Understanding Computer Science Students' Career Fair Experiences: Goals, Preparation, and Outcomes

Alyssia Chen; Anthony Peruma; Briana Lee; Kenny Ka'aiakamanu-Quibilan; Samantha Limon

REVIEW 4 major objections 5 minor 2 references

Understanding Computer Science Students' Career Fair Experiences: Goals, Preparation, and Outcomes

T0 review · 4 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read Career fairs serve CS students as both recruiting events and informal career education, with over half reporting exposure to new career paths or technologies and most rating the experience positively.

desk verdict Honest, useful exploratory survey; the missing-data handling is the main thing to fix before trusting the percentages. read the letter →

arxiv 2509.10717 v1 pith:U645347P submitted 2025-09-12 cs.CY

Briana Lee , Samantha Limon , Alyssia Chen , Kenny Ka'aiakamanu-Quibilan , Anthony Peruma This is my paper · ORCID

classification cs.CY

keywords careerfaircomputersciencestudentspreparationstudentmotivationssurveyresearchdevelopmentinternshipsjobsearchstrategies

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

The paper tries to establish that a university tech career fair is more than a recruiting event: it is a venue where CS students, especially underclassmen, encounter career paths and technologies they had not known existed. Using a 17-question survey of 86 students who attended a single Fall 2024 departmental career fair, the authors describe students' goals (mostly internships for underclassmen, full-time roles for seniors), their preparation habits (typically light to moderate, resume-focused), and their evaluations (mostly positive). The central descriptive finding is that 53% of respondents reported learning about new career paths or technologies, such as cybersecurity, product management, and government/defense roles, and many gained job-search advice like resume customization. A sympathetic reader would care because career fairs are a near-universal campus intervention, yet little prior research examines them from the CS student's perspective; this study supplies a baseline of motivations, preparation patterns, and self-reported outcomes that universities and employers can use to design better events.

What carries the argument

The survey instrument: 17 questions (14 mandatory) covering demographics, goals, preparation, in-fair interactions, and outcomes, grounded in Social Cognitive Career Theory. The analysis combines frequency counts and Likert distributions with thematic coding of open-ended responses. What carries the argument is the aggregated self-report: goals by class standing, preparation by method and time, evaluation by rating and company count, and learning by yes/no plus examples.

What would settle it

Compare survey responses with actual post-fair outcomes: track the 86 respondents through the following semester to see whether reported learning of new career paths corresponds to internship applications, interviews, or offers in those fields. If students who said they learned about cybersecurity, product management, or defense roles do not apply to or enter those roles, the claim that career fairs 'expose' students in a developmentally meaningful way would weaken. A second check: compare respondents with non-respondents (the 34 who began but did not complete the survey) on any observable cha

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that career fairs contribute to CS students' career development in two distinct ways: they act as direct recruitment channels (the majority of attendees seek internships or jobs) and as informal educational spaces (over half learn about roles and technologies they had not previously considered). The evidence comes from a convenience sample of 86 respondents who answered all mandatory questions after a Fall 2024 career fair hosted by a single institution's information and computer sciences department. The authors report that students typically interacted with about five companies, rated the event 'Good' or higher, and cited diverse employer r

Load-bearing premise

The load-bearing premise is that the 86 students who answered every mandatory question fairly represent all career fair attendees, and that their self-reports of preparation, confidence, and learning are accurate; nearly every conclusion is an aggregate of these self-reports.

Editorial extensions

If this is right

If career fairs are educational spaces, universities should scaffold them with pre-fair workshops on resume customization and employer research, since most students prepare only lightly.
Underclassmen's near-universal internship focus suggests fairs should be scheduled and marketed with early-career students in mind.
The discovery of new career paths (cybersecurity, product management, government/defense) implies that employer variety directly affects the learning outcome.
Low confidence in follow-up (about half not or slightly confident) suggests the fair alone does not close the loop; structured post-fair engagement could help.
The request for on-site resume feedback indicates a concrete, low-cost improvement.

Reading between the lines

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

The zero response to 'asked for advice from faculty, career office, or university representative' suggests students do not see campus career services as a preparation resource; this could be tested by comparing fair outcomes for students who do consult them.
Since the sample is single-institution and self-selected, the 53% learning figure may not generalize; a multi-institution replication with follow-up recruiting data would test whether reported learning translates into internship or job applications.
The low interest in QA/testing may indicate a curriculum gap; if true, career fairs could serve as early exposure to overlooked roles, but the paper does not establish that.

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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

Quick take: this is a modest, honest single-institution survey of CS students at a career fair. It fills a small gap—I don't know of a prior study focused on CS students' goals, preparation, and takeaways from these events—and the authors share their survey instrument, which helps. The descriptive findings are plausible: underclassmen want internships, everyone updates their resume, and about half say they learned about new career paths. The thematic analysis with three independent coders is a plus.

