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Record W4412032557 · doi:10.20853/39-3-6383

Can MS Excel help Finance students to Excel? A study in student work readiness

2025· article· en· W4412032557 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSouth African Journal of Higher Education · 2025
Typearticle
Languageen
FieldComputer Science
TopicSpreadsheets and End-User Computing
Canadian institutionsImpact
Fundersnot available
KeywordsMs excelHigher educationWork (physics)Mathematics educationStudent engagementComputer scienceFinancePsychologyBusinessEconomicsEngineeringSoftware engineeringEconomic growth

Abstract

fetched live from OpenAlex

Microsoft Excel is vital in the finance sector, and preparing students for the professional arena involves honing their spreadsheet skills. A study investigated the efficacy of a finance-centric Microsoft Excel workshop as an intervention to enhance students' skills and readiness for work. Pre- and post-intervention tests assessed students' perceptions and abilities. The results revealed a significant improvement across basic, intermediate, and advanced Excel skills, positively impacting students' self-perceptions. The majority recognized Excel as a critical job market skill. The study recommends integrating Excel assignments into finance modules to elevate skills and enhance finance knowledge retention, promoting self-directed learning.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.320
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it