Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal Emerging Technologies in Education Journal of Social Entrepreneurship and Creative Technology
Green, David
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Education Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Predictive Analytics to Enhance Learning Outcomes: Cases from UK Schools Green, David; Thompson, Emily; Ochieng, Isaac
Journal Emerging Technologies in Education Vol. 3 No. 1 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jete.v3i1.2127

Abstract

Background. The increasing integration of data-driven technologies in education has positioned predictive analytics as a promising tool for enhancing student learning outcomes. In the UK, schools are beginning to leverage predictive models to identify at-risk learners, personalize instruction, and inform pedagogical decisions. Purpose. This study investigates the practical application and impact of predictive analytics in secondary education settings across selected schools in England and Scotland. The primary objective is to assess how predictive tools are used to improve academic performance, engagement, and targeted interventions.Method. A qualitative case study approach was employed, involving interviews with school leaders, data analysts, and teachers in six institutions, alongside document analysis and system usage observations.Results. The findings reveal that predictive analytics, when implemented with pedagogical alignment and ethical oversight, significantly supports early identification of student needs and enables timely academic interventions. However, challenges persist in terms of data literacy among staff, algorithmic transparency, and balancing predictive insights with professional judgment. Conclusion. The study concludes that predictive analytics can enhance learning outcomes when embedded within a holistic educational framework that prioritizes equity, accountability, and human-centered decision-making.
Scaling Social Impact: A Longitudinal Analysis of Sustainable Business Models for Waste Bank Social Enterprises in Urban Indonesia Yuliastuti, Hilda; Patel, Priya; Green, David
Journal of Social Entrepreneurship and Creative Technology Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jseact.v2i5.2662

Abstract

Waste Bank Social Enterprises (WBSEs) are crucial for addressing urban waste challenges in Indonesia but struggle with scalability and financial sustainability. Many fail to move beyond micro-scale operations, limiting their social impact. This study identifies the business model characteristics that enable WBSEs to sustainably scale. It analyzes the evolutionary process of their business models over time. A 36-month longitudinal, mixed-methods study was conducted on twelve urban Indonesian WBSEs. We combined quantitative performance metrics with 72 semi-structured interviews to analyze their scaling trajectories. Findings reveal a stark divergence. Most WBSEs stagnated, trapped by a precarious aggregation-only model. The Rapidly Scaling enterprises were universally differentiated by a strategic pivot: adopting value-adding processing. This transformation allowed them to exit the low-value commodity trap and secure stable, high-value industrial contracts. Sustainable scaling is contingent upon a fundamental business model transformation from a passive collector to an active producer. This evolution from a community project to a market-integrated social enterprise is essential for financial resilience and amplifying social impact.
Co-Authors Ochieng, Isaac Patel, Priya Thompson, Emily Yuliastuti, Hilda