The soft spots are real but not fatal. The biggest one, flagged in the stress-test note, is the complete-case analysis: 120 people started, 34 were dropped for missing a mandatory question, and there is no comparison between the 34 and the 86. Since the survey was given to students leaving the venue, the non-completers might be the frazzled or negative ones. That makes every percentage a possibly-biased estimate for the full attendee population. The authors should at least report the missing rate and do a simple comparison on the questions they did answer. They also don't give a response-rate denominator, so we can't tell how many students were approached.

There are also some numerical typos that should have been caught: 'nearly half of the 40 participants (45.51%)' is clearly meant to be 86 participants, and 'nine out of sixteen Freshmen' can't be right when the sample has five freshmen. These are minor fixes, but they undercut confidence in the numbers.

The claims are appropriately hedged in the threats-to-validity section: single institution, exploratory, no follow-up interviews. The paper doesn't overreach. My main disappointment is that the discussion section offers generic advice (resume workshops, mock interviews) rather than connecting to the specific differences in preparation by class standing, which are interesting.

Who should read this: career services staff, CS educators who run career fairs, and researchers looking for baseline data. It's not a major result, but it's a reasonable small contribution. I'd send it to peer review for a teaching-and-education venue, with a request to address the missing-data issue and fix the typos. Worth engaging with, but not a must-read.

Referee Report

4 major / 5 minor

Summary. This paper reports on a survey-based study of 86 computer science students who attended a Fall 2024 career fair at the University of Hawai'i at Manoa. The survey, grounded in Social Cognitive Career Theory, addresses four research questions: students' career goals and interests (RQ1), preparation behaviors (RQ2), evaluation of the career fair experience (RQ3), and learning/career development outcomes (RQ4). The results indicate that the most common primary goal was seeking internships, software development was the most frequently selected career interest, most students prepared moderately and updated their resumes, overall experiences were generally positive, and just over half of respondents reported learning about new career paths or technologies. The paper offers implications for academic institutions and employers, discusses threats to validity, and provides a link to the survey artifacts.

Significance. If its claims hold, the paper contributes descriptive evidence on a poorly studied topic: how CS students specifically experience and benefit from career fairs. Its strengths include a clearly presented survey instrument, an openly available artifact link, and a mixed-methods analysis with independent coding of open-ended responses. However, the study is a single-institution convenience sample, and the central findings are based only on the 86 complete responses out of 120 initial respondents. The paper does not analyze the excluded responses, and some conclusions—especially the RQ4 summary that career fairs 'help CS students by exposing them to new career paths'—are stronger than the data support. The work is best viewed as an exploratory descriptive study; with appropriate caveats and corrections it could provide a useful baseline for future multi-institution research.

major comments (4)

[§4 (Results, first paragraph) and §3.3] The analysis excludes 34 of 120 respondents who did not answer all mandatory questions, but the paper never compares the excluded group with the 86 retained respondents. Every reported percentage and the RQ summaries are complete-case estimates, so nonresponse bias could directly affect the central claim. Because participants were approached as they left the venue and many completed the survey on smartphones, break-offs may be systematically different in preparation, engagement, or satisfaction. Please report the response rate, compare available demographic and early-answer data between completers and non-completers, and discuss how the missing 28.3% of responses could affect the conclusions. Without such a check, the representativeness of the 86 is unestablished.
[§4.1 (RQ1)] The text calls the 52.33% internship-seeking figure an 'overwhelming majority' and asserts 'a clear relationship between class standing and primary goals.' These are load-bearing descriptive claims, but no statistical test is provided. A chi-square or Fisher exact test on the class standing × goal table would be appropriate; if the sample is too small for such tests, the claims should be softened to 'the descriptive counts suggest' rather than 'clear relationship.' This overstatement also appears in the RQ1 summary.
[§4.4 (RQ4), Abstract, Conclusion] The RQ4 summary states that 'career fairs help CS students by exposing them to new career paths, technologies, and advice.' However, question #14 shows that only 53.49% (46/86) learned something new and 46.51% did not. This is a near-even split, not a strong majority. The central claim should either be restricted to 'about half of the respondents reported learning...' or supported with additional data showing that other benefits justify the broader 'help' claim. As written, the abstract and conclusion overstate the strength of the evidence.
[§4.2, Q6 and Table 3] The sentence 'nearly half of the 40 participants (45.51%)' is internally inconsistent: 40/86 equals 46.51%, not 45.51%, and '40 participants' is a count, not a percentage. Additionally, in Table 3 the percentages are computed over total responses (145 selections), but the text says 'chosen by 68 participants (46.9%)', which is misleading because 68/86 equals 79.1%. Please clarify the denominators and correct the numbers, since these are key descriptive results for RQ2 and affect how readers interpret preparation patterns.

minor comments (5)

[§4.2, Q7] The summary says students 'usually spent one to two hours preparing,' but the survey categories are 'Less than 1 hour,' '1-3 hours,' and '3-5 hours.' Revise to 'one to three hours' or report the actual distribution/median.
[§4.3, Q11] The phrase 'the majority of participants reported low to moderate confidence levels' groups 'Moderately confident' with 'low'; consider wording such as 'no more than moderate confidence' to avoid ambiguity.
[§4.4, Q16] There is a grammatical error: 'Participant highlighted the importance...' should be 'Participants highlighted...'.
[Table 3] The header contains a typo: 'Preperation' should be 'Preparation'.
[§6 (Threats to Validity)] The threats section does not mention the incomplete-response issue. Adding a discussion of nonresponse bias would strengthen the validity assessment and align with the major comment above.

