undergraduate programs and leverage these to enable retention, progression, degree completion, and future success.

Finally, international students contribute financially to the university economy and make up more than half of all Business School students in many universities. Alongside the growing Chinese students (around 38%), 20% of international students in the USA are from India whereas South Korea contributes roughly 5% of the international cohort (a total of around 1 million). Similarly, there is a large number of international students in Australia with numbers approximating 440,000 in 2022 (although this is down 15% on 2021 figures (Austrade, 2022)). In spite of the increasing economic and strategic importance of international students in universities, we have a limited understanding of the success factors for international business students compared to their domestic counterparts. One may assume differences between the two cohorts related to educational backgrounds, language, and cultural aspects, but if these exist, how should we best respond to assure success for all?

With these aims in mind, our research questions are what are the factors that influence undergraduate students’ academic achievement in an early-stage management unit at an Australian university and how does the influence of those factors vary for domestic and international students?

Current literature

Student academic success

A study that investigated the differential between student scores as they entered the university system and how well they did during their first year found several significant predictors of academic success, including gender (males fared better in the first year), father’s education level (higher), perceived stress (lower), and (positive) transition perception (Wintre et al., 2011). Kuh et al. (2008) examined students’ demographic characteristics, pre-college experiences, and prior academic achievement as predictors of grade point average (GPA) with the latter having the strongest influence. Jansen and Bruinsma (2005) argued against the common stereotype of older people being deficient in intellectual skills and found that older students are ‘better disciplined and better able to use the appropriate deep information processing strategies’ (p. 238). Studies have also shown that a good teacher–student relationship results in a better student experience including improved learning (Kahu & Picton, 2019) though the contextual factors involved are under-researched in the higher education sector (Hagenauer & Volet, 2014).

There are some gender-based studies in the literature, for example, one study found some gendered differences in their study of female computer students’ achievement and tutor’s academic qualifications (Lang et al., 2007). In their sample of 43 tutors, females had more teaching qualifications and men had more programming experience and the performance of female students was lower than their male counterparts. On the contrary, Marx et al. (2016) investigated student–tutor relationships with grade improvement and found that there were no dependencies between any demographic traits, such as race and gender, nor with tutors’ experience or training level. Kwizera et al. (2001) also found tutor subject-matter expertise has little or no influence on student achievement when implementing problem-based learning.

We know that students with lower entry scores struggle with university study and report lower levels of academic engagement than other students (Baik et al., 2015). Yet literature that reports on the specifics of how this low entry score impacts student achievement is scarce. Similarly, whilst the literature discusses findings on students balancing other commitments with study (McKenzie & Schweitzer, 2001), there are little to no studies reporting on the details of these other commitments such as the number of degrees they study concurrently and how this impacts their academic achievement.

We also found a dearth of research looking specifically at student success in our context of undergraduate first-year management core units. Given the high percentage of international students who comprise this cohort (42% at our Business School) further investigation is warranted.

Internationalization of the student cohort

Highly ranked universities attract a higher proportion of international students along with their corresponding high fees to attend these prestigious institutions (Knight, 2012). That income affords the university the ability to employ esteemed researchers who in turn contribute to the ranking position. Depending on your point of view this cycle is known as vicious or virtuous (Castro et al., 2016). It is therefore in our best interests (both economical and reputational) to ensure our international cohorts’ educational success.

Studies have investigated the noncognitive factors that influence international students’ academic success such as self-acceptance, work-avoidance, and help-seeking (Pyburn et al., 2016). In addition to adjusting to university life, international students must also deal with cross-cultural adjustment (Shmueli Gabel et al., 2005). One study on the transition issues of international students in Australia found that in addition to personal and social loneliness, there is a third lens, cultural loneliness that is more difficult to overcome (Sawir et al., 2008).

Domestic and international student success

Though international students have become a significant part of the Australian university cohort, the success factors are different for them compared to domestic students.

