university graduates

Graduate employability Outcomes (GEO) is a crucial issue in emerging countries, where skill gaps and unemployment among university graduates are consistently rising. In this context, higher education institutions (HEIs) play a pivotal role in developing a strategic approach to fill the gap through collaboration between academia and industry. Nowadays, every higher education institution in emerging countries has an internal quality assurance cell (IQAC), and they play a critical role in ensuring continuous improvement, stakeholder engagement, and curriculum development. However, despite the growing relevance of this issue, limited empirical studies have been conducted on the mediating role of IQAC in the relationship between industry and academia for confirming graduate employability in an emerging economy, like Bangladesh. The purpose of this study is to examine the mediating role of IQAC, including university curriculum (UC), job scholars’ opportunities (JSO), on the association between university-industry collaboration (UIC), and university GEO in Bangladesh, using partial least squares structural equation modelling (PLS-SEM) under the quantitative research approach. Data were collected using a structured questionnaire from university graduates, faculty members, and administrative staff. The empirical evidence reveals that UIC significantly contributes to GEO; moreover, IQAC plays a substantial mediating role, including UC and JSO. The findings of the study offer practical contributions for higher educational institutions (HEIs), policymakers, and industry experts to continuously improve graduate employability outcomes, such as practical competencies, skills development, and job readiness.

Traditional statistical models face the challenges of insufficient modeling capabilities and difficulty in interpreting complex, nonlinear, and high-dimensional data. Therefore, this paper combines fuzzy logic and deep learning methods to construct a framework model for identifying key factors for high-quality employment for college students. The model in this paper uses multi-channel encoding technology. We use the attention mechanism to fuse the features obtained from multi-channel encoding and input the self-organizing fuzzy inference structure for regression and classification analysis. The system can transform numerical prediction results into a semantic rule-driven decision-making process, which can significantly improve the model’s modeling capabilities and interpretability for uncertainty. Our experiments were conducted on 5,623 graduates. Experimental results on the data show that our model achieved an average mean square error of 0.831, a coefficient of determination of 0.892, and a classification accuracy of 0.865, which are significantly higher than traditional statistical and machine learning models. The corresponding experiments demonstrate the key role of cross-modal fusion and fuzzy reasoning modules in improving the results. Feature importance analysis shows that factors such as internship duration, GPA, and research participation have a key impact on high-quality employment, which is consistent with expert consensus.