Digital Platforms May Boost Students’ Labour Literacy Through Motivation and Self-Regulated Learning

As universities worldwide scramble to prepare graduates for a labour market being reshaped by artificial intelligence and automation, a new study offers a quantitative answer to a deceptively simple question: can the digital platforms students already use every day actually make them more work-ready? Researchers at King Mongkut’s Institute of Technology Ladkrabang in Bangkok report that support provided through digital learning platforms is positively associated with students’ labour literacy, and that this relationship operates largely through two psychological engines, learning motivation and self-regulated learning. The findings, published in SN Social Sciences, arrive amid mounting evidence of a global skills gap, with recent employability surveys suggesting that employers consistently rate new graduates as underprepared for the demands of modern workplaces.

Labour literacy is a concept that extends well beyond vocational training. The researchers define it as the full set of work-related knowledge, skills and attitudes that students need to develop both personally and professionally. It encompasses an understanding of labour markets and workers’ rights, practical competencies for employment, and the dispositions, such as responsibility and perseverance, that underpin sustainable working lives. Unlike narrow technical skills, labour literacy is conceived as a foundational component of a student’s overall development, which is precisely why educators and policymakers have struggled to measure it, let alone cultivate it at scale.

The research team, led by Pengfei Liu under the supervision of Piyapong Sumettikoon, surveyed 472 university students, including both undergraduate and postgraduate learners, all of whom had prior experience with digital learning. Participants completed structured questionnaires measuring their perceptions of digital platform support, their learning motivation, their self-regulated learning habits, and their labour literacy. The responses were analyzed in SPSS using descriptive statistics, reliability and validity testing, correlation and regression analysis, and structural equation modeling, a statistical technique that allows researchers to test whether one variable influences another indirectly through an intermediary.

The headline result is a statistically significant positive association between digital platform support and labour literacy, with a standardized path coefficient of 0.350, significant at the p less than 0.001 level. In practical terms, students who reported that their platforms provided useful resources, timely feedback and responsive technical support also reported higher levels of work-related knowledge, skills and attitudes. But the more intriguing findings lie in the mediating pathways. Digital platform support was associated with higher learning motivation, which in turn was linked to labour literacy, with a mediated path coefficient of 0.277. A second pathway ran through self-regulated learning, the capacity to plan, monitor and adjust one’s own studying, which carried a coefficient of 0.250.

The structural equation models revealed striking explanatory power. The full model accounted for an R-squared value of 0.791 for labour literacy, meaning that the combination of platform support, motivation and self-regulated learning statistically explained nearly 79 percent of the variance in students’ labour literacy scores. Learning motivation itself showed an R-squared of 0.839, and self-regulated learning an R-squared of 0.815, suggesting that the variables in the model capture a tightly interwoven web of educational dynamics rather than isolated factors.

These results align with well-established theoretical frameworks. Self-determination theory, developed by psychologists Edward Deci and Richard Ryan, holds that motivation flourishes when learners experience autonomy, competence and relatedness, conditions that well-designed digital platforms can actively supply through choice, immediate feedback and collaborative tools. Meanwhile, Bandura’s social cognitive theory emphasizes that learners develop self-efficacy by observing models, mastering experiences and receiving constructive feedback, all of which digital environments can deliver at scale. The new findings suggest that when platforms satisfy these psychological needs, they do more than deliver content; they cultivate the motivational and regulatory machinery that students carry into their working lives.

The study’s practical implications are considerable. For universities, the message is that investment in digital infrastructure should not be measured solely by content delivery or user satisfaction metrics, but by whether platforms are designed to foster motivation and self-regulatory skill. Features such as adaptive learning pathways, progress dashboards, goal-setting tools and structured feedback loops are not merely conveniences; according to this model, they are the mechanisms through which technology translates into workforce readiness. The authors recommend that educational institutions and government agencies integrate digital platforms deliberately into labour training programs rather than treating them as supplementary tools.

For policymakers, the findings arrive at a moment when governments are grappling with how to close skills gaps identified in reports from organizations such as QS and Cengage Group, which document persistent mismatches between graduate capabilities and employer expectations. If labour literacy can be strengthened through the motivational and self-regulatory affordances of digital platforms, then national digital education strategies may double as workforce development strategies, a proposition with significant budgetary and curricular implications. The researchers suggest that technology-enhanced labour education could be scaled in ways that traditional classroom-based career preparation cannot easily match.

As with any cross-sectional survey study, caution is warranted in interpreting the direction of causality. The data capture associations at a single point in time, and it remains possible that students who are already more motivated and self-regulated engage more deeply with digital platforms, rather than platforms producing those qualities. Longitudinal and experimental designs would be needed to confirm the causal pathways the model proposes. Nevertheless, the strength of the observed coefficients, the large sample size and the use of structural equation modeling give the findings considerable weight, and the authors note that the anonymized dataset has been deposited in a public Zenodo repository to allow independent verification and reuse.

What makes this study resonate beyond the statistics is its reframing of a familiar debate. Rather than asking whether screens help or harm learning in the abstract, it identifies the specific psychological levers, motivation and self-regulation, through which digital tools appear to shape one of the most consequential outcomes in higher education: whether students leave university genuinely prepared for the world of work. In an era when the half-life of job-specific skills is shrinking and lifelong learning has become an economic necessity, understanding how everyday educational technology builds the deep literacy of labour may prove to be one of the most consequential questions educational science can ask.

Subject of Research: How digital learning platforms enhance university students' labour literacy through learning motivation and self-regulated learning

Article Title: Enhancing students’ labour literacy through digital platform support: the mediating role of learning motivation and self-regulated learning

Article References: Liu, P., & Sumettikoon, P. (2026). Enhancing students’ labour literacy through digital platform support: the mediating role of learning motivation and self-regulated learning. SN Social Sciences, 6(9), Article 385. https://doi.org/10.1007/s43545-026-01661-9

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01661-9

Keywords: labour literacy, digital platforms, learning motivation, self-regulated learning, educational technology, structural equation modeling, higher education, employability, technology-enhanced learning, student engagement, workforce readiness, survey research