A study examining how motivation and self-regulated learning efficacy influence American community college domestic and international students’ academic performance, found that only age emerged as important for international students’ GPA and that none of the independent variables affected domestic students’ GPA (Lioa et al., 2012). Whilst many programs and interventions are directed at all students, they tend to be domestic-centric and often have an alienating effect on international students. These researchers also found that for international students, motivation only affected GPA through the mediating influence of self-regulated learning efficacy. A number of reasons for these results were discussed including the fact that international students may have had a different education ‘training’ and may be better prepared due to their age difference (tended to be older), had been in school longer, resulting in a higher number of academic credits.

Both domestic and international student retention in Australian universities is of paramount importance in order to build human capital to drive innovation (Haverila et al., 2020). Conflicting findings have been reported in the literature regarding domestic and international student retention. Whilst DeBerard et al. (2004) found international students are likely to discontinue their studies based on unsatisfactory performance, Haverila et al. (2020) found that there was only a slight impact on retention intention and that there was no significant difference between domestic and international. International students often have to put extra effort into adjusting to their new environments, college life, and social interactions and therefore may perform poorly in their first semesters of study (Albert, 2010). However, Haverila et al. (2020) found that adjustment to college life was not a contributing factor to retention intention even though it was more important to international students. They also found that for domestic students, the quality of instruction was the most significant factor for their retention intention.

In summary, there are a range of factors that influence student success with particular differences between domestic and international cohorts. This research study investigates these factors in a specific context, a very large first-year undergraduate management unit. In the next sections, we will introduce the data we analysed and the statistical methods we applied. Then we will discuss significant findings and present a theoretical model in which to further our understanding of the relationships between these factors.

Methods

Empirical approach

In this study, we conducted an exploratory abductive analysis of the students’ academic performance in one of the first-year compulsory management units of study in a leading Australian university business school. Abductive approaches allow us to interpret the statistical findings through the lenses of our practice as teacher and educational developer as well as to generate plausible and conjecturable explanations.

Research setting

We use students’ performance data from an undergraduate program in a Group of Eight (Go8)¹ Australian research-intensive university. Seven of the Go8’s members are in the world’s top 100 universities and all Go8 members are ranked in the world’s top 150 universities. The Go8 educates 380,000 students – more than a quarter of all higher education students in Australia, and includes 100,000 international students from some 200 countries, which equates to one-third of Australia’s international students. The Business school at this university has approximately 12,000 students and 500 staff. Our University Low Risk Ethics Committee approved this study (no:2023/247).

The management unit under investigation

We use one of the compulsory management units for incoming undergraduate students in one of the largest Australian universities, as our sample. This first-year unit is designed to provide commencing students with insights into the practice of business. We use only Semester 1 (Feb–June) data of the year of study as this semester’s starting time is aligned with the Australian high-school system and represents an appropriate balance between domestic and international students.² This unit consists of a weekly lecture and tutorial each of 90 min duration across a 13-week semester.

Sample construction

The dataset for this research was created through a multi-stage process. First, student demographic data such as birth country, nationality, age, and number of enrolled degrees were collected. Second, we collected entry scores from the admission office. In Australia, Australian Tertiary Admission Rank (ATAR) is the primary criterion for entry into undergraduate university programs. International students’ entry scores are converted into ATAR to standardize entry criteria. Third, we gathered data about the tutors such as educational background and gender. Finally, we collected final grades for each of the students from the unit of study under investigation. Each of these four datasets is deidentified and linked through a unique student identifier. The final merged data set has information for 990 students.

Definition of variables

Variables under investigation are displayed along with their definition and linkage to the prior literature in Table 1.

Table 1. Explanatory and contingent variable definitions and their connections to the literature.