Circularity Check

0 steps flagged · score 0.0 of 10

Survey-based descriptive study; no circular derivation found.

full rationale

This paper is a descriptive survey study. It does not present a mathematical derivation, fitted model, or predictive claim whose output is equivalent to its input. Each research question is answered by directly reporting and aggregating responses to the survey instrument (Table 1), e.g., RQ4 is answered by summarizing the Yes/No responses to Q14 and the free-text themes from Q15 and Q16. The central claim that career fairs expose students to new career paths is a thematic summary of those self-reports, not a prediction from a fitted parameter. Social Cognitive Career Theory (SCCT) is used only to inform question design, and it does not constrain or determine the reported outcomes. The two self-citations (Chen et al. 2024 and Chen et al. 2025, both involving authors of the present paper) appear in the introduction and related work as contextual background on impostor syndrome and AI-in-hiring; they are not load-bearing for any result. The paper also openly acknowledges its limitations, including single-institution sampling and lack of follow-up interviews. No step in the paper reduces by definition or by self-citation to its own input. Therefore, no significant circularity is present.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

No free parameters or invented entities. The study relies on domain assumptions about sample representativeness and self-report validity.

assumptions (2)

domain assumption Self-reported survey responses accurately reflect students' actual preparation, confidence, and learning outcomes.
The analysis uses self-reports without objective measures; any systematic self-report bias propagates to all findings.
domain assumption The 86 valid respondents are representative of career fair attendees at the institution, and the single-institution sample supports the study's claims about CS students generally.
The paper acknowledges single-institution limits in Threats, but the conclusions in Section 5 are stated broadly.

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Cite this review

Pith. "Pith review of Understanding Computer Science Students' Career Fair Experiences: Goals, Preparation, and Outcomes." pith.science (2026). https://pith.science/paper/U645347P

@misc{pith2026250910717,
  author       = {Pith},
  title        = {Pith review of: Understanding Computer Science Students' Career Fair Experiences: Goals, Preparation, and Outcomes},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/U645347P}},
  note         = {Machine review of arXiv:2509.10717}
}

read the original abstract

The technology industry offers exciting and diverse career opportunities, ranging from traditional software development to emerging fields such as artificial intelligence, cybersecurity, and data science. Career fairs play a crucial role in helping Computer Science (CS) students understand the various career pathways available to them in the industry. However, limited research exists on how CS students experience and benefit from these events. Through a survey of 86 students, we investigate their motivations for attending, preparation strategies, and learning outcomes, including exposure to new career paths and technologies. We envision our findings providing valuable insights for career services professionals, educators, and industry leaders in improving the career development processes of CS students.

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Reference graph

Works this paper leans on

2 extracted references

[1]

Baltes, S., & Ralph, P. (2022). Sampling in software engineering research: A critical review and guidelines.Empirical Software Engineering. https://doi.org/10.1007/s10664-021-10072-8 Beam, E. A. (2016). Do job fairs matter? Experimental evidence on the impact of job-fair attendance. Journal of Development Economics. https : / / doi.org/10.1016/j.jdeveco.2...

doi:10.1007/s10664-021-10072-8 2022
[81]

H¨otte, K., Somers, M., & Theodorakopoulos, A. (2023). Technology and jobs: A systematic literature review.Technological Forecasting and Social Change,194, 122750. Kasunic, M. (2005). Designing an effective survey. Kitchenham, B. A., & Pfleeger, S. L. (2002). Principles of survey research: Part 3: Constructing a survey instrument.SIGSOFT Softw. Eng. Notes...

arXiv 2023

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.

[1] [1]

Baltes, S., & Ralph, P. (2022). Sampling in software engineering research: A critical review and guidelines.Empirical Software Engineering. https://doi.org/10.1007/s10664-021-10072-8 Beam, E. A. (2016). Do job fairs matter? Experimental evidence on the impact of job-fair attendance. Journal of Development Economics. https : / / doi.org/10.1016/j.jdeveco.2...

doi:10.1007/s10664-021-10072-8 2022

[2] [81]

H¨otte, K., Somers, M., & Theodorakopoulos, A. (2023). Technology and jobs: A systematic literature review.Technological Forecasting and Social Change,194, 122750. Kasunic, M. (2005). Designing an effective survey. Kitchenham, B. A., & Pfleeger, S. L. (2002). Principles of survey research: Part 3: Constructing a survey instrument.SIGSOFT Softw. Eng. Notes...

arXiv 2023