Information Technology Curriculum: General or Specialized? An Australia's Census Study

Pak-Lok Poon; Santoso Wibowo; Sau-Fun Tang; Srimannarayana Grandhi

REVIEW 3 major objections 7 minor 11 references

Information Technology Curriculum: General or Specialized? An Australia's Census Study

T0 review · 3 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A complete census of Australian universities shows that the general IT degree is no longer the sole norm: most institutions now offer specialized or major-based degrees, with data science, cybersecurity, AI, and software engineering…

desk verdict A genuinely useful descriptive census of Australian IT degrees that deserves peer review, but the industry-needs claim outruns the supply-side data and the dataset should be shared openly. read the letter →

arxiv 2608.02952 v1 pith:HOVALENL submitted 2026-08-03 cs.CY

Pak-Lok Poon , Sau-Fun Tang , Santoso Wibowo , Srimannarayana Grandhi This is my paper · ORCID

classification cs.CY

keywords informationtechnologycurriculumgeneraldegreespecializedwithmajorscensusstudyeffectsizeanalysisgraduateemployabilityAustralianuniversities

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper reports a complete census of information-technology degree offerings at every Australian university that teaches IT, classifying each degree as general, specialized, or a degree with majors. The central finding is that offering only an unspecialised general IT degree has become unusual: over 92% of bachelor's-level and over 94% of master's-level institutions offer a specialized or major-based alternative, and the most common specializations and majors are data science and analytics, cybersecurity, artificial intelligence, and software engineering. Using effect sizes rather than significance tests, the authors show that technology-focused universities offer more specialized degrees than comprehensive universities, that the prestigious Group of Eight (Go8) research-intensive universities offer at least as many once one outlier is set aside, and that master's degrees embed internships less often than bachelor's degrees. The census also exposes gaps: formal industry co-design and co-teaching are nearly absent, and career-oriented specializations such as FinTech do not appear in IT schools. The paper thus provides the first systematic baseline for asking whether Australian IT curricula are producing graduates who match industry needs.

What carries the argument

The central object is the three-way classification of IT degrees into general (GL), specialized (SP), and major-based (MA) degrees, where an MA-degree contains a named major inside a broader degree and therefore sits on a spectrum between the general and the specialized. The argument is carried by a hand-collected census dataset of all 39 Australian universities offering IT study, built from university websites over October–November 2025. The analytic machinery is Cohen's d effect size applied to the entire population, used to compare mean numbers of offerings between groups such as technology-focused versus comprehensive universities; because the dataset is a census, the paper deliberately avoids hypothesis testing and instead reports the magnitude of differences.

What would settle it

Contact the universities and compare the online course data against official handbooks and program regulations; if the counts of specialized degrees, majors, internships, research components, or research institutes change materially, the census picture is not stable. A repeat census at a different date (for example, six months later) that shifts the top-four specializations or reverses the effect-size directions would also contradict the reported status quo.

Watch

Extended reading notes

Core claim

The paper's central discovery is the status quo itself: as of late 2025, offering a plain general IT degree as the only option is no longer the norm in Australia. Across the 39 universities that offer IT study, 92.1% of bachelor's-level institutions and 94.7% of master's-level institutions offer either a specialized degree or an IT degree with one or more majors. The four most popular specializations and majors are the same list – data science and analytics, cybersecurity, artificial intelligence, and software engineering – and together they account for 78.3% of bachelor's and 85.9% of master's specialized offerings. The authors interpret this as universities responding to the employment market, but they also find that curricula are largely technology-centered rather than career-centered, that master's degrees embed internships less often than bachelor's degrees, and that formal industry involvement beyond internships is minimal.

Load-bearing premise

The study assumes that the information posted on each university's official website is accurate, current, and complete enough to classify every IT degree and to count the presence of majors, internships, research components, professors, and research institutes correctly.