Variable	Description	Literature Reference
Result	Ordinal form in five different grade categories – High distinction (HD) indicates a mark range of 85–100, Distinction (D) indicates a mark range of 75–84, Credit (CR) indicates a mark range of 65–74, Pass (PS) indicates a mark range of 50–64, and Fail (FA) indicates a mark range of 0–49	Gutiérrez & Tomás, 2019
Tutor.PhD	Takes ‘PhD’ if a tutor has a PhD, otherwise takes ‘Non–PhD’	Heng, 2014
Tutor.Exp	A continuous variable that captures tutors’ experience teaching in higher education in years	Kwizera et al., 2001; Lang et al., 2007
Tutor.Gender	A dichotomous variable (Tutor.Gender) that takes ‘male’ for male tutors, otherwise takes ‘female’	Lang et al., 2007; Marx et al., 2016
Student.Age	A continuous variable that captures a student’s age (years)	Lioa et al., 2012; Bornschlegl et al., 2020
Student.Gender	A dichotomous variable (Student.Gender) that takes ‘male’ in the case of a male student, otherwise takes ‘female’	Wintre et al., 2011
Entry.Score	A continuous variable between 0 and 100 that captures ATAR for domestic students or an equivalent score for international students	Baik et al., 2015
Degree.No	Takes ‘Dual–Degree’ for dual degree or ‘BCom’ if the student is enrolled only in one degree	Johnson & Walberg, 1989; McKenzie & Schweitzer, 2001
Tutorial.Missed	A numeric variable that captures the number of tutorials missed by a student in the unit	Kuh et al., 2008
Page.Views	The number of times a student accessed content pages in the learning management system during the regular semester timeframe	Vallis et al., 2022
Ed.Participations	The number of postings in an online discussion forum (Ed) through a continuous variable	Vallis et al., 2022
Home.Country	An ordinal variable (Home.Country) that takes ‘Domestic’ for Australian students and otherwise takes ‘International’	Sawir et al., 2008

Model specification

Considering that our dependent variable is ordinal (five different grade categories – HD, D, CR, PS, and FA), we use the ordinal logistic regression technique.

Results

We have mentioned the direct effect of all the independent variables in model 1 (Table 2). All the predictors except Tutor Experience, Student Gender, Page Views, and Ed Participations are significant. There was no student whose Total Marks warranted an HD. Hence, each of these predictors has a significant influence on the likelihood of a student receiving a certain higher grade (D, CR, and PS) relative to Failing.

Table 2. Direct effect of explanatory variables and contingent variable.

	Model 1						Model 2
	Beta	S.E.^a	p-value	2.5% CI^b	97.5% CI	OR^c	Beta	S.E.	p-value	2.5% CI	97.5% CI	OR
Tutor.PhD(PhD)	0.466	0.155	0.003	0.163	0.770	1.594	0.555	0.157	<0.001	0.249	0.863	1.742
Tutor.Exp	0.001	0.007	0.874	−0.013	0.015	1.001	−0.001	0.007	0.866	−0.015	0.013	0.999
Tutor.Gender(male)	−0.257	0.135	0.056	−0.521	0.007	0.773	−0.233	0.136	0.086	−0.499	0.033	0.792
Student.Age	−0.260	0.046	<0.001	−0.350	−0.170	0.771	−0.167	0.046	<0.001	−0.280	−0.050	0.846
Student.Gender(male)	0.053	0.128	0.677	−0.198	0.305	1.055	−0.109	0.131	0.407	−0.367	0.148	0.897
Entry.Score	0.073	0.010	<0.001	0.053	0.093	1.076	0.118	0.010	<0.001	0.080	0.158	1.125
Degree.No(Dual-Degree)	1.178	0.149	<0.001	0.886	1.471	3.249	0.620	0.163	<0.001	0.301	0.941	1.859
Tutorial.Missed	−0.599	0.059	<0.001	−0.715	−0.483	0.549	−0.593	0.061	<0.001	−0.714	−0.477	0.552
Page.Views	0.000	0.000	0.269	0.000	0.000	1.000	0.000	0.000	0.023	0.000	0.001	1.000
Ed.Participations	−0.016	0.011	0.168	−0.038	0.007	0.985	−0.021	0.011	0.063	−0.043	0.001	0.979
Home.Country(International)							−1.498	0.164	<0.001	−1.833	−1.168	0.224
1Fail|2Pass	−1.835	0.002	<0.001				3.319	0.003	<0.001
2Pass|3Credit	1.684	0.212	<0.001				7.006	0.220	<0.001
3Credit|4Distinction	4.410	0.242	<0.001				9.915	0.256	<0.001
AIC	1939.145						1859.359

^a Standard error.

^b Confidence interval.

^c Odds ratio.

For students who were taught by a tutor who has a PhD, the odds of obtaining a higher grade rather than Fail is 1.594 times or 59% (p = 0.003) that of students who were taught by a tutor not having PhD, controlling for all other variables. Therefore, tutors who have a PhD are more effective compared to tutors who do not have a PhD.

