Encyclopedia:
From Scholars for Scholars

The University Student Engagement Inventory (USEI) is a multidimensional self-report instrument designed to assess student engagement in higher education through three interrelated dimensions: behavioral, emotional, and cognitive engagement. Developed within a hierarchical framework, the USEI provides both dimension-specific scores and an overall indicator of student engagement, supporting research, institutional assessment, and educational quality evaluation across diverse cultural and educational contexts. Student engagement is widely recognized as a key determinant of academic success, persistence, and educational quality in higher education. The University Student Engagement Inventory (USEI) was developed to provide a theoretically grounded and psychometrically sound measure of engagement that captures students’ behavioral, emotional, and cognitive involvement in learning activities. This encyclopedia entry reviews the conceptual foundations, development process, factorial structure, psychometric properties, and applications of the USEI. Evidence from multiple studies indicates that the instrument exhibits satisfactory reliability, construct validity, predictive validity, and measurement invariance across different countries, languages, and educational settings. The USEI has been translated and adapted for use in diverse cultural contexts and has been employed extensively in research examining academic performance, well-being, persistence, and learning environments, including online and blended education. Its brevity, multidimensional structure, and cross-cultural validation have supported its use in educational research and institutional assessment. Overall, the USEI represents a robust and versatile instrument for evaluating student engagement and supporting evidence-based improvements in higher education.

Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains, enabling identity fraud, disinformation campaigns, and evidence fabrication. In high-stakes environments, ranging from journalism and finance to healthcare and legal contexts, the consequences extend to severe misinformation, market manipulation, identity fraud, and the erosion of institutional trust. This entry explores how modern visual intelligence and computer-vision techniques are used to detect deepfakes. It outlines key deepfake generation models, such as GANs, autoencoders, neural rendering, and diffusion systems, while also explaining how adversarial methods enhance realism and challenge existing detectors. The overview highlights visual artifacts, digital patterns, and physiological cues commonly leveraged in detection and reviews major CNN, transformer, and frequency-based approaches. It also summarizes evaluation practices and the difficulty of achieving strong generalization. Finally, it identifies emerging directions, including modern intelligence techniques for civilian and military content verification. This survey covers generation architectures (GANs, latent diffusion, neural rendering, video synthesis), the spatial, temporal, frequency-domain, and physiological artifacts they produce, and the detector families that exploit them. We examine evaluation benchmarks and protocols, highlighting cross-generator generalization as the field’s central open challenge. Beyond detection, we discuss cryptographic provenance standards, watermarking, and regulatory frameworks (EU AI Act, DSA, GDPR). We conclude that effective deepfake governance requires defense in depth integrating forensic detection, verifiable provenance, and institutional accountability.