The Impact of an Immersive Block Model on International Postgraduate Student Success and Satisfaction: An Australian Case Study

Abstract

International postgraduate students enrich higher education institutions and host societies, contributing economically, socially, and culturally. However, much less is known about how to improve their academic outcomes compared with their undergraduate counterparts. This study explores the impact of a non-traditional form of learning, a six-week immersive block model underpinned by guided, active learning pedagogy, on the academic success, satisfaction, and experiences of international postgraduate students at an Australian university. A convergent mix-methods design was used. Chi square tests and generalised estimating equations were used to compare the students’ success rates (N = 14,340) and unit satisfaction (N = 4903) in traditional semester and immersive block learning over five years. Qualitative insights were gathered via student focus groups (N = 9). Significant positive changes in success were observed after controlling for gender, age, discipline, and home region, with particularly strong positive effects for male and information technology students. Despite some challenges with depth of learning and placement organisation, focus group participants valued the clear timelines and flexible delivery, reporting that this supported effective time management and study-work–life-balance. Immersive block learning appears to be an effective strategy for transforming the experiences and outcomes of international postgraduate students in higher education.

1. Introduction

Over the past fifty years, many countries have implemented policies and initiatives to increase and diversify higher education (HE) participation. These have contributed to larger numbers of international students studying in countries such as Australia, the United Kingdom (UK), and the United States of America (USA) (Ammigan & Jones, 2018). International students are pivotal to HE, contributing economically, enriching campus life, and bringing global perspectives and cultural diversity (Moogan, 2020; Rovagnati et al., 2022). However, few studies have addressed the unique perspective of international postgraduates as they transition to new, advanced learning environments. They are, as Bodine Al-Sharif et al. (2025) note, “missing voices in higher education” (p. 13). This is somewhat surprising, given that international postgraduate students represent a significant cohort for HE. In the full year ending 2023, there were 524,514 students from overseas countries studying ‘onshore’ in Australia, representing a 16.9% increase from 2022 (Department of Education, 2024). Almost half (47%) were postgraduate students. Similarly, data from the U.S. indicate that around 40% of international students—over 380,000 students—were enrolled in graduate degrees over the 2021/2022 academic year (Bodine Al-Sharif et al., 2025). As Moogan (2020) argues:

PG students are a valuable sector within HE and are often neglected in terms of both the research being performed and the differentiation of their expectations in comparison to UG students. In particular, international PG students bring diversity and other benefits to the classroom so they should be listened to (p. 94).

Given the dearth of empirical research in this space, it remains important to seek further insight, drawing from the diverse experiences and learning styles of our international postgraduate students, who make such a significant contribution to HE. In this paper, we seek to explore whether academic outcomes improve for this cohort through the implementation of a non-traditional form of learning: an immersive block model. Also known as ‘block’, ‘intensive’, or ‘accelerated’ models (Goode et al., 2023; Turner et al., 2021), immersive blocks are typically structured over 4–6 week teaching periods and a full-time load of 1–2 units at a time, departing from traditional semester models in HE.

This study investigated whether immersive block learning supports better academic outcomes compared with traditional semester learning for international postgraduates studying on a student visa at a host institution in Australia. The research used a mixed-methods approach to explore the students’ academic success, satisfaction, and perspectives on their learning experiences. We acknowledge that definitions of student success can go beyond good grades and unit completion to include both social and academic engagement (Naylor, 2017) and the ability to persevere and have self-belief (Tinto, 2017). Nonetheless, ‘student success’ is increasingly used as the established term for key student outcome metrics and is the lens through which student and institutional performance is often viewed and discussed (Lowe, 2023). In this study, we focused on pass rates as a measure for student success. We also acknowledge that satisfaction is a complex construct, influenced by various factors such as teaching quality, support services, and the overall learning environment (Hornstein, 2017). Despite these complexities, a study across nearly 100 HE institutions in Australia, the UK, and the USA found that satisfaction with the learning experience had the most significant impact on the overall experience of international students (Ammigan & Jones, 2018). Therefore, we used the results from unit feedback surveys (UFSs) as a measure of student satisfaction, coupled with qualitative data from student focus groups.

Three key research questions were examined in relation to postgraduate international student visa holders enrolled onshore at the university from 2019 to 2024:

• How do success rates in a traditional model compare with success rates in an immersive block model?
• How does unit satisfaction in a traditional model compare with unit satisfaction in an immersive block model?
• How do students experience immersive block model learning?

1.1. International Student Success in HE

During their studies, both domestic and international students face academic, financial and relationship challenges. However, international students for whom English is an additional language have been found to face challenges not only in language proficiency, but also in cultural adjustment, social integration, and experiences of bias—all of which impact their overall adjustment and success (Andrade, 2006; Arkoudis et al., 2019). Such students need to develop confidence and competence not only in the new language and culture of the country, but the culture of the academy including complex academic and digital literacies (Roche, 2017). Outside the academy, they can confront culture shock (Bai & Wang, 2022) as well as employment, financial, and housing precarity (Arkoudis et al., 2019). The academy-based challenges of adapting to language and digital proficiency expectations have also been found to hold true for students who decide to undertake English-medium instruction (EMI) in their own country (Ahmed & Roche, 2021; Roche et al., 2016). In either setting, where the students’ expectations about educational practice differ to those of the host institution, lower achievement, dissatisfaction, and attrition can result (Roche et al., 2015; Sinha et al., 2018).

HE providers have a responsibility to provide quality support and learning environments for postgraduate international students and ensure that they meet both the education and employability outcomes (Cook et al., 2021). At the same time, international students can confront a unique and often complex HE environment (Moogan, 2020). For postgraduate internationals from majority world backgrounds, this has the potential to be more pronounced when transitioning to institutional practices and academic literacies that differ from their undergraduate education at home (Bodine Al-Sharif et al., 2025; Evans et al., 2018; Rovagnati et al., 2022).

There is a small body of literature focused on international postgraduates, exploring topics such as employability (Cook et al., 2021), decision-making processes and information needs (Moogan, 2020), course choice (McNicholas & Marcella, 2022), and assessment feedback cultures and literacies (Rovagnati et al., 2022). Work-related stress also impacts international postgraduates, and they can struggle to balance the competing demands of the professional, academic, and personal aspects of their lives (Abdul Aziz et al., 2025). Institutional initiatives and supports such as tailored workshops, policies that provide flexibility, and clear academic guidance can alleviate some of these challenges (Abdul Aziz et al., 2025). However, there is very little research on how holistic curriculum interventions can bolster the success and satisfaction of international postgraduate students at scale. Here, we investigate immersive block learning as a curriculum innovation that can potentially make a significant positive difference to international postgraduate student outcomes.

1.2. Immersive Block Models in HE

A four-week block model was first used at Colorado College in the U.S. in 1970 and resulted in notable benefits in student retention and satisfaction (Helfand, 2013). Building on these successes, more recent immersive block models (e.g., Buck & Tyrrell, 2022; McCluskey et al., 2019; Roche et al., 2024) have drawn on cognitive load theory (Sweller, 1988) as an underpinning theoretical framework for successful HE learning. By reducing the number of concurrent subjects in a full-time load, immersive block models aim to minimise interference effects caused by multiple simultaneous topics and assessments, and consequently reduce cognitive overload and enhance student focus (Goode et al., 2023; Richmond et al., 2015). This is, in turn, designed to enable more ‘immersive’ learning over manageable time frames, supporting enhanced learning retention (Roche et al., 2024) and a greater capacity to successfully balance study with work and family commitments (Turner et al., 2021; Wilson et al., 2025b).