Interestingly, tutor experience is not statistically significant (p = 0.874). Therefore, in our sample, the experience of the tutor did not play any significant role in determining the student’s performance in the unit. This can be explained since the tutor conducts the tutorial based on the scripted guidelines created by the core faculty members. Likewise, the effect of gender of the tutor is statistically non-significant (p = 0.056). We have provided a possible explanation of this observation in the discussion section.

For every one-year increase in student age, the odds of obtaining a higher grade rather than Fail is multiplied 0.771 times (significant at p < 0.001) controlling for all other variables. Therefore, for every one-year decrease in student age, the odds of obtaining a higher grade rather than Fail is increased by 23%.

Our results indicate that student’s gender does not have any significant (p = 0.677) role in explaining the outcome. Therefore, we cannot explain the result based on student’s gender.

For every one-point increase in the entry score, the odds of obtaining a higher grade rather than Fail is multiplied 1.076 times controlling for all other variables (p < 0.001). In short, students having a higher entry score are more likely to obtain a higher grade.

For dual degree, students, the odds of obtaining a higher grade rather than Fail is 3.249 times (p < 0.001) that of students who are only studying one degree, controlling for all other variables. Thus, dual-degree students have a significantly higher likelihood of obtaining higher grades.

For every additional one tutorial that is missed, the odds of obtaining a higher grade rather than Fail is multiplied 0.549 times controlling for all other variables (significant at p < 0.001). Hence, for every additional tutorial attended, the likelihood of obtaining a higher grade improves (nearly doubles).

Our results indicate that the level of learning management system engagement (p = 0.269) and discussion forum (Ed) engagement (p = 0.168) do not have any significant role in explaining the final result.

Subsequently, we added Home.Country as an additional variable in model 2 (Table 2). All the variables included in model 1 have shown similar patterns as model 2. The coefficient of Home.Country is statistically significant (p < 0.001). It indicates that domestic students have a significantly higher chance of obtaining higher grades compared to international students.

To capture the differential effect of explanatory variables based on home country (domestic vs. international), in model 3 (Table 3) we include all the explanatory variables from model 2 along with interaction terms between each of the explanatory variables and Home.Country. We found that six interaction terms are statistically significant: Tutor qualification (p < 0.001), Tutor gender (p = 0.035), Age of the student (p = 0.031), Number of tutorials missed (p = 0.014), Entry score (p < 0.001), and Number of degree (p = 0.034).

Table 3. Contingent effect.

	Model 3
	Beta	S.E.^a	p-value	2.5% CI^b	97.5% CI	OR^c
Tutor.PhD(PhD)	0.849	0.138	<0.001	0.579	1.120	2.338
Tutor.Exp	−0.008	0.008	0.360	−0.024	0.009	0.992
Tutor.Gender(male)	−0.484	0.170	0.004	−0.816	−0.151	0.617
Student.Age	−0.073	0.060	0.219	−0.190	0.043	0.929
Student.Gender(male)	0.002	0.163	0.992	−0.318	0.321	1.002
Tutorial.Missed	0.137	0.013	<0.001	0.112	0.161	1.146
Entry.Score	0.354	0.139	0.011	0.081	0.627	1.425
Degree.No(Dual-Degree)	−0.742	0.086	<0.001	−0.911	−0.573	0.476
Page.Views	0.000	0.000	0.060	0.000	0.001	1.000
Ed.Participations	−0.025	0.015	0.096	−0.053	0.004	0.976
Home.Country(International)	5.945	0.001	<0.001	5.943	5.948	382.014
Tutor.PhD(PhD)* Home.Country(International)	−0.548	0.104	<0.001	−0.752	−0.344	0.578
Tutor.Exp* Home.Country(International)	0.020	0.014	0.153	−0.008	0.048	1.021
Tutor.Gender(male)* Home.Country(International)	0.556	0.263	0.035	0.040	1.072	1.744
Student.Age* Home.Country(International)	−0.198	0.092	0.031	−0.379	−0.018	0.820
Student.Gender(male)* Home.Country(International)	−0.367	0.267	0.170	−0.891	0.157	0.693
Tutorial.Missed* Home.Country(International)	−0.046	0.019	0.014	−0.083	−0.009	0.955
Entry.Score* Home.Country(International)	0.605	0.084	<0.001	0.439	0.770	1.831
Degree.No(Dual-Degree)* Home.Country(International)	0.240	0.113	0.034	0.018	0.462	1.271
Page.Views* Home.Country(International)	0.000	0.000	0.999	−0.001	0.001	1.000
Ed.Participations* Home.Country(International)	0.000	0.023	0.983	−0.045	0.046	1.000
AIC	1853.978

^a Standard error.

