Data-informed Leadership

A Guide to AI-Enabled Data Science Across Education Systems
1. Auflage 2027
ISBN: 978-1-041-02056-1
Verlag: Taylor & Francis Ltd

Buch, Englisch, 192 Seiten, Format (B × H): 152 mm x 229 mm

Data can transform education, but only when it is interpreted responsibly and turned into meaningful action. This work demystifies analytics for anyone seeking to improve teaching, learning, leadership, and institutional performance while safeguarding professional judgement, fairness, privacy, and the interests of every individual learner.

The book guides teachers, school leaders, researchers, and decision-makers through the complete journey from data collection and governance to visualisation, interpretation, communication, and sustained improvement. Accessible chapters introduce data literacy, quality, ethics, regression, clustering, classification, forecasting, spatial analysis, and educational data mining. Practical examples, case studies, decision frameworks, and visual models demonstrate applications in instructional design, student support, assessment, institutional planning, and school leadership. Dedicated coverage of generative and agentic artificial intelligence explores emerging opportunities alongside concerns about assessment integrity, surveillance, bias, transparency, and accountability, enabling readers to evaluate both established analytical methods and rapidly developing technologies confidently and responsibly.

Distinctively human-centred, this book treats analytics not as a substitute for educators’ expertise, but as a disciplined companion to contextual knowledge and stakeholder voice. It equips readers to ask better questions, challenge misleading claims, communicate uncertainty honestly, and transform evidence into ethical, equitable, and sustainable educational action for every learner.

Zielgruppe

Professional Practice & Development and Professional Training

Autoren/Hrsg.

Cheng, Eric C. K.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Sozialwissenschaften Pädagogik Pädagogik Bildungswesen: Organisation und Verwaltung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Sozialwissenschaften Pädagogik Schulen, Schulleitung Schulleitung, Schulentwicklung

Inhaltsverzeichnis

- The Role of Data Analytics in Education and Its Sources

- Data Ethics, Quality, and Communication with Stakeholders

- Navigating, Managing, and Visualising Data in Schools

- Designing Instructional Events Based on Student Data

- Core Methods for Educational Data Mining

- Spatial Data Analytics for School Systems

- Predicting School Performance and Student Outcomes

- Harnessing Generative and Agentic AI for Educational Data Analytics

- School Leadership: Making Data-Informed Decisions

- From Data to Sustained Transformation

Über Autor(innen)

Eric C. K. Cheng is an internationally recognised scholar and higher education leader specialising in educational data analytics, AI-enhanced pedagogy, knowledge management, and teacher education. A Professor at The Education University of Hong Kong, he connects research, ethical innovation, and professional practice to advance educational improvement worldwide.