Beyond scheduling, immersive block models also necessitate a fundamental transformation of an institution’s pedagogical framework (McCluskey et al., 2019; Roche et al., 2024; Wilson et al., 2025a, 2025c). At the host institution, Southern Cross University, Australia, the block calendar was accompanied by a deliberate shift towards active learning pedagogies, driven through policy reform (Roche et al., 2024; Wilson et al., 2025a). Active learning pedagogies, grounded in the theoretical perspectives of Dewey (1938) and Kolb (1984), have been recognised as highly effective pedagogies for fostering greater student engagement, knowledge acquisition, and academic success in HE compared with didactic knowledge transfer approaches (Freeman et al., 2014; Turner et al., 2021; Wilson et al., 2025b). Active learning facilitates a deeper form of learning (application rather than memorisation; see Marton & Saljo 1976). It appears especially valued in contemporary online and blended contexts, where communication and collaboration can play key roles in fostering student engagement (Ramos-Pla et al., 2022).

Some researchers have raised concerns over whether immersive block learning delivers equivalent depth and rigour to traditional semester formats (Dixon & O’Gorman, 2020; Lutes & Davies, 2018). There are also indications that an accelerated pace for knowledge construction can become stressful when the curriculum is not redesigned thoroughly (Jones & de Main, 2025) or when unanticipated circumstances arise in the students’ lives (Buck & Tyrrell, 2022; Goode et al., 2024b). However, other studies conclude that students consider the intellectual demand of shorter and longer models to be similar (Lee & Horsfall, 2010; Richmond et al., 2015), and that learners perform just as well after studying a block unit as they do after studying an equivalent unit in a semester (A. M. Austin & Gustafson, 2006). There are also some indications that long-term knowledge retention (Faught et al., 2016) and graduate outcomes (Eames et al., 2018) are equivalent.

Overall, the international uptake of immersive block models can be understood both as an institutional effort to increase the academic success of diverse student cohorts and as a response to student preferences for more flexible and engaging student learning (Helfand, 2013; Wilson et al., 2025b). Despite growing interest in immersive block models in HE, and an understanding that they are suited to improving the outcomes of diverse student cohorts, including pathway (also known as enabling, foundation or access) students (Goode et al., 2024d), first-year undergraduates (Buck & Tyrrell, 2022), undergraduate international students (Goode et al., 2024c), and underrecognised or underrepresented students (Roche et al., 2025), little is known about the experiences of postgraduate international students in these non-traditional forms of learning.

2. The Southern Cross Model: A Case Study

This paper explored the success rates and satisfaction of international postgraduate students in an immersive block model known as the Southern Cross Model (SCM), implemented at a regional, public HE institution in Australia from 2021 to 2023. The University has approximately 19,000 students in undergraduate, postgraduate, and pathway courses in the discipline areas of health, science, engineering, laws, business, information technology, education, Indigenous knowledge and the arts; 32% of its enrolments are international students (Southern Cross University, 2025). In contrast to the common one-unit-at-a-time structure of most block models, in the SCM, full-time students study up to two units simultaneously over six-week terms, typically completing eight units a year.

This study draws on a theory of change that connects the structural and pedagogical shifts inherent in the immersive block model to widely-used student outcome metrics, particularly academic success and satisfaction (unit feedback scores). Central to this theory is the premise that the block model reduces cognitive load by allowing students to focus on fewer units at a time, thereby enhancing comprehension and retention (Sweller, 1988). Importantly, this model also facilitates the integration of active learning—such as collaborative and interactive classroom activities and responsive online learning modules—which is known to promote deeper engagement and improved academic performance (Freeman et al., 2014). As such, the study assumes that the immersive block model is not merely a scheduling change, but a pedagogical intervention with the potential to influence both cognitive and affective dimensions of the student experience.

The SCM draws on a specific form of active learning pedagogy: guided active learning. Guided active learning is an instructional approach that combines active learner engagement with structured guidance from a teacher in class or through instructional materials (Roche et al., 2022). This pedagogy acknowledges that effective active learning is facilitated through carefully sequenced structured tasks, distinguishing it from pure discovery learning where students independently seek knowledge and skills (Kirschner et al., 2006; Mayer, 2004). These structured tasks move beyond the passive exposure often used in HE learning (i.e., lecturers and readings) to requiring the retrieval and application of knowledge and skills in class and online (Karpicke & Roediger, 2008). It leverages principles of human cognitive architecture to optimise learning efficiency by reducing cognitive load, providing explicit direction, and scaffolding the learning process (Kirschner et al., 2006). In practice, the focused, guided, active curriculum is embedded in three main forms of learning in each subject:

• Self-access online modules that are media-rich, interactive and responsive.
• Scheduled classes that are guided and interactive, involving activities such as discussion, problem-based scenarios, and simulations.
• Assessments that are authentic and scaffolded, with no more than three assessments per unit (see Roche et al., 2024 for a more detailed description of the pedagogy of the SCM, Goode et al., 2022 for an example of its application, and Wilson et al., 2025a for an outline of how assessment was reformed for block delivery).

As such, the immersive block model approach used here accords with current research on learning (see the four pillars of learning articulated by Dehaene (2020): attention (here focus), active engagement, error feedback, and consolidation).

3. Materials and Methods

The study employed a convergent mixed-methods design (Creswell & Plano Clark, 2011) whereby quantitative and qualitative data were collected simultaneously and insights from both sets of data informed the overall conclusions made from the study. In the present inquiry, the two strands of data also addressed specific research questions: quantitative institutional data primarily addressed research questions 1–2; and qualitative data from student focus groups primarily addressed research question 3. Together, both sets of data were intended to offer nuanced insights into the impacts of immersive block learning on international postgraduate students in HE. The study was approved by the University’s human research ethics committee (approval 2022/054), and all protocols concerning voluntary participation, secure data management, and privacy and confidentiality were followed.

3.1. Quantitative Data Collection and Preparation

Quantitative institutional data were obtained from the University’s Office of Business, Intelligence, and Quality. These included enrolment and academic records from 2019 through to 2024 and responses to the University’s standardised Unit Feedback Survey (UFS). Commonly known as a Student Evaluation of Teaching (SET) survey in the literature (Goode et al., 2024a), the UFS is administered in the final weeks of a study period (before the release of students’ grades) and includes 5-point Likert scale items on perceptions of the unit and teaching quality.

These datasets were merged in Microsoft Excel and filtered to only include international postgraduate coursework students studying onshore under a student visa. This ensured some consistency in relation to study mode and load, as these students are subject to visa requirements that they study full-time and predominantly on-campus. Domestic, undergraduate, and non-coursework (i.e., Doctoral or Master’s thesis) students were excluded. Data from 2020 were removed to account for atypical study arrangements during the COVID-19 pandemic. Aligning with how ‘student success’ is reported in Australian HE (Department of Education, 2025), only observations with an enrolment status of Completed, Withdrawn, or Failed were retained. These were recoded into a binary outcome: ‘pass’ (Completed) and ‘not pass’ (Withdrawn or Failed). For the satisfaction analysis, responses to the item: ‘Overall, I am satisfied with this unit’ were similarly recoded into binary outcomes of ‘agree’ (5 strongly agree, or 4 agree) or ‘not agree’ (3 average, 2 disagree, or 1 strongly disagree).