^b Confidence interval.

^c Odds ratio.

Subsequently, we draw six effect graphs for all the statistically significant interaction terms. Figure 1 indicates that high-performing domestic students benefitted from the tutor with a PhD whereas students who secured a Pass score did not benefit from tutors with a PhD. A similar negative but weaker trend is observed for international students who received Pass grade. Figure 2 captures that the probability of achieving a Distinction is much higher for domestic students when they are tutored by a female compared to a male. The pattern is the opposite in the case of the probability of achieving a Pass when domestic students are tutored by a female. We observe no such effect (except a minor better effect from a female tutor compared to a male tutor in the Pass category) of a tutor’s gender in the case of international students on the probability of achieving any specific grade. Figure 3 shows that though the effect is relatively less prominent, increasing age reduces the probability of achieving either a Distinction or a Credit for domestic students. However, increasing age increases the probability of getting a Pass among domestic students. The increasing age effect is extremely prominent in the case of international students with Credit (negative) and failed international students (positive). We find an interesting U-shaped effect of age among international students in the Pass category. Figure 4 indicates that entry score has a positive trajectory on the probability of achieving a Distinction or a Credit for domestic students. However, the pattern is negative for domestic students when they have secured only a Pass as their outcome. The similar positive and negative trajectory of entry score in Credit and Pass category respectively for international students is visible. In Figure 5, we observe that the effect of dual degree has a more drastic effect, e.g., a positive trajectory in the Credit category and a negative trajectory in the Pass category on international students compared to domestic students. Finally, Figure 6 indicates the number of tutorials missed has a detrimental effect on the students securing Distinction or Credit. Interestingly students securing Pass grade is benefitted by attending an optimal number of tutorials (the inverted U-shaped pattern).

Figure 1. Interaction between Tutor.PhD and Home.Country – high-performing students benefitted from a tutor with a PhD.

Figure 2. Interaction between Tutor.Gender and Home.Country – Tutor’s gender influences student’s performance

Figure 3. Interaction between Student.Age and Home.Country – Student’s age influences domestic and international student’s success differently

Figure 4. Interaction between Entry.Score and Home.Country – Higher entry scores explain student’s performance.

Figure 5. Interaction between Degree.No and Home.Country – Dual degree helps students to secure better performance.

Figure 6. Interaction between Tutorial.Missed and Home.Country – Tutorial missed has a complex effect on student’s performance.

Finally, whilst we found that engagement with the LMS and the online discussion forums did not have any significant effect on grades, we note that this relationship is complex and requires further investigation since we know that such engagement needs to be purposively designed and communicated (Vallis et al., 2022).

Discussion

Through empirical exploration we sought to understand the influence of non-cognitive factors on the probability of achieving higher grades in a first-year undergraduate compulsory management unit, using ordinal logistic regression to analyse the performance of 990 students. As internationalisation of the student body grows and universities across the world increasingly rely on these income sources, we visually analyse the differential effects of the key success factors between domestic and international students with the help of multiple split sample graphs.

Post-hoc linking between theories and empirical constructs

Two of the most prominent theories that underpin student learning are self-regulation theory, whereby learners focus on goal-directed actions, and self-determination theory, which focuses on how learners are motivated by internal and external factors (Geldhof et al., 2017). These theories have strong correlations with four of the variables used in our study: age of student, entry score, number of concurrent degrees studied, and number of tutorials missed.

Closely linked to these theories are motivational systems theory and academic achievement theory. In the former, there is a close association between personal goals, belief in one’s own capability, provision of support, and emotional processes (Ford, 1992). Walberg’s theory of academic achievement proposes that educational outcomes are influenced by two psychological variables, students’ characteristics, and their immediate environment (Fraser et al., 1987). We associated these with the variables in our study: student age, entry score and number of concurrent degrees that are being studied, tutor experience, and tutor qualification.