Editorial extensions

If this is right

A prospective IT student in Australia can expect nearly every university to offer a specialized or major-based degree, with data science/analytics, cybersecurity, AI, and software engineering the most common focal areas.
Universities of technology offering more specialized degrees than comprehensive universities implies that institutional mission and STEM focus shape curriculum breadth, not just the level of resources.
The near-absence of FinTech as an IT specialization and only one quantum-computing degree suggest these curricula are aligned with today's job market more than with emerging technology careers.
Low rates of industry co-design and co-teaching imply that internships are currently the dominant, and often the only, formal industry link in most IT degrees.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The general/specialized/majors trichotomy could serve as a comparative lens for IT education in other countries, enabling cross-national comparisons of how curricula specialize.
If the population-level effect sizes hold, they give any university a direct baseline: its own counts of specialized and major-based degrees can be compared with the national means reported here.
The finding that internships are more common in bachelor's than in master's degrees is likely to change as professional master's programs grow, and could be tracked by repeating the census over time.
The paper's explanation for regional universities narrowing the gap – city campuses that behave like metropolitan universities – suggests that physical campus location, rather than institution type, may be the better predictor of curriculum breadth.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The new thing here is the dataset and the categorization. Nobody else has systematically classified every Australian university's IT bachelor's and coursework master's offerings into general degrees, specialized degrees, and degrees with majors, then compared counts across university types. That is a real gap, and the authors filled it. The counting conventions are disclosed, and using Cohen's d on a population is defensible because they are measuring the magnitude of group differences, not inferring from a sample. The RQ-by-RQ structure makes the paper easy to check. I believe the descriptive findings: UoTs offer more specialized degrees than comprehensive universities; regional universities are closer to metropolitan than intuition suggests; the top specializations are data science/analytics and cybersecurity; master's degrees have fewer internships.

Credit where due: the authors notice when their own expectations fail (e.g., regional vs metropolitan) and offer concrete mechanisms. The Go8 analysis is handled honestly: UoM's Melbourne Model is a real outlier, and they rerun as Go7 and report both. The limitation statement about website currency is candid.

Now the soft spots. The biggest one is the gap between the supply-side census and the industry-needs conclusion. The abstract, RQ10, and the recommendations repeatedly infer that popular specializations mean the sector "properly addresses" industry needs, but none of the data measure employer demand, hiring difficulty, or graduate outcomes. The FinTech example cuts the other way: the absence of FinTech degrees is taken as a curriculum gap, but the paper does not show that employers demand FinTech-specific IT degrees; arguably a data science or cybersecurity graduate is what is actually wanted. That claim should be re-scoped to "these are the most common offerings, which suggests what universities believe the market wants," or backed with external labor-market data. The post-hoc UoM exclusion is a soundness wrinkle: the Go8-vs-others bachelor's comparison flips from mixed to positive after exclusion. The authors are transparent about it, which I respect, but the conclusion in the abstract and conclusion ("Go8 universities (except UoM) offer ... more") is fragile with n=7/8. Better to present both and emphasize the sensitivity. The data availability statement ("upon reasonable request") is weak for a census whose whole value is the dataset; they should deposit the degree-level counts in a repository. I would also like to see inter-rater reliability on the GL/MA/SP classification if more than one person coded, or at least a sample audit. Minor: some discussion paragraphs lean on speculative explanations, but they are clearly labeled as speculation.

Who is this for? Computing education researchers, Australian universities planning programs, and policy makers. It is not causal, not theoretical, but it is the missing descriptive base layer. A serious referee should engage with it. My recommendation: send to peer review, with the note that the industry-needs language needs to be pulled back and the data published.

Referee Report

3 major / 7 minor

Summary. This paper reports a census study of all 39 Australian universities that offer IT degrees, categorizing their undergraduate and postgraduate coursework IT programs into general degrees (GL), specialized degrees (SP), and degrees with majors (MA). The authors pose ten research questions covering the prevalence of each degree type, differences by university category (universities of technology, Group of Eight, regional vs. metropolitan), the most popular specializations and majors, the presence of research components and internships, supporting professorial infrastructure and research institutes, and forms of industry involvement. They use counts, percentages, and population effect sizes (Cohen's d) to describe the status quo, and derive recommendations about career-centered curricula, cross-university co-teaching, and student co-creation of syllabi.

Significance. The study's main contribution is a systematic, population-level inventory of IT degree offerings in Australia, a useful reference for curriculum planners and policy discussions. The population approach is a strength: because all offering universities are included, the authors correctly avoid null-hypothesis significance testing and report effect sizes as descriptive magnitudes. The classification scheme (GL/MA/SP) is clearly defined and the counting conventions, though complex, are disclosed. However, the paper's stated conclusion that the data provide 'insights into whether these IT degrees properly address the IT industry's needs' is not supported by the supply-side counts alone; no demand-side or labor-market outcome data are collected. The post hoc exclusion of the University of Melbourne in RQ3 also changes the qualitative conclusion and needs stronger justification. These issues are fixable by re-scoping the claims and adding sensitivity analysis, but they affect the central interpretation.

major comments (3)