The data were uploaded to SPSS (30.0) for further cleaning, transformation, and analysis. Students’ disciplines of study were classified according to the Australian Standard Classification of Education (ASCED; Australian Bureau of Statistics, 2001), and students’ home regions were classified according to the Standard Australian Classification of Countries (SACC; Australian Bureau of Statistics, 2016). Age was approximated for each record using the students’ year of birth and year of enrolment.

To compare outcomes in the SCM with outcomes in the traditional model, enrolments were flagged according to delivery model:

• T0: Baseline traditional semester delivery, prior to the introduction of the SCM (2019);
• T1: Traditional semester delivery during the SCM’s progressive implementation (2021–2022);
• SCM: Units delivered in the six-week SCM (2021–2024).

The inclusion of T0 was considered important given that this represents the ‘business as usual’ baseline model that the university had adopted over many years, with persistently low student success and retention. T1 is a more contemporaneous traditional semester offering; however, it may also be influenced by the policy and practice shift towards active, blended learning and authentic assessment that was implemented across the institution from 2021 onwards as the SCM was rolled out (Roche et al., 2024; Wilson et al., 2025a). Together, both comparisons allow for strengthened insights into the impact of the SCM compared with a longer semester format. Only units offered in all three models were retained for analysis.

Home regions and disciplines with fewer than 50 observations in any delivery model were omitted to support more reliable data analysis and interpretation (for example, there were only 38 enrolments in T1 from Oceania, Europe, and North America combined). Final variable categories were:

• Home region: Africa and Middle East; North-East Asia (e.g., China, Japan, Korea); South America; Southeast Asia (e.g., Thailand, Indonesia, Malaysia); and Southern and Central Asia (e.g., India, Sri Lanka, Nepal)
• Disciplines: Information Technology; Engineering and Related Technologies; and Management and Commerce
• Age range: 20–24 years; 25–34 years; and 35+ years
• Gender: Female; Male.

3.2. Quantitative Data Analysis

Analysis proceeded in two phases. Following Field’s (2018) recommendation for non-normal data, Pearson’s chi-square tests were conducted in phase 1 to compare success and unit satisfaction between T0 and SCM, and between T1 and SCM.

In phase 2, binomial logistic generalised estimating equation (GEE) models (Liang & Zeger, 1986) were fit. Although both GEE and generalised linear mixed-effect models (GLMM) can account for within-cluster correlation (here, enrolment-level observations grouped by student), GEE was used due to its robustness to non-normal distributions and correlation structure misspecification (P. C. Austin et al., 2024; Koper & Manseau, 2009). Estimating population-averaged effects also aligns with the aim of the study to investigate the impact of a new delivery model on populations of interest. Models used an exchangeable working correlation structure and robust standard errors (per P. C. Austin et al., 2024). No multicollinearity was detected, with a variance inflation factor (VIF) < 2.0. Multiple models were tested using quasi-likelihood under the independence model criterion (QIC) to assess model fit (per Pan, 2001). A full list of the models tested and QIC results is provided in

Table A1. Generalised estimating equation models tested for the analysis of student success and unit satisfaction.

Success (S) Models			Unit Satisfaction (US) Models
Model	Predictors	QIC	Model	Predictors	QIC
MS0	Null	17,571.2	MUS0	Null	4434.4
MS1	Delivery model	16,167.5	MUS1	Delivery model	4361.3
MS2	As above, plus: Home region	15,969.9	MUS2	As above, plus: Discipline	4347.4
MS3	As above, plus: Gender	15,682.7	MUS3	As above, plus: Age	4349.0
MS4	As above, plus: Age	15,618.1	MUS4	As above, plus: Home region	4350.1
MS5	As above, plus: Discipline	15,585.0	MUS5	As above, plus: Gender	4358.1
MS6	As above, plus: Delivery model * Home region	15,565.5	MUS6	As above, plus: Delivery model * Age	4354.1
MS7	As above, plus: Delivery model * Gender	15,464.2	MUS7	As above, plus: Delivery model * Home region	4363.4
MS8	As above, plus: Delivery model * Age	15,429.4	MUS8	As above, plus: Delivery model * Gender	4364.0
MS9	As above, plus: Delivery model * Discipline	15,369.8	MUS9	As above, plus delivery model * Discipline	4375.2

Notes. Final models used are in bold.

. The final model for success (MS9) included pass/not pass as the dependent variable, delivery model as the predictor of interest, discipline, home region, age, and gender as control variables, and interactions between the delivery model and each covariate. The final model was run twice to obtain comparisons of interest: once with T0 as the reference, and once with T1 as the reference.

For the satisfaction analysis, the data were filtered to include only records with UFS responses. Two discipline categories (Africa and Middle East, and South America) were collapsed into one due to small sample sizes (n = 27 and n = 6, respectively in T0). The procedures reported above were then repeated with overall unit satisfaction (agree/not agree) as the dependent variable. The final satisfaction model (MUS6) included the same covariates as the success model, plus an interaction between delivery model and age. Although the final model QIC was slightly higher than a model with fewer covariates (difference = 6.7; see Table A1), it was considered important to control for the same factors as the success model. However, adding interactions between the delivery model and home region, gender, and discipline were not found to improve the model fit and were not included.

3.3. Qualitative Data Collection and Analysis

Focus groups were conducted to gather the students’ qualitative perceptions of learning in the immersive block model. A convenience sampling approach was used (Battaglia, 2008), whereby the potential participant pool was not restricted according to particular characteristics, and instead, all international postgraduate students enrolled at the university while in Australia on a student visa were invited to participate. The fourth author distributed email invitations to all potential participants in mid-October 2024. This resulted in three focus groups, and one participant who elected to provide responses by email. There was a mix of participants in each group, with the first group comprising students from Accounting and Osteopathy, the second including students from Social Work, Business, and Nursing, and the third comprising students from Social Work and Information Technology.

The focus group protocol, developed by the first, second, and fourth authors, included open-ended questions about:

• Participants’ motivations for choosing the host institution;
• Initial expectations and perceptions of the SCM;
• Perceptions of how well they were able to balance study, work and life in the SCM;
• Perceptions and experiences of the focused, guided and active pedagogy of the SCM;
• How prepared they felt for their future career or future study.