Self-regulation studies have taken the teacher perspective such that those who provide high interaction, supportive feedback, and clear goals impact student learning. Motivational systems theory further explains extrinsic motivators that can often lead to surface-level learning whereas intrinsic motivators lead to deeper, longer-lasting understanding. Therefore, the teacher’s experience and ability can greatly influence the learning environment through the feedback mechanisms and support provided.

In a similar vein, as we investigate the literature on academic achievement theory, we know that variables influencing achievement include prior achievements, willingness to persevere, and the quantity as well as quality of instruction. This focus on the quantity of study having a limiting effect has been defined by Bruinsma and Jansen (2005) yet little attention has surfaced on the details, be it studying dual degrees (as is growing common in higher education) or the impact of other demands on students’ time (Johnson & Walberg, 1989).

We have revisited these theories with our empirical results to understand the current and ever-changing landscape with reference to differentials between domestic and international students.

Emerging theoretical constructs

Through this process of mapping our empirical constructs and findings to the literature, we found a set of three theoretical constructs emerged – tutor competence, student competence, and student commitment.

Tutor competence

A tutor with a PhD in the topic they are teaching has a depth of knowledge and is able to answer a range of student questions. We found that student achievement is positively correlated with tutors’ qualifications. However, there are conflicting findings in the literature in this regard. For example, McKay (2016) found no effect on student improvement (though their sample size of 5 tutors was small), whereas others have found that student improvement results from a mix of tutor qualification, facilitation skills, and popularity. Future research may wish to cross reference this factor and should include an investigation into the contextual background of how the PhD prepares doctoral students for answering challenging and unknown questions in the classroom once employed as a tutor (Mantai, 2019).

We found that the role of the tutor’s qualification such as a PhD has a positive effect on the domestic students who receive a Distinction grade (Figure 1). A similar pattern was also observed for international students who receive a Credit grade. Therefore, high-performing students benefit more from the presence of a tutor with a PhD. However, we observed an interesting negative trend in the case of both domestic and international students who received a Pass grade. One possible explanation is that the high-degree experience of tutors’ is mismatched with the average students’ aspiration and plays out as detrimental for these students.

The next finding of interest is that the effect of female tutors compared to their male counterparts with respect to students achieving higher grades is statistically negligible. Research has shown that there is limited relationship between student ratings of teachers/teaching and student learning (Basow et al., 2013). This research has also found that women faculty are often rated higher than men on questions relating to faculty–student interactions. This may be one cause of why we found higher achievement in domestic students who had women tutors (over men) in their tutorial, and this is an area for further investigation given the weak statistically significant difference. The role of tutor gender indicates a small significance in the case of domestic students who have received Distinction or Pass grades whereas a similar pattern is missing for international students (Figure 2). One possible explanation is that the high-performing domestic students may be more influenced by a female tutor whereas male tutors may be better suited to help average students to pass the unit. The absence of a similar effect for international students indicates that they may not be influenced by the gender of the tutor.

Student competence

Whilst the spread of ages in our study was not wide (98.79% of students are in the 18–23 years age group), we did find that older students outperformed their younger peers, which aligns with the findings of Bornschlegl et al. (2020) that help-seeking behaviours are positively correlated with age. Figure 3 indicates that the older domestic students have a downward trajectory to achieve a Distinction or Credit whereas the pattern is opposite for those domestic students who received a Pass grade. One possible inference is that higher age is associated with greater level of managerial ability, and it helps domestic students to manage a demanding university schedule and eventually pass the unit. However, a similar ability is detrimental for achieving higher grades such as Distinction or Credit. We observe some intriguing patterns in the data for international students. The probability of failing the unit is higher with the older international students whereas the probability of getting a Credit grade is lower with the older international students. The pattern takes an inverted U-shape for international students in the case of their probability of getting a Pass in the unit. Therefore, the key takeaway is that there is a specific age zone where the probability of international students getting a Pass result is highest. It provides key strategic information such as background context to support special consideration applications or appeals as well as the need for possible additional support to transition to a university educational environment. This could then support all age groups to succeed in an entry-level management unit.