[Abstract and Introduction; RQ5/RQ7 discussions; Conclusion] The claim that the census provides 'insights into whether these IT degrees properly address the IT industry's needs' is not supported by the data actually collected. RQ1–RQ9 are supply-side inventories (counts of degree types, research components, internships, professors, and research institutes), and RQ10 counts only the presence of industry advisory boards, co-design, co-teaching, and internships. None of these measures captures employer demand, graduate employment outcomes, skill shortages, or the fit between curricula and job requirements. For example, the observation that no university offers a FinTech specialization is used to argue a gap, but no evidence shows that employers demand such a degree from IT schools. I recommend removing or explicitly re-scoping the needs-assessment claim to 'supply-side landscape,' or adding external labor-market data (e.g., job advertisement analyses, employer surveys) if the claim is retained.
[RQ3, Table 7] The exclusion of the University of Melbourne from the RQ3 analysis is post hoc and changes the qualitative conclusion. Initially, the Go8 comparison for bachelor's SP-degrees yields Cohen's d = -0.14 (marginally fewer than other universities); after removing UoM, the text reports d = -0.09 and concludes that 'Go7 offers at least the same mean number of (and often more) MA-degrees and SP-degrees as other universities at both degree levels.' This is a data-dependent decision: the exclusion is motivated by the Melbourne Model, which is a legitimate structural reason, but the manuscript does not present a pre-specified outlier criterion or a sensitivity analysis showing both Go8 and Go7 results side by side with interpretation. I recommend reporting both sets of effect sizes in full and clearly labeling the Go8 result as driven by UoM, rather than switching to Go7 without transparent comparison.
[Data availability statement and Study settings] The dataset for this census is described as 'available upon reasonable request,' but no raw data, degree lists, classification codebook, or per-university worksheet is provided. Because every result in the paper derives from manual coding of university websites, an independent reader cannot verify the central counts in Tables 2, 4, 6, 8, 10, 12, 14, 15, or the classification of degrees into GL, MA, and SP. I strongly encourage making the full dataset and coding protocol publicly available as supplementary material (e.g., a spreadsheet listing each university, each program title, the assigned category, and the webpage source and access date). Without this, the census is not auditable, and the manuscript's reproducibility claim is weakened.

minor comments (7)

[Tables 5, 7, 9] The notes contain typos: 'Gohen's d' should be 'Cohen's d' in all three table notes.
[RQ3 text] In the RQ3 discussion, 'G07' should be 'Go7', and 'formers' should be 'former's' in the sentence about 'the formers' scale of operations.'
[Tables 4, 6, 8] The header rows of these tables are visually ambiguous: the repeated 'No. of MA-degrees' columns are not clearly separated into UoTs vs CUs (or Go8 vs others, metropolitan vs regional) and bachelor's vs master's. Adding subheaders or blank columns would improve readability.
[RQ6] The standard deviations reported for SP-degrees (bachelor's mean 1.2, SD 4.3; master's mean 1.8, SD 8.7) are implausibly large for count-like data and suggest a highly skewed distribution. Reporting medians and interquartile ranges, or a direct frequency comparison, would be more informative than mean and Cohen's d for such data.
[RQ9] The definition of 'percentage of coverage' says the authors checked whether a university has professors whose expertise covers a major or specialization, but the method for determining 'expertise' from websites (e.g., research profile keywords, faculty directory categories) is not described. Please add a sentence specifying the coding rule.
[References] The reference 'Kumari, A. (2026)' appears to be a blog post with a publication date after the data collection period (October–November 2025). Verify the date and source; if it is a blog, consider replacing it with a peer-reviewed curriculum theory reference.
[Table 13 footnote] The footnote for Table 13 correctly notes that bachelor's-level counts exclude UoM because it offers no bachelor's IT degree, but the table row label 'Bachelor's or master's' is ambiguous. Please rephrase to make clear that the 84.6% refers to either level among all 39 universities.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the census is descriptive, its counting conventions are disclosed, and no fitted parameter or self-citation chain drives the results.

full rationale

This is a descriptive census of Australian university IT degree offerings. The derivation chain is: define degree types (GL, MA, SP) using stipulated criteria; collect counts from university websites; compute group means and Cohen's d effect sizes on the full population; report the counts. There is no fitted parameter later presented as a prediction, no equation whose output is an input by construction, and no load-bearing self-citation. The classification conventions (counting an MA-degree with multiple majors as multiple degrees, splitting double-IT majors for RQ9, excluding the University of Melbourne from the Go8 bachelor-level analysis) are disclosed and are measurement choices rather than circular reductions. The conclusion that the status quo 'provided insights into whether these IT degrees properly address the IT industry's needs' goes beyond the supply-side data, because no employer-demand variable is measured; that is an evidentiary or soundness limitation, not circular reasoning. The stated limitations (two-month data collection window, possible stale websites) are acknowledged data-quality concerns. Accordingly, no circular step is identified and the circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No free parameters are fitted; the study is a census. The main assumptions are data fidelity, classification reliability, and one post-hoc exclusion. No new entities are posited.