The groups were facilitated online by the lead author, each lasting one hour, and were recorded and transcribed by Zoom. To protect the students’ privacy, names were replaced with pseudonyms in the transcripts. These were cleaned for accuracy and then uploaded to NVivo (14). Braun and Clarke’s (2006) seminal approach to thematic analysis was used to analyse the data, whereby transcripts were read iteratively: initial codes relating to the students’ perceptions and study experiences were generated inductively from the data, and codes were then developed into themes or salient ‘patterns of meaning’ (p. 86). The generation of themes was aided by a codebook that captured sentiments related to the students’ experiences in the model as well as the iterative development of a thematic map (Figure 1; see also Dawadi, 2020) to capture clusters of related ideas—themes and sub-themes—and the interconnections between them. In most cases, the sub-themes represented codes that were deemed to be most strongly indicative of the main theme (e.g., the theme of ‘learning’ included the following codes, which became sub-themes: ‘self-directed learning’, ‘recorded classes’, ‘flexibility’, ‘interactive online modules’, ‘missed learning’, and ‘placements’). As is common during thematic analysis, the process of generating these codes and aligning them into themes and sub-themes was recursive and informed by subjective impressions of salience and of the participants’ intended meanings (Braun & Clarke, 2006). Hence, themes also encapsulated several codes that were collapsed into others during the analysis and/or codes that are not reported on here. For example, the codes ‘active learning’ and ‘accountability’ were bundled into the sub-theme of ‘self-directed learning’ in the final analysis. Other codes were discarded for the final manuscript due to their weak presence in the sample or their weaker alignment with the research questions compared with other codes. Examples under the theme of ‘learning’ include the notion of ‘pre-planning’ (making schedules for each unit at the start of the term) and ‘prioritising’ (deciding which aspects of the curriculum to focus on).

As the themes and sub-themes were formed, transcript extracts were selected to exemplify these, forming the overview presented in the results below. All authors concurred with the final themes, sub-themes, and indicative quotes.

4. Results

4.1. Student Success

Success rate data are presented in

Table 1. Student success rates in traditional (T) and immersive block (SCM) model learning.

		T0		T1		SCM		T0 to SCM	T1 to SCM
Cohort		n (%)	Success Rate (%)	n (%)	Success Rate (%)	n (%)	Success Rate (%)	Change X2(1), p	Change X2(1), p
All		8381	58.7	1744	73.1	4215	90.6	31.9 ***	17.5 ***
		(100.0%)		(100.0%)		(100.0%)		1345.9, <0.001	304.4, <0.001
Discipline
	Information Technology	897	46.5	516	78.7	1522	92.9	46.4 ***	14.2 ***
		(10.7%)		(29.6%)		(36.1%)		660.9, <0.001	81.6, <0.001
	Engineering	1572	56.1	218	75.7	854	94.5	38.4 ***	18.8 ***
		(18.8%)		(12.5%)		(20.3%)		385.6, <0.001	72.6, <0.001
	Management and Commerce	5912	61.2	1010	69.7	1839	86.9	25.7 ***	17.2 ***
		(70.5%)		(57.9%)		(43.6%)		420.9, <0.001	124.2, <0.001
Home region
	Africa and Middle East	87	83.9	77	83.1	157	89.8	5.9	6.7
		(1.0%)		(4.4%)		(3.7%)		1.8, 0.176	2.1, 0.144
	South America	59	86.4	108	90.7	342	98.0	11.6 ***	7.3 **
		(0.7%)		(6.2%)		(8.1%)		18.5, <0.001	11.7, 0.001
	North-East Asia	457	74.4	154	74.7	398	90.5	16.1 ***	15.8 ***
		(5.5%)		(8.8%)		(9.4%)		36.9, <0.001	23.0, <0.001
	Southeast Asia	345	82.6	62	69.4	297	92.9	10.3 ***	23.5 ***
		(4.1%)		(3.6%)		(7.0%)		15.4, <0.001	28.8, <0.001
	Southern and Central Asia	7433	56.1	1343	71.1	3021	89.6	33.5 ***	18.5 ***
		(88.7%)		(77.0%)		(71.7%)		1072.7, <0.001	235.6, <0.001
Gender
	Female	3141	73.4	716	74.3	1692	91.3	17.9 ***	17.0 ***
		(37.5%)		(41.1%)		(40.1%)		218.1, <0.001	122.8, <0.001
	Male	5240	49.8	1028	72.3	2523	90.1	40.3 ***	17.8 ***
		(62.5%)		(58.9%)		(59.9%)		1184.8, <0.001	182.2, <0.001
Age
	20–24	3175	49.9	247	72.9	773	88.6	38.7 ***	15.7 ***
		(37.9%)		(14.2%)		(18.3%)		381.4, <0.001	36.0, <0.001
	25–34	4956	63.2	1344	73.0	3133	91.5	28.3 ***	18.5 ***
		(59.1%)		(77.1%)		(74.3%)		801.3, <0.001	267.1, <0.001
	35+	250	79.6	153	74.5	309	86.4	6.8 *	11.9 **
		(3.0%)		(8.8%)		(7.3%)		4.6, 0.032	10.0, 0.002

Notes. T0 is the baseline traditional model delivery (2019). T1 is the traditional model delivery during the immersive block rollout (2021–2022). SCM is the immersive block (Southern Cross Model) delivery (2021–2024). * p < 0.05; ** p < 0.01; *** p < 0.001.

(phase one) and

Table 2. Predictors of student success: T0 to SCM and T1 to SCM.

Predictor		OR	95% CI	p
Reference: T0
	SCM	2.31 *	1.11–4.82	0.025
	SCM*Southern and Central Asia	1.14	0.52–2.46	0.746
	SCM*Southeast Asia	0.67	0.23–1.96	0.468
	SCM*South America	3.30	0.59–18.56	0.175
	SCM*Africa and Middle East	0.40	0.11–1.52	0.180
	SCM*Male	2.15 ***	1.49–3.10	<0.001
	SCM*35+	0.23 ***	0.11–0.47	<0.001
	SCM*25–34	0.76	0.55–1.04	0.083
	SCM*Engineering	1.53	0.94–2.50	00.088
	SCM*Information Technology	2.59 ***	1.62–4.16	<0.001
Reference: T1
	SCM	2.44 *	1.07–5.55	0.033
	SCM*Southern and Central Asia	0.87	0.35–2.21	0.753
	SCM*Southeast Asia	0.98	0.32–2.98	0.973
	SCM*South America	1.72	0.40–7.29	0.463
	SCM*Africa and Middle East	0.36	0.08–1.63	0.185
	SCM*Male	0.97	0.63–1.48	0.877
	SCM*35+	0.49	0.20–1.21	0.122
	SCM*25–34	1.12	0.69–1.80	0.646
	SCM*Engineering	1.53	0.83–2.85	0.175
	SCM*Information Technology	1.03	0.61–1.72	0.918
Model QIC = 15369.8
Observations = 14,340
Subjects = 3122
Measurements per subject = 1–24
Working correlation matrix (exchangeable) = 0.295

Notes. T0 is the baseline traditional model delivery (2019). T1 is the traditional model delivery during the immersive block rollout (2021–2022). SCM is the immersive block (Southern Cross Model) delivery (2021–2024). * p < 0.05; *** p < 0.001.

(phase two). Notable demographic changes across the delivery models (see Table 1) included strong increases in the proportion of enrolments in Information Technology, from South America, and aged over 25. Conversely, the proportions of students enrolled in Management and Commerce, from Southern and Central Asia, and aged between 20 and 24 fell between T0 and SCM.