Positive effect of entry score on students’ greater achievement in the final grade is intuitive as it indicates superior capability and preparation before entering the university system. The upward pattern of the effect of entry score on the probability of achieving Distinction or Credit for domestic students emphasizes the importance of prior academic excellence to receive appropriate momentum (Figure 4). The possible explanation for the downward trajectory in securing a Pass grade is that domestic students with higher entry scores rarely achieve a Pass grade. We observe a similar pattern for international students in the case of a Credit (upward) or a Pass (downward) grade. Interestingly, higher entry scores did not greatly influence the probability of achieving a Distinction for international students. The possible inference is that prior academic performance has minimal explanatory power for high-performing international students as they need to adjust to a new learning environment in a foreign educational context.

The final variable of interest in the student competence category is that students undertaking dual degrees achieve higher grades than those studying only one degree as may be expected since admission requirements would be higher for these students. This is in line with other studies that have compared course credits with GPAs finding greater retention even after controlling for academic ability, prior academic success, on-campus employment hours, and other background characteristics (Szafran, 2002). Considering that the effect of a dual degree is more prominent for international students (Figure 5), we can infer that the extra load of studying a dual degree motivates international students to increase the probability of achieving a Credit and the pattern is opposite when students have achieved only a Pass. We can also infer that the probability of achieving a Pass grade is higher among international students when they are studying only one degree as it is easier to manage the study load in this case. Since research into study time and academic performance has produced conflicting and inconsistent findings (Masui et al., 2014), we suggest further interrogation into the relationship between the number of concurrent courses, motivation, and academic achievement.

Student commitment

There is no surprise that the effect of the tutorial missed variable is extremely high on achieving a higher grade compared to failing the unit (McKay, 2016). The pattern in Figure 6 shows us that irrespective of domestic or international students, the more tutorials that are missed increases the probability of receiving a Fail grade. Similarly, increasing the number of tutorials missed reduces the probability of getting a Credit grade for both domestic and international students. What is most intriguing is that the pattern is an inverted U-shape for those students who receive a Pass grade. This indicates that there is an optimal number of tutorials that ensures the highest probability of achieving just a Pass grade for both cohorts.

Theoretical model

Finally, we offer a connection between our chosen empirical constructs, the literature, the emerging theoretical constructs, and a proposed theoretical model (Figure 7). This model is based on a causal relationship in that all three constructs need to be present for a student to exhibit improved academic performance. In addition, we indicate that the status of a student, whether domestic or international, can have a moderating effect on student achievement.

Figure 7. Connection between empirical constructs, theories, emerging theoretical constructs, and proposed theoretical model.

Limitations

Our study has some limitations due to certain unique characteristics of the institution and higher education system under study. First, this study is based in a leading Australian research-intensive university. It has a high proportion of non-residential students and different results may be found in a residential university, as this will likely introduce other factors that may influence student success (McKenzie & Schweitzer, 2001). Second, the sample was taken from one unit of study which may limit the generalizability of the findings and should therefore be replicated in other units or in similar units but at different institutions to strengthen the conclusions. Third, we use a narrow definition of student success, e.g., grades obtained, and we do not take into consideration knowledge gained and future application (Szafran, 2002).

Future research and conclusions

We identified a number of areas for future research including how a PhD prepares doctoral students for answering challenging and unknown questions in the classroom, why women tutors produce higher achievement (over men) for students in their first-year undergraduate management course, and the relationship between the number of concurrent degree programs, motivation, and academic achievement. Also, it would be beneficial to look at a matrix of effects such as tutors who are men/women, with/without a PhD.

In conclusion, this study has shown that a number of factors can influence student academic success in a first-semester compulsory undergraduate management unit. Considering that performance in the first unit of study lays the foundations and provides a certain momentum for a student’s trajectory through their university journey, our findings educate multiple stakeholders, including business school management and faculty in charge of the unit, about these critical success factors. In addition, we also provide a nuanced understanding of the effect of those determinant factors in the context of domestic and international students.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 https://go8.edu.au/.

2 In Australia, semester 2 predominantly caters to international students.