Daniel Jesayanto Jaya

1. Inclusive education is widely recognised as an essential dimension of high-quality educational provision for disabled and neurodivergent learners, supporting personal development and meaningful ...

Aim: This article examines how Technical and Vocational Education and Training (TVET) can support entrepreneurial career transformation among workers in the construction sector. Context: TVET is commonly associated with employability and technical skill formation, but its role in enabling transitions from wage employment to construction entrepreneurship remains insufficiently theorized. This issue is especially relevant in infrastructure-oriented economies where construction is project-based and closely connected to subcontracting and small-enterprise opportunities. Method: A structured thematic evidence review with translational synthesis was conducted. The core review covered literature published between 2000 and 2024 from Scopus, Web of Science, ERIC, and Google Scholar, supplemented by citation tracking and policy and institutional documents. A targeted update covering January 2025 to June 2026 strengthened the theoretical and international context. The final evidence base comprised 52 publications and institutional sources. Findings: Five interrelated clusters were identified: construction-sector opportunity structures, vocational skill formation, entrepreneurial capability development, institutional and policy scaffolding, and psychological and socio-cultural mediation. These clusters converge into three enabling drivers: vocationally enhanced individual agency, institutional and policy scaffolding, and entrepreneurial ecosystem support. Contribution: The study reconceptualizes TVET as a multi-level career-transformative system and develops a framework explaining how occupational competence can be converted into entrepreneurial pathways. Implications: The framework informs entrepreneurship-integrated curricula, work-based learning, incubation, certification, industry partnerships, and small-enterprise support in Indonesia and comparable Global South and international contexts.

Naming is not a neutral administrative act in higher education. Programme names classify knowledge, organise institutional identity, orient prospective students, and connect curricula to profession...

In one of our statistics lessons at a vocational high school in Pangkep, South Sulawesi, Indonesia, we faced a familiar challenge: how to make abstract clustering algorithms feel real to students. ...

This study applies K-Means clustering to segment job applicant test data from a construction consulting firm to support data-driven screening decisions. From 161 applicants, 30 candidates who met the document-screening requirements were invited for in-person testing and included in the analysis. Three assessment variables were used: AutoCAD drafting skills, planning/supervision report-writing skills, and adaptability. Using R, K-Means clustering was performed to partition candidates into three groups based on multivariate similarity patterns, and the resulting group structure was visualized using 2D and 3D scatter plots. The clustering output revealed distinct competency profiles: one group characterized by generally lower scores across the three variables, a second group with moderate and mixed scores, and a third group with consistently higher scores. Internal validity indices suggested modest separation (mean silhouette = 0.16; Davies–Bouldin Index = 2.05), consistent with exploratory clustering on a small pre-screened sample. These patterns provide a structured interpretation of applicant diversity and can inform practical recruitment actions such as prioritizing candidates for interviews, identifying borderline profiles for additional evaluation, and designing targeted upskilling recommendations for specific competency gaps. Overall, this study illustrates how unsupervised clustering of routine recruitment test results may support more structured interpretation of applicant competency profiles in early-stage construction-sector recruitment, provided that the results are used cautiously alongside professional judgment and further validation.

The integration of generative artificial intelligence into higher education has transformed the ways postgraduate students begin and advance their academic work. However, few studies have explored how AI influences epistemic authority in the development of thesis proposals. This study examines how postgraduate students establish and prioritize AI, peers, and supervisors as epistemic authorities within an AI-mediated academic environment. Using a qualitative interpretative methodology, we gathered data from 105 graduate students through questionnaires and semi-structured interviews. Reflexive thematic analysis unveiled a stratified epistemic hierarchy. AI was seen as an important authority because it could help people think more clearly and generate new ideas, but it had to be checked. Peers acted as relational authorities, giving emotional support and dialogic assistance. Supervisors maintained the highest epistemic status as institutional authorities owing to their evaluative authority, responsibility, and symbolic capital within academic frameworks. AI reduced uncertainty and increased perceived control in the early stages of proposal creation, without undermining supervisory legitimacy. Authority remained grounded in institutional acknowledgment rather than in the rapid dissemination of knowledge. The findings enhance AI-in-education studies by shifting the emphasis from usage frequency to authority configuration, offering insights for AI governance and postgraduate supervision.

Workers in Yogyakarta often wait on half-finished building sites without written contracts, clear schedules or social protection, uncertain whether the coming days will bring paid work or prolonged...

For teacher education students in Indonesia, field experience practice is a rite of passage conducted during the final stage of undergraduate study. In this period, preservice teachers are assigned...