Table 1 shows that the success rates rose significantly in the SCM, increasing overall by 31.9% points compared with T0, and by 17.5% points compared with T1. Increases were strong and significant (14.2–46.4% points) for all disciplines. Students from Asian countries experienced strong gains (10.3–33.5% points); however, increases for students from South America were less pronounced, and changes for students from Africa and the Middle East were non-significant. Increases were higher for male students than female students, with the SCM closing a pronounced gap evident in the traditional model and bringing the success for males (90.1%) close to the success for females (91.3%). Success rates for older students (+35 years of age) appeared less affected by the SCM than for their younger counterparts.

The GEE results augment these findings, as shown in Table 2. For ease of interpretation in relation to the research questions, Table 2 only includes the results relating to the SCM as the predictor of interest (see

Table A2. Full outputs for the predictors of student success: T0 to SCM and T1 to SCM.

Predictor		OR	95% CI (Lower)	95% CI (Upper)	p
Reference: T0
	SCM	2.31	1.11	4.82	0.025
	T1	0.95	0.47	1.93	0.881
	Southern and Central Asia	0.49	0.34	0.70	<0.001
	Southeast Asia	1.36	0.79	2.36	0.268
	South America	0.91	0.28	2.92	0.875
	Africa and Middle East	1.83	0.84	3.97	0.129
	35+	2.48	1.51	4.09	<0.001
	25–34	1.48	1.29	1.69	<0.001
	Male	0.36	0.31	0.42	<0.001
	Engineering	1.21	0.99	1.47	0.062
	Information Technology	0.72	0.59	0.89	0.002
	SCM*Southern and Central Asia	1.14	0.52	2.46	0.746
	SCM*Southeast Asia	0.67	0.23	1.96	0.468
	SCM*South America	3.30	0.59	18.56	0.175
	SCM*Africa and Middle East	0.40	0.11	1.52	0.180
	SCM*Male	2.15	1.49	3.10	<0.001
	SCM*35+	0.23	0.11	0.47	<0.001
	SCM*25–34	0.76	0.55	1.04	0.083
	SCM*Engineering	1.53	0.94	2.50	0.088
	SCM*Information Technology	2.59	1.62	4.16	<0.001
	T1*Southern and Central Asia	1.31	0.67	2.56	0.427
	T1*Southeast Asia	0.69	0.27	1.76	0.433
	T1*South America	1.92	0.45	8.24	0.379
	T1*Africa and Middle East	1.12	0.37	3.43	0.842
	T1*Male	2.22	1.58	3.13	<0.001
	T1*35+	0.47	0.20	1.08	0.074
	T1*25–34	0.68	0.44	1.03	0.067
	T1*Engineering	1.00	0.63	1.59	0.994
	T1*Information Technology	2.52	1.64	3.88	<0.001
Reference: T1
	SCM	2.44	1.07	5.55	0.033
	T0	1.06	0.52	2.15	0.881
	Southern and Central Asia	0.64	0.36	1.16	0.140
	Southeast Asia	0.93	0.40	2.18	0.874
	South America	1.75	0.67	4.57	0.253
	Africa and Middle East	2.05	0.73	5.71	0.172
	35+	1.16	0.58	2.33	0.671
	25–34	1.00	0.67	1.49	0.989
	Male	0.80	0.58	1.10	0.172
	Engineering	1.21	0.78	1.87	0.401
	Information Technology	1.83	1.24	2.68	0.002
	SCM*Southern and Central Asia	0.87	0.35	2.12	0.753
	SCM*Southeast Asia	0.98	0.32	2.98	0.973
	SCM*South America	1.72	0.40	7.29	0.463
	SCM*Africa and Middle East	0.36	0.08	1.63	0.185
	SCM*Male	0.97	0.63	1.48	0.877
	SCM*35+	0.49	0.20	1.21	0.122
	SCM*25–34	1.12	0.69	1.80	0.646
	SCM*Engineering	1.53	0.83	2.85	0.175
	SCM*Information Technology	1.03	0.61	1.72	0.918
	T0*Southern and Central Asia	0.76	0.39	1.49	0.427
	T0*Southeast Asia	1.46	0.57	3.76	0.433
	T0*South America	0.52	0.12	2.23	0.379
	T0*Africa and Middle East	0.89	0.29	2.73	0.842
	T0*Male	0.45	0.32	0.63	<0.001
	T0*35+	2.14	0.93	4.92	0.074
	T0*25–34	1.48	0.97	2.25	0.067
	T0*Engineering	1.00	0.63	1.59	0.994
	T0*Information Technology	0.40	0.26	0.61	<0.001
Model QIC = 15369.8
Observations = 14,340
Subjects = 3122
Measurements per subject = 1–24
Working correlation matrix (exchangeable) = 0.295

Notes. T0 is the baseline traditional model delivery (2019). T1 is the traditional model delivery during the immersive block rollout (2021–2022). SCM is the immersive block (Southern Cross Model) delivery (2021–2024).

for a full list of parameter estimates). Overall, students in the SCM had significantly higher odds of passing compared with students in the traditional model. Specifically, odds of passing in the SCM were 2.3 times higher than in T0, and 2.4 times higher than in T1, after controlling for home region, gender, age, and discipline. Interaction terms indicate that when comparing results in T0 and the SCM, the impact of the SCM was moderated by gender, age, and discipline but not by home region. The SCM had a significantly stronger effect on male students and students in Information Technology compared with females and Management and Commerce students but a significantly weaker effect on students aged over 35 compared with students aged 20–24. No significant interactions were found in the T1 to SCM comparison, indicating that the effect of the SCM on student success was consistent across student characteristics and discipline.

4.2. Unit Satisfaction

Results for unit satisfaction are presented in

Table 3. Overall unit satisfaction in traditional (T) and immersive block (SCM) model learning.

		T0		T1		SCM		T0 to SCM	T1 to SCM
Cohort		n (%)	Agreement (%)	n (%)	Agreement (%)	n (%)	Agreement (%)	Change X2(1), p	Change X2(1), p
All		2042	77.7	728	83.7	2133	88.5	10.8 ***	4.8 ***
		(100.0%)		(100.0%)		(100.0%		87.0, <0.001	11.3, <0.001
Discipline
	Information Technology	230	73.0	204	82.4	760	87.1	14.1 ***	4.7
		(11.3%)		(28.0%)		(35.6%)		25.8, <0.001	3.0, 0.103
	Engineering	346	82.7	65	84.6	418	93.5	10.8 ***	8.9 *
		(16.9%)		(8.9%)		(19.6%)		22.2, <0.001	6.3, 0.012
	Management and Commerce	1466	77.2	459	84.1	955	87.3	10.1 ***	3.2
		(71.8%)		(63.0%)		(44.8%)		38.8, <0.001	2.7, 0.098
Home region
	Africa, Middle East, and South America	33	78.8	107	88.8	299	83.6	4.8	−5.2
		(1.6%)		(14.7%)		(14.0%)		0.5, 0.482	1.7, 0.199
	Northeast Asia	159	81.1	80.00	85.0	228.00	91.7	10.6 **	6.7
		(7.8%)		(3.9%)		(4.0%)		9.4, 0.002	2.9, 0.088
	Southeast Asia	161	77.6	30	80.0	184	88.6	11.0 **	8.6
		(7.9%)		(4.1%)		(8.6%)		7.5, 0.006	1.7, 0.187
	Southern and Central Asia	1689	77.3	511	82.6	1422	89.0	11.7 ***	6.4 ***
		(82.7%)		(70.2%)		(66.7%)		72.9, <0.001	13.8, <0.001
Gender
	Female	1008	77.8	359	84.4	965	86.9	9.1 ***	2.5
		(49.4%)		(49.3%)		(45.2%)		28.4, <0.001	1.4, 0.232
	Male	1034	77.6	369	82.9	1168	89.7	12.1 ***	6.8 ***
		(50.6%)		(50.7%)		(54.8%)		60.4, <0.001	12.4, <0.001
Age
	20–24	688	77.2	80	88.8	360	84.2	7.0 *	−4.6
		(33.7%)		(11.0%)		(16.9%)		7.1, 0.010	1.1, 0.299
	25–34	1268	77.5	561	84.1	1581	90.2	12.7 ***	6.1 ***
		(62.1%)		(77.1%)		(74.1%)		86.5, <0.001	15.1, <0.001
	35+	86	83.7	87	75.9	192	82.3	−1.4	6.4
		(4.2%)		(12.0%)		(9.0%)		0.1, 0.771	1.6, 0.211

