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Developing effective mentoring relationships for early childhood education teachers in Kupang, Indonesia: A bioecological perspective
(The University of Waikato, 2026-09-30) Suwarningsih, Dwi Purwestri Sri; Morrison , Michele; Clarkin-Phillips, Jeanette
Grounded in Bronfenbrenner’s evolving ecological and bioecological theory, this doctoral study examines how mentoring relationships shape the professional learning of early childhood education (ECE) teachers in a decentralised, resource-constrained context in Kupang District, eastern Indonesia. While early ecological formulations (Phase I) emphasised nested environmental systems, and later refinements (Phase II) foregrounded reciprocal interaction, Bronfenbrenner’s (2005) mature bioecological model (Phase III) positioned proximal processes as the primary mechanisms of human development operating across the Process–Person–Context–Time (PPCT) framework. Drawing on this theoretical progression, the study conceptualises mentoring not as a discrete intervention but as a sustained developmental process embedded within multilevel systems. Adopting a constructivist-interpretive paradigm, the research employed a qualitative instrumental case study design. Data were generated through online surveys, semi-structured interviews, and document analysis involving teachers, mentors, district officials, national policymakers, and development partners engaged in the Holistic-Integrative Early Childhood Development (HI-ECD) programme. Guided by the PPCT model, analysis explored how mentoring functioned as a proximal process shaped by individual dispositions, relational dynamics, institutional structures, cultural norms, and shifting policy conditions over time. The findings demonstrate that mentoring was most effective when characterised by sustained, reciprocal dialogue, modelling of classroom practices, and reflective feedback grounded in trust and mutual commitment. Teachers’ professional confidence, pedagogical decision-making, and identity formation were strengthened when mentoring relationships were consistent and relationally secure. However, mentoring outcomes were not determined solely by interpersonal quality. Exosystem and macrosystem influences, including leadership capacity, decentralised governance arrangements, donor transitions, cultural expectations of hierarchy, and fluctuating funding priorities, significantly shaped the continuity and sustainability of mentoring. Over time, mentoring contributed to gradual shifts from teacher-centred to child-centred pedagogy, yet these transformations remained vulnerable to systemic disruption. The study advances three key contributions. First, it reconceptualises mentoring as a bioecological developmental process, demonstrating how professional learning emerges from sustained proximal interactions embedded within layered policy and cultural ecologies. Second, it refines the application of Bronfenbrenner’s Phase III bioecological model in educational research by illustrating how mentoring operates simultaneously across micro-, meso-, exo-, macro-, and chronosystem dimensions. Third, it extends the scholarship on mentoring in Global South contexts by analysing how donor-supported initiatives intersect with decentralised governance structures to influence sustainability and teacher professional identity. The thesis concludes that strengthening ECE mentoring in Indonesia requires systemic coherence across relational, institutional, and policy dimensions. Sustainable mentoring depends not only on high-quality interpersonal processes but also on alignment across systems and continuity over time. These findings offer implications for policymakers, development partners, and educational leaders seeking to embed context-responsive mentoring within decentralised education systems and to strengthen the professionalisation of the early childhood workforce.
Machine learning for data streams with CapyMOA
(Springer, 2026) Sun, Yibin; Gomes, Heitor Murilo; Lee, Anton; Gunasekara, Nuwan; Cassales, Guilherme; Liu, Justin Jia; Heyden, Marco; Cerqueira, Vitor; Bahri, Maroua; Koh, Yun Sing; Pfahringer, Bernhard; Bifet, Albert
The exponential growth of data in recent decades has underscored the need for high-speed, real-time, and adaptive processing in machine learning. Data stream learning provides an effective framework to address this challenge. This article introduces CapyMOA, an open-source library designed specifically for data stream learning, offering powerful tools for building and deploying adaptive ML models. GitHub: https://github.com/adaptive-machine-learning/CapyMOA. Website: https://capymoa.org.
Streaming Isolation Forest
(Springer, 2025) Liu, Justin Jia; Cassales, Guilherme; Liu, Fei T.; Pfahringer, Bernhard; Bifet, Albert
Anomaly detection is crucial to identify unusual patterns in various domains. In particular, continuous and rapid flow creates distinct challenges within streaming data. This paper introduces the Streaming Isolation Forest (SiForest), a novel algorithm that uses isolation principles and reservoir sampling to align the model with current data distributions. SiForest efficiently detects anomalies with minimal computational and memory requirements and dynamically updates its model using a subtree regrowing strategy. Empirical evaluation on twenty-three benchmark datasets demonstrates that SiForest outperforms eight state-of-the-art algorithms in terms of AUC-ROC scores, achieving greater precision and adaptability.
Tools for self- or peer-assessment of interprofessional competencies of healthcare students: A scoping review
(Frontiers, 2024) Brownie, Sharon; Yap, Jia Rong; Blanchard, Denise; Amankwaa, Issac; Pearce, Amy; Sampath, Kesava Kovanur; Yan, Ann-Rong; Andersen, Patrea; Broman, Patrick
Introduction: Healthcare professionals are expected to demonstrate competence in the effective management of chronic disease and long-term health and rehabilitation needs. Care provided by groups of collaborating professionals is currently well recognized as a more effective way to support people living with these conditions than routine, single-profession clinical encounters. Clinical learning contexts provide hands-on opportunities to develop the interprofessional competencies essential for health professional students in training; however, suitable assessment tools are needed to support student attainment of interprofessional competencies with self-assessment espoused as an important component of learning. Method: A structured approach was taken to locate and review existing tools used for the self-assessment and peer assessment of students’ competencies relevant to interprofessional practice. Results: A range of self- and/or peer assessment approaches are available, including formally structured tools and less structured processes inclusive of focus groups and reflection. Discussion: The identified tools will usefully inform discussion regarding interprofessional competency self- and peer assessment options by healthcare students participating in a broad range of clinical learning contexts. Conclusion: Self- and/or peer assessment is a useful approach for those seeking to effectively enhance interprofessional learning and measure the attainment of related competencies.
Adaptive Isolation Forest
(Springer, 2025) Liu, Justin Jia; Cassales, Guilherme; Liu, Fei T.; Pfahringer, Bernhard; Bifet, Albert
Anomaly detection in real-world data streams often struggles with concept drift, where evolving data distributions challenge algorithms to maintain a balance between accuracy, speed, stability, and plasticity. We present Adaptive Isolation Forest (AIF), a novel anomaly detection algorithm designed to effectively adapt to such changes in a resource-efficient and balanced manner. AIF’s novelty can be found in the combination of a smart model update mechanism together with a newly developed MinTreeMaxMass (MTMM) criterion, which scores individual trees for replacement. Extensive evaluations on various benchmark datasets demonstrate that AIF significantly outperforms existing state-of-the-art streaming anomaly detection algorithms in terms of detection accuracy. Moreover, AIF achieves linear time and space complexities, providing a robust solution that maintains high accuracy and efficiency, balancing stability and plasticity in dynamic data streams.