Search Results (4)

This study proposes a hybrid artificial intelligence (AI) framework for graduate subject allocation that enhances fairness, transparency, and operational efficiency in higher education institutions. Traditional subject allocation processes are predominantly manual and time-consuming in increasingly complex academic environments. The proposed framework integrates a custom Python-based rule engine for institutional constraint reasoning with advanced deep learning models, including XGBoost, Wide-and-Deep Neural Networks (WDNNs), and Graph Neural Networks (GNNs), to ensure policy-compliant and data-driven subject allocation decisions. Subsequently, a systematic hyperparameter optimization strategy is applied to enhance predictive accuracy and model stability across all architectures. Experimental evaluation demonstrates that the proposed framework significantly improves predictive and ranking performance. The GNNs model achieved the highest results with Accuracy = 0.964, Precision = 0.953, Recall = 0.941, F1-score = 0.947, and AUC = 0.976, outperforming WDNN (Accuracy = 0.956, AUC = 0.972) and XGBoost (Accuracy = 0.934, AUC = 0.942). Ranking effectiveness was also validated with HR@10 = 0.784 and NDCG@10 = 0.622. Feature-importance analysis using SHAP revealed that Digital Pedagogical Competence (12.6%), Research Productivity (10.8%), and Postgraduate Supervision (9.7%) are the most influential factors in allocation decisions. To ensure institutional alignment, a multi-objective reranking mechanism was introduced to balance suitability, workload fairness, research alignment, and diversity. This approach reduced workload variance from 0.26 to 0.18 and improved research–subject alignment by 21%. Overall, the proposed framework provides a scalable, explainable, and data-driven solution for optimizing graduate subject allocation in modern higher education systems. Full article

This study used event-related potentials (ERPs) to examine how candidates’ educational background (elite vs. non-elite universities) and prior internship experience (Fortune 500 vs. non-Fortune 500 enterprises) influence recruitment evaluations. Thirty-two participants completed a 2 × 2 within-subjects design task. Behavioral data indicated that applicants with Fortune 500 internships and graduates from elite universities received higher evaluation scores. ERP results revealed that Fortune 500 experience elicited larger P200 amplitudes (reflecting early attention). Crucially, this effect was modulated by educational background as only candidates from elite universities showed both enhanced P200 and reduced N300 amplitudes (suggesting efficient later processing). These findings indicate that recruiters dynamically allocate attention based on academic prestige (P200) and evaluate semantic congruence between education and employer reputation (N300), providing neurophysiological evidence for educational bias in hiring. Full article

To graduate, pharmacy technician students write a project in their third year. They choose between six elective courses, and work with a subject related to their education and everyday practice at community or hospital pharmacies. In this article, we report the mapping of third-year project themes and provide an overview of the challenges that COVID-19 pandemic restrictions have had on completing the projects. On the basis of all project titles, a list of themes was generated and described before all projects were allocated to one of the themes. Challenges experienced due to the COVID-19 pandemic were investigated from an analytical workshop where supervisors discussed their experience with supervising students throughout the completion of the projects. In total, 140 projects were included and thematised into eight themes: advanced pharmacy services, digital patient support, organisation and collaboration, handling of medicine, automated dose dispensing, medication counselling in community pharmacy, hospital pharmacy, and others, covering all six elective courses. The COVID-19 pandemic affected students’ possibilities to collect data from either physical interviews or observations. The challenges prompted both constructive and creative discussions between students and supervisors to find ways to complete the projects, and required flexibility from all those involved: students, supervisors, community pharmacies, and hospital pharmacies. In conclusion, all students managed to complete their third-year project at a similar level of achievement statistically compared to average grades for the previous six years (2016–2020). Full article

A study on the effectiveness of engineering education in the development of industry-ready graduate engineers was conducted among academics and industry experts of engineering disciplines who have relevant experience in work-integrated learning in Australia. The hypothesis was that embedding enhanced work-integrated learning into all study semesters has the increased possibility of developing industry-ready graduate engineers. This paper outlines the research outcomes and an enhanced work-integrated learning framework that might be helpful for improving the industry-readiness of graduating engineers. Based on the research results, the researchers propose the allocation of an appropriate level of work-integrated learning for each indicator of attainment component from the elements of Engineers Australia’s Stage I Competencies. The aim of this paper is to provide detailed recommendations for implementing an enhanced work-integrated model in Bachelor of Engineering programs in Australia. The authors also present the concept of curriculum development based on industry-integrated learning outcomes, as well as the campus and industry engagement model for enhanced work-integrated learning for the subjects of study in the Bachelor of Engineering program. This framework can be used globally as a reference for developing similar work-integrated learning models. Full article