Notes. T0 is the baseline traditional model delivery (2019). T1 is the traditional model delivery during the immersive block rollout (2021–2022). SCM is the immersive block (Southern Cross Model) delivery (2021–2024). * p < 0.05; ** p < 0.01; *** p < 0.001.

(phase one) and

Table 4. Predictors of unit satisfaction: T0 to SCM.

Predictor		OR	95% CI	p
Reference: T0
	SCM	1.89 **	1.24–2.88	0.003
	SCM*35+	0.42	0.16–1.08	0.072
	SCM*25–34	1.24	0.76–2.01	0.384
Reference: T1
	SCM	0.63	0.30–1.34	0.234
	SCM*35+	1.40	0.40–4.93	0.599
	SCM*25–34	2.03	0.88–4.71	0.097
Model QIC = 4354.1
Observations = 4903
Subjects = 1328
Measurements per subject = 1–14
Working correlation matrix (exchangeable) = 0.179

Notes. T0 is the baseline traditional model delivery (2019). SCM is the immersive block (Southern Cross Model) delivery (2021–2024). ** p < 0.01.

(phase two). Overall UFS response rates ranged from 24.4% in T0 to 41.7% in T1 and 50.6% in SCM. The lowest response rate was among male students (19.7% in T0) and the highest was among students aged over 35 (62.1% in SCM). Table 3 shows that the overall unit satisfaction rose in the SCM to a statistically significant extent compared with T0 (10.8% points) and T1 (4.8% points). However, increases compared with both T0 and T1 were only observed for some sub-groups, namely male students, and students in Engineering, from Southern and Central Asia, and aged 25–34. No significant declines were observed.

GEE modelling (Table 4) revealed that the students’ odds of being satisfied with units were 1.9 times higher in SCM compared with T0 after controlling for age, gender, home region and discipline. No significant differences were found between the outcomes in T1 and SCM (see

Table A3. Full outputs for the predictors of unit satisfaction: T0 to SCM.

Predictor		OR	95% CI (Lower)	95% CI (Upper)	p
Reference: T0
	SCM	1.89	1.24	2.88	0.003
	T1	2.99	1.44	6.20	0.003
	Southern and Central Asia	0.71	0.47	1.07	0.098
	Southeast Asia	0.72	0.42	1.22	0.218
	Africa, Middle East, and South America	0.63	0.37	1.07	0.085
	35+	1.74	0.83	3.65	0.143
	25–34	1.10	0.85	1.43	0.472
	Male	0.93	0.75	1.14	0.486
	Engineering	1.65	1.21	2.25	0.002
	Information Technology	0.96	0.73	1.26	0.774
	SCM*35+	0.42	0.16	1.08	0.072
	SCM*25–34	1.24	0.76	2.01	0.384
	T1*35+	0.30	0.08	1.16	0.081
	T1*25–34	0.61	0.27	1.37	0.231
Reference: T1
	SCM	0.63	0.30	1.34	0.234
	T0	0.33	0.16	0.69	0.003
	Southern and Central Asia	0.71	0.47	1.07	0.098
	Southeast Asia	0.72	0.42	1.22	0.218
	Africa, Middle East, and South America	0.63	0.37	1.07	0.085
	35+	0.52	0.16	1.68	0.273
	25–34	0.67	0.31	1.45	0.312
	Male	0.93	0.75	1.14	0.486
	Engineering	1.65	1.21	2.25	0.002
	Information Technology	0.96	0.73	1.26	0.774
	SCM*35+	1.40	0.40	4.93	0.599
	SCM*25–34	2.03	0.88	4.71	0.097
	T0*35+	3.37	0.86	13.13	0.081
	T0*25–34	1.64	0.73	3.68	0.231
Model QIC = 4354.1
Observations = 4903
Subjects = 1328
Measurements per subject = 1–14
Working correlation matrix (exchangeable) = 0.179

for the full results.)

4.3. Qualitative

Nine focus group participants contributed data to the study (see

Table 5. Description of the focus group participants.

Characteristic		No.	%
Home country	China	3	33.3%
	Brazil	1	11.1%
	Fiji	1	11.1%
	Nepal	1	11.1%
	Peru	1	11.1%
	Philippines	1	11.1%
	United States	1	11.1%
Gender	Female	7	77.8%
	Male	2	22.2%
Course	Master of Social Work	3	33.3%
	Master of Business Administration	1	11.1%
	Master of Information Technology	1	11.1%
	Master of Nursing	1	11.1%
	Master of Osteopathic Medicine	1	11.1%
	Master of Professional Accounting	1	11.1%
	Master of Teaching	1	11.1%

). The majority were female, and there was variation in the students’ home countries and courses of study.

Analysis of the qualitative data resulted in the identification of three main thematic areas (hereafter main themes): timelines, learning, and study-work–life balance. These are shown as circles in Figure 1. Connected to each of these main themes are various sub-themes, shown in rectangles. The lines between the themes and sub-themes represent interconnections between the ideas. For example, ‘difficulties’ were identified in relation to ‘timelines’, ‘learning’, and ‘study-work–life balance’ as well as with several sub-themes within these clusters (e.g., ‘placements’). However, the most prominent area of difficulty mentioned by the participants aligned with ‘study-work–life balance’, and therefore this sub-theme was clustered under that main theme, as indicated by the shared colour.

Broadly, the themes and sub-themes converged with the quantitative results by providing insights into why success may have risen in the immersive block model (see Table 1 and Table 2) and why satisfaction rose or was maintained (see Table 3 and Table 4). Prominent sentiments aligned with each main theme are presented in turn below.

4.3.1. Timelines

Participants highlighted that the immersive block model enabled clear and efficient timelines, which in turn supported a sense of motivation and assisted with effective time management. For example, Chunhua (Teaching) offered that ‘if it were not for the 6-week model, I might not be able to work as efficiently. The clear structure and focused timeline of the terms help me stay productive and motivated.’ Claudia (Business) also noted that the 6-week timeline helped her to ‘actually make a schedule and stick to it’, and Mariana (Social Work) felt able to ‘improve my time management under pressure’. Similarly, Meiling (Social Work) noted about the immersive block model that ‘The good part is like a tight schedule. Give me a bit more motivation. So I am more efficient’. These time and motivated-related factors may partly explain the heightened success captured in Table 1 and Table 2.