assumptions (4)

domain assumption University websites accurately and completely reflect current IT degree offerings and curriculum details
Data collection was based solely on websites in the period October-November 2025 (Section 'Participant universities and data collection'). If websites are outdated or incomplete, all counts are affected.
domain assumption Degrees can be reliably classified into GL, MA, and SP using the authors' definitions
The authors assign each degree to one of three types based on titles and described structure (Section 'Introduction'). No inter-rater reliability check is reported.
ad hoc to paper The University of Melbourne is an outlier and can be excluded from the Go8 analysis in RQ3
In RQ3, Go8 is replaced by Go7 after removing UoM because the Melbourne Model makes it an outlier. This exclusion changes the bachelor's MA-degree effect size from 0.52 to 0.84.
standard math Cohen's d thresholds for small, medium, and large effects apply meaningfully to whole-population comparisons
The paper uses Cohen's d with the standard thresholds (Magnusson, 2025). Since the data are the whole population, the thresholds are used as interpretive conventions rather than inference tools.

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Cite this review

Pith. "Pith review of Information Technology Curriculum: General or Specialized? An Australia's Census Study." pith.science (2026). https://pith.science/paper/HOVALENL

@misc{pith2026260802952,
  author       = {Pith},
  title        = {Pith review of: Information Technology Curriculum: General or Specialized? An Australia's Census Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HOVALENL}},
  note         = {Machine review of arXiv:2608.02952}
}

read the original abstract

Despite the strong employment prospect for information technology (IT) graduates, a comprehensive study investigating the status quo of offering different types of IT degree by Australian universities does not exist. To address this issue, this paper investigates how Australian universities offer three different types of IT degree: general, specialized, and those with majors. Using effect size analysis, we have observed some interesting phenomena about the correlation between how Australian universities offer their IT degrees and different factors, including, for example, type and reputation of universities, degree level, research component, supporting infrastructure, and industry engagement. Our census study painted the status quo of offering different types of IT degree by Australian universities, and provided insights into whether these IT degrees properly address the IT industry's needs. Based on the findings, we have also highlighted some insights and made recommendations on how to improve IT students' learning outcomes and graduates' employability.

Review history (2 revisions) →

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Reference graph

Works this paper leans on

11 extracted references · 11 canonical work pages

[1]

GL” denotes “General

1 Information technology curriculum: General or specialized? An Australia’s census study Pak-Lok Poon (Corresponding Author) School of Engineering and Technology Central Queensland University Melbourne 3000, VIC, Australia Email: p.poon@cqu.edu.au Sau-Fun Tang School of Engineering and Technology Central Queensland University Melbourne 3000, VIC, Australi...

work page 2025
[4]

practical

At first glance, for both bachelor’s and master’s levels, the mean number of MA-degrees and the mean number of SP-degrees offered by UoTs are larger than the corresponding numbers offered by CUs. For example: • Mean number of bachelor’s MA-degrees: UoTs = 7.3; CUs = 4.4. • Mean number of master’s SP-degrees: UoTs = 3.0; CUs = 1.7. Since our study covers t...

work page 2025
[5]

Second, by virtue of their higher academic reputation, Go8 universities are more able to attract high-caliber academics to fill their professorship positions

First, Go8 universities have more funding resources, thereby allowing them to recruit more professors to fit their teaching and research needs. Second, by virtue of their higher academic reputation, Go8 universities are more able to attract high-caliber academics to fill their professorship positions. Research institute or center: Lindsay et al. (2002) re...

work page 2002
[8]

obviously less

The mean number of MA-degrees and SP-degrees at both levels offered by metropolitan universities are larger than those offered by regional universities (e.g., mean number of bachelor’s MA-degrees: metropolitan universities=5.1; regional universities=3.6). The corresponding effect size analysis is shown in 7 Some universities have multiple campuses but non...

work page 2025
[19]

business-oriented

This table shows that, considering the bachelor’s and master’s levels together, more than half (59.0%) of universities offering internships in at least one of their IT degrees, indicating that universities generally consider that internships play a key role in training students and improving employability (Chillas et al., 2015). Considering Tables 18 and ...

work page 2013
[48]

research institutes

The mean number of research institutes per each university is 1.23 (=48/39), with a range [0, 6], and a standard deviation of 1.54. Due to their prestigiousness, Go8 universities are able to attract more funding and, hence, are expected to have more financial resources to establish their research institutes. Under this rationale, we reperformed the above ...

