Changes in ICT career aspirations are not systematically linked to contemporaneous changes in other occupational domains¶

Claim · All claims
Evidence · 1 study · associational r1 · q2
Currency · newest study 2022 · not yet checked against its sources · not reviewed by a person · recheck by 2027: baselines and conditions change

Subclaims¶

q2 i? A learned Bayesian network over changes in ICT, health, science-engineering and science-technical aspirations contains no edges, indicating no significant conditional dependencies between domains. → Popa 2022

Evidence¶

Popa 2022¶

Popa, D. M., Oprea, S.-V., & Bâra, A. (2022). Learning after COVID-19 and the ICT career aspirations: Are students entering the AI era with weaker skills? Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies. [email protected]. https://arxiv.org/abs/2605.27391 doi:10.48550/arxiv.2605.27391

q2 · i? · associational · r1

Score-based (BIC, hill-climbing) Bayesian network over quantile-discretized country-level aspiration changes from PISA 2018–2022. The null structure result supports interpreting ICT aspirations as a distinct, autonomous domain; no effect size is printed.

"The learned structure contains no edges, indicating that changes in ICT aspirations are not systematically linked to contemporaneous changes in health, science and engineering, or science-related technical career expectations."

Discussion¶

Related Claims¶

• Digital skills are the only consistently positive predictor of ICT career aspiration growth, while teacher support and autonomy show negative standardized associations — related
• Country-level ICT career aspirations increased globally but unevenly between PISA 2018 and 2022, with most systems showing moderate gains and a minority declining — related
• Simulated one-standard-deviation increases in autonomy yield only modest predicted changes in ICT career aspirations — related
• Learning environment indicators only moderately discriminate between countries with high versus low ICT aspiration growth, with digital skills the strongest discriminant — related
• In simulations, Bayesian GLASSO estimation outperforms frequentist GLASSO on bias in edge weights, centrality measures, correlation between estimated and true partial correlations, and specificity — related
• In simulations, the Bayesian GLASSO performed better than the Horseshoe prior for estimating symptom networks — related
• The frequentist graphical LASSO has difficulty estimating centrality indices and uncertainty in these measures in symptom networks — related