Several students, such as Pedro (IT), reflected that this prepared them well for their future careers: ‘[the model] prepares me to be resilient and more flexible in the next step for the industry, for the world’. Arjun (Accounting) also noted that: ‘with this model, you know, I’ve adapted to how to quickly learning about things. I feel like that could really be useful at the workplace’.

4.3.2. Learning

Participants also emphasised the importance of self-directed learning in the immersive block model. Arjun (Accounting) and Fen (Nursing), for example, described their learning as involving ‘a lot of self-study’. This was supported by the online modules embedded in each unit, which students described as interactive and engaging: ‘what is online in terms of modules is excellent… There’s a lot of like, you know, fill-in-the-blank, match these things. And that is really engaging’ (Bella, Osteopathy).

Students appreciated synchronous classes alongside the modules, appreciating the availability of both face-to-face, and online, recorded classes. This in turn allowed for flexibility and for consolidating learning: ‘the flexibility of online courses is helpful when I am working as a teacher aide, as I can review recordings after work and reflect on what I’ve learned’ (Chunhua, Teaching). The emphasis on independent but flexible learning was perceived as enhancing the students’ ‘active learning skill’ (Meiling, Social Work) and supporting them to embrace ‘accountability to take ownership of our learning’ (Sanjana, Social Work). Students further emphasised the professional learning value of placements: ‘I was particularly excited about the placement opportunities… these experiences will provide me with valuable opportunities to train myself to become a qualified teacher’ (Chunhua, Teaching).

Despite these advantages, some aspects of learning were difficult. Here, student sentiments seemed to diverge from the positive quantitative findings presented in Table 1, Table 2, Table 3 and Table 4, illuminating ways in which immersive block learning was challenging and potentially unsatisfying. There was a sense of ‘missing’ elements of learning, with some students such as Claudia (Business) wondering whether units had been streamlined too much: ‘Sometimes it feels a bit rushed, I guess, and that they… remove some content that we should be learning’. Meiling (Social Work) felt that: ‘you can only probably be efficient. Focus on the assignment… I can get to know the concepts to have a general understanding, though it is not very deep, not very profound’.

4.3.3. Study-Work–Life Balance

The final theme was study-work–life balance (SWLB). The majority of participants indicated that they found their lives to be reasonably balanced. Students such as Pedro (IT) acknowledged that full-time postgraduate study necessitated a high time commitment: ‘it is a very intense masters that requires 100% of your attention and you have to be prepared to sacrifice your free time’. Other sentiments were similarly indicative of ‘intense but manageable’ study: ‘I would say, study work balance is fine… But when it comes to leisure, I feel like doing this model [I] could not dedicate enough time’ (Arjun, Accounting).

Students felt, however, that manageability was variable, depending on how well the unit was structured. Meiling (Social Work) explained: ‘It is a very intense schedule… Some of them works pretty well, and some of them is a bit tough’. For Claudia (Business), manageability was also partly dependent on the subject matter:

[The shorter model is] helpful in some units. And then, as I’ve said before, it is not very helpful in others, especially if it is more like technical units where it is skill-based and you really need the time and focus to actually understand the content.

Two students noted difficulties with maintaining an acceptable SWLB, with heightened stress and negative impacts on their well-being. Mariana (Social Work) also spoke of a lack of social connection in the SCM’s self-directed, fast-paced learning style: ‘In my country, I used to go to university to do my bachelor every day… I feel pretty lonely studying [now]… I do not have friends, like it is too stressful for me’.

Bella’s (Osteopathy) experience was unique in the sample and demonstrated divergence from the positive quantitative findings and the experiences of other participants. She described feeling ‘peak stress’ throughout a six-week term, which was exacerbated by balancing unpaid clinical placements with coursework units. She felt that in the two-week break she was ‘not really resting thoroughly’ as she was able to do in the traditional model. This sentiment may indicate one reason why satisfaction did not rise to the same extent as success rates in the immersive model (see Table 3 and Table 4).

Bella also highlighted the impact of financial pressures, recounting that she had to extend her visa and find new living arrangements as some units were not available until later in the year. Claudia (Business) also noted that a need to work alongside a full-time study load exacerbated stresses: ‘The units, I think it is manageable…. But really it is the working part that is a bit tedious’.

5. Discussion

This study examined whether a non-traditional form of university education, a six-week immersive block model, could improve the academic outcomes for international postgraduate students. Data from five years at an Australian university were explored as a case study to evaluate the effectiveness of block model learning as an intervention for raising student success and satisfaction across diverse student cohorts. Results for international postgraduates indicated a significant transformation in this cohort of students’ academic success compared with traditional semester delivery, with overall success rates rising from 58.7% in T0 and 73.1% in T1 to 90.6% in the SCM. This positive influence remained significant after controlling for the students’ home region, discipline, gender, and age. As shown in Table 2, the students’ odds of passing were found to be 2.31–2.44 times higher in the SCM compared with the traditional model after adjusting for these factors. The GEE analysis further indicates that the SCM was significantly associated with better success rates among male students and students enrolled in Information Technology when T0 was considered as the reference delivery model. However, these heightened effects were not significant when T1 was considered the reference. Although it is not clear from the data, we hypothesise that the active, blended learning and authentic assessment aspects of the SCM, progressively rolled out via policy and practice changes across the institution in 2021–2022 (Roche et al., 2024; Wilson et al., 2025a), may have already begun to benefit these cohorts prior to the introduction of immersive scheduling. The more focused six-week terms, combined with additional uplift in pedagogical practice as both staff and students adjusted to the new delivery (Goode et al., 2024b), then benefitted all cohorts without affecting particular groups more strongly than others.

Overall, the SCM appears to have had a highly positive ‘uplifting and levelling’ effect on the students’ academic achievement. Where stark disparities once existed in the student success rates depending on their discipline, gender, and age, this case study suggests that overall, the focused, guided, and active SCM has reduced these gaps to bring students from different demographic and disciplinary backgrounds into closer alignment with each other. The data therefore suggest that immersive blocks may be a more equitable way of learning for diverse international postgraduates, as they have also been found to be for undergraduate domestic cohorts (Roche et al., 2025; Samarawickrema & Cleary, 2021). In particular, success rates for male, younger (aged 20–24), and Information Technology students in the SCM rose to become much closer to, and some cases higher, than that of other postgraduates on international student visas. It remains unclear whether gender differences contribute significantly towards negative experiences as an international student, for example, through heightened acculturative stress (Alzukari & Wei, 2024). However, indications that the immersive block model appears to have significantly improved outcomes for male students, closing a notable gap relative to female students, accords with prior research into immersive block learning at an undergraduate level (Turner et al., 2021) and underscores its potential to shift postgraduate student outcomes at scale.

Satisfaction rates were maintained in the SCM, with no significant declines observed for any cohort (see Table 3). While a significant positive change was observed from T0 to SCM, with the students’ odds of overall unit satisfaction 1.89 times higher in the SCM, no significant change was found from T1 to SCM (see Table 4). Satisfaction was more variable in both domestic and international undergraduate cohorts at the same university, with some significant declines observed in disciplines such as Engineering (Goode et al., 2024c; Wilson et al., 2025c). International postgraduate students are, therefore, seemingly more satisfied with the immersive block format than their undergraduate counterparts. However, we note that declines in satisfaction tend to be linked to transition and adjustment issues in the early years of a new model’s implementation (Goode et al., 2024b), and this study’s data include several years post-implementation.