work page 2022
[327]

Magnusson, K. (2025). Interpreting Cohen’s d Effect Size: An Interactive Visualization. https://rpsychologist.com/cohend/ Monash University Malaysia. (2025). Exploring the Top-10 In-demand Fields in Computer Science. www.monash.edu.my/news-and-events/trending/top-10-high-demand-Fields-in-compute-science Parliament of Australia. (2022). University Research...

work page 2025
[641]

Yates, L., & Collins, C. (2010). The absence of knowledge in Australian curriculum reforms. European Journal of Education, 45(1), 89-102. 14 Table

work page 2010

Show all 11 references

[825]

Kumari, A. (2026). Subject Centered Curriculum: Meaning, Definition, and Everything You Need to Know. https://www.21kschool.com/in/blog/subject-centered-curriculum/ Laundon, M., Williams, P., & Williams, J. (2023). Benefits of co-creating higher education learning resources: A...

2026
[2008]

Accordingly, UoM does not offer a specific bachelor’s IT degree (hence, the number of GL-degrees, MA-degrees, and SP-degrees at the bachelor’s levels are all zero)

This model replaced traditional bachelor’s degrees with a two-tiered system, where students complete a broad bachelor’s degree (e.g., arts or science) before moving on to a specialized (e.g., IT, engineering, and medicine) graduate-level study. Accordingly, UoM does not offer ...

2025
[2025]

full” professors whose research expertise matches to the IT disciplines offered, and Go8 universities recruit more “full

to complete. In principle, though unlikely, some changes could have happened in the online web pages amidst our data collection work. However, subject to our available resources, we have already 12 made our best effort to shorten the data collection period, with a view to mini...

2022

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

GL” denotes “General

1 Information technology curriculum: General or specialized? An Australia’s census study Pak-Lok Poon (Corresponding Author) School of Engineering and Technology Central Queensland University Melbourne 3000, VIC, Australia Email: p.poon@cqu.edu.au Sau-Fun Tang School of Engineering and Technology Central Queensland University Melbourne 3000, VIC, Australi...

work page 2025

[2] [4]

practical

At first glance, for both bachelor’s and master’s levels, the mean number of MA-degrees and the mean number of SP-degrees offered by UoTs are larger than the corresponding numbers offered by CUs. For example: • Mean number of bachelor’s MA-degrees: UoTs = 7.3; CUs = 4.4. • Mean number of master’s SP-degrees: UoTs = 3.0; CUs = 1.7. Since our study covers t...

work page 2025

[3] [5]

Second, by virtue of their higher academic reputation, Go8 universities are more able to attract high-caliber academics to fill their professorship positions

First, Go8 universities have more funding resources, thereby allowing them to recruit more professors to fit their teaching and research needs. Second, by virtue of their higher academic reputation, Go8 universities are more able to attract high-caliber academics to fill their professorship positions. Research institute or center: Lindsay et al. (2002) re...

work page 2002

[4] [8]

obviously less

The mean number of MA-degrees and SP-degrees at both levels offered by metropolitan universities are larger than those offered by regional universities (e.g., mean number of bachelor’s MA-degrees: metropolitan universities=5.1; regional universities=3.6). The corresponding effect size analysis is shown in 7 Some universities have multiple campuses but non...

work page 2025

[5] [19]

business-oriented

This table shows that, considering the bachelor’s and master’s levels together, more than half (59.0%) of universities offering internships in at least one of their IT degrees, indicating that universities generally consider that internships play a key role in training students and improving employability (Chillas et al., 2015). Considering Tables 18 and ...

work page 2013

[6] [48]

research institutes

The mean number of research institutes per each university is 1.23 (=48/39), with a range [0, 6], and a standard deviation of 1.54. Due to their prestigiousness, Go8 universities are able to attract more funding and, hence, are expected to have more financial resources to establish their research institutes. Under this rationale, we reperformed the above ...

work page 2022

[7] [327]

Magnusson, K. (2025). Interpreting Cohen’s d Effect Size: An Interactive Visualization. https://rpsychologist.com/cohend/ Monash University Malaysia. (2025). Exploring the Top-10 In-demand Fields in Computer Science. www.monash.edu.my/news-and-events/trending/top-10-high-demand-Fields-in-compute-science Parliament of Australia. (2022). University Research...

work page 2025

[8] [641]

Yates, L., & Collins, C. (2010). The absence of knowledge in Australian curriculum reforms. European Journal of Education, 45(1), 89-102. 14 Table

work page 2010