The focus groups, while limited in generalisability, broadly converged with the quantitative results and provide some indications as to underpinning reasons for these trends. However, there were some important points of divergence; principally, some focus group participants studied in disciplines not addressed in the quantitative data due to small sample sizes in the traditional model, and there were a few male participants. Their sentiments are not therefore strongly indicative of the outcomes presented in the quantitative results. Nonetheless, the sentiments shared still provide valuable insights into student experiences and perceptions and useful context for the quantitative findings. First, participants highlighted that the two-unit-at-a-time, six-week structure was conducive to improving and maintaining effective time management (i.e., it delivered the focus the SCM purports to create). The importance of time management for students transitioning from undergraduate to postgraduate study has been emphasised in the literature and appears highly valued by postgraduate students (Abdul Aziz et al., 2025; Evans et al., 2018). The SCM structure appeared to provide clear deadlines that produced a healthy sense of pressure and motivation. The qualitative data suggest that rather than experiencing cognitive overload from up to four units at a time, students could maintain steady and efficient progress while focusing on two units only—and were ultimately more likely to be successful (see Table 1 and Table 2) through the focus the model provides.

Students also emphasised that the blended learning approach in the immersive block model, with interactive online modules and a mix of on-campus and online classes, supported independent learning and enabled enough flexibility to achieve adequate SWLB. SWLB has emerged as an important but underexplored aspect of the experiences of international students, comprising the interplay between their educational, employment, and private lives (Ong & Ramia, 2009; Vokić et al., 2021). Poor SWLB has been found to contribute to stress, anxiety, depression, exhaustion, and lower academic achievement (Vokić et al., 2021). In the focus groups, most students reported being able to strike an ‘intense but manageable’ balance between study and other aspects of their lives. This is a promising finding, given that difficulties of balancing full-time study with work have been well-documented among international postgraduate cohorts in traditional models of study (Alanzalon, 2024).

The independence facilitated by the blended design of the model was also perceived positively by these postgraduate learners. This may be a key difference between undergraduate and postgraduate students, which partly explains the more positive trends in satisfaction compared with undergraduate cohorts (Goode et al., 2024c). Although both undergraduate and postgraduate international students experience challenges related to transition and adjustment (Andrade, 2006; Menzies et al., 2015), it is acknowledged that postgraduates tend to be older, more academically and professionally experienced, and have a better understanding of the need for independent learning (Evans et al., 2018; Menzies et al., 2015). Accordingly, most participants seemed to adapt well to the self-directed nature of learning in the immersive block model, and there was little emphasis on seeking more peer-to-peer contact. This diverges from studies emphasising the importance of peer interactions for the adjustment of international students (Andrade, 2006; Arkoudis et al., 2019; Parmar et al., 2025) and may again reflect the differing motivations and preferred study habits of postgraduate students compared with their undergraduate counterparts. The focus group data suggest that the blended design of the immersive block model largely suited these postgraduate students’ desires for efficient and structured forms of study. Although studying in ways that are different to previous experiences in home countries can heighten stress among international students (Payne & Leslie, 2025), the clear and guided nature of the immersive block model appeared to mitigate this risk and helped them to learn independently, supporting high rates of success and satisfaction as well as reasonable SWLB.

While the data indicate that overall, postgraduate international students’ achievement and satisfaction were enhanced in the SCM, several points for consideration arose. Some students perceived that, contrary to the model’s theoretical underpinnings, some units were not designed in ways that were conducive to deep learning or that the cognitive load remained high. Concerns about sacrificing depth of learning echo earlier block model studies (Buck & Tyrrell, 2022; Goode et al., 2024b), highlighting the importance of well-structured, scaffolded content in immersive block units and clear communication from educators to ensure that learning is reinforced to a suitable depth. These comments also highlight the importance of targeted professional development for academics moving their content to a block model as well as institutional quality assurance processes to ensure that the volume of content and assessment is appropriate (Jones & de Main, 2025).

While it is unsurprising that professional placements were considered valuable for career preparation among these postgraduate international students (Dennis & Ammigan, 2021), one student in health spoke of heightened stress when placements were not organised well in the block model. Difficulties with placements in a shorter time frame could be one reason why the satisfaction did not rise significantly (see Table 3 and Table 4) in the immersive block model. This suggests that clear and timely communication about ‘roles, responsibilities, and expectations’ is crucial for positive student experiences in work-integrated-learning in block models (Winchester-Seeto et al., 2024, p. 17; see also Goode et al., 2024b). Future research exploring best practice for placements in shorter models, particularly in health disciplines, appears warranted.

Study Limitations

This study had several limitations. As is typical in inductive, qualitative epistemologies (Creswell & Plano Clark, 2011), the focus group participants provided rich insights into their experiences. However, the deliberately small sample did not fully allow for a depth of exploration into any one discipline, and there was also divergence between the demographics of the qualitative and quantitative samples. Therefore, the qualitative findings related to research question 3 should be considered as tentative and indicative, rather than generalisable or neatly transferrable to the samples addressed in the quantitative strand of this study. Sampling bias is also possible, whereby the students consenting to participate may have been very engaged in their studies, with either highly positive or highly negative sentiments to share. Furthermore, in the quantitative data, there were insufficient enrolments in the traditional model to explore outcomes for students from some regions (e.g., Oceania, North America, and Europe) and disciplines (e.g., Health and Education). It was also outside the scope of this study to focus on students enrolled offshore (outside of Australia), studying in the SCM in their home countries through education partnerships. Additional investigations into the experiences of students from these regions and disciplines appear necessary to more fully explore the factors driving or impeding successful and highly satisfying learning. While various controls were used in the quantitative analysis, there were also uncontrolled factors such as the teachers delivering the units and the types of assessment tasks that could have influenced the outcomes.

This study’s findings should be interpreted in light of the underlying theory of change described earlier in Section 2, which considers that immersive block models enhance academic and satisfaction outcomes through reduced cognitive load and active learning pedagogies. Future research could further unpack these mechanisms, examining how specific instructional practices within the block model contribute to the observed effects.

6. Conclusions

Overall, this research demonstrates that immersive block learning using a blended, active learning curriculum design approach can significantly raise the academic success of international students undertaking postgraduate studies. This is an important contribution towards understanding models of learning that improve the experiences of these students, given the dearth of existing research. The data suggest that international postgraduate students value the focus immersive block model learning brings—the clear and efficient timelines enabled by immersive scheduling as well as interactive and flexible learning opportunities and well-organised professional placements. Challenges in curriculum design for immersive block learning include carefully scaffolding and signposting important concepts to support depth of learning and the clear and timely organisation of placements. To support student satisfaction and SWLB, we recommend careful attention to these facets of learning when implementing an immersive block approach. If this can be achieved, the success, satisfaction, and SWLB of international postgraduate students can be transformed for the better, supporting their academic and professional aspirations, and in turn, the rich contribution they make to HE and society.