Template-Type: ReDIF-Article 1.0 Author-Name: Yundong Wu Title: A Research Trend Analysis on Teaching Methods for Pre-Service Special Education Teacher Education in China Publication-Status: Published Abstract: This study examines research trends in teaching methods used in pre-service special education teacher education in China. A total of 186 relevant journal articles published between 2000 and 2025 were selected from CNKI and Wanfang Data. The studies were examined from four aspects: publication period, teaching method, research method, and research topic. Frequency and percentage analyses were used, with multiple coding applied to teaching methods when one study covered more than one method; research topics were assigned one primary topic per study. The 12 teaching-method categories generated 260 coding cases. Reflective teaching was the most frequently coded method (42 cases, 16.2%), followed by field-based learning (36, 13.8%) and problem-based learning (31, 11.9%). Qualitative research accounted for 48.9% of the studies, followed by quantitative research (30.1%), mixed methods (14.5%), and literature/conceptual research (6.5%). Curriculum and instructional competence was the most frequently coded primary research topic (36.6%), followed by professional identity and development (21.0%) and practical teaching competence (15.1%). Annualized publication rates indicate that research activity increased across all successive periods, with the highest average rate occurring in 2020–2025. The findings indicate a strong emphasis on instructional competence and practical preparation, while assessment and classroom management, digital teaching competence, and rigorous outcome evaluation remain comparatively underrepresented. Because the review describes research attention rather than intervention effectiveness, the findings should not be interpreted as evidence that one teaching method is more effective than another. Future research should use reproducible search procedures, reliable coding, mixed-method and longitudinal designs, university–school collaboration, and competency-specific outcome measures. Keywords: pre-service special education teachers; teaching methods; teacher education; research trends; special education Journal: Milky Way Scientific Journal Pages: 184-199 Volume: 2 Issue: 3 Year: 2026 Month: September DOI: 10.65021/mwsj.v2.i3.45 File-URL: https://mwsjournal.com/index.php/mws/article/view/45 File-Function: Abstract page File-Format: text/html File-URL: https://mwsjournal.com/index.php/mws/article/download/45/17 File-Function: Full text File-Format: application/pdf Handle: RePEc:gyr:mwsjou:v:2:y:2026:i:3:id:45 Template-Type: ReDIF-Article 1.0 Author-Name: Shivabeerappa M. Author-Name: Priya K. Author-Name: Ruchitha G. Title: Predicting Electric Vehicle Adoption Intention Using Machine Learning: An AI-Driven Framework for Charging Infrastructure Planning in Karnataka, India Publication-Status: Published Abstract: Forecasting electric-vehicle (EV) demand is important for charging-infrastructure planning, but aggregate sales models offer limited insight into which consumers are considering a purchase. The study's methodological contribution is a reproducible, leakage-controlled workflow that applies machine-learning classifiers to primary survey data to predict individual purchase consideration in a secondary-city context, as a complement to aggregate forecasts. A convenience and snowball sample of 175 respondents was drawn from Mysuru and surrounding districts of Karnataka, India; the analysis used the 162 non-EV owners (117 Yes/Maybe; 45 No). Logistic regression and random forest classifiers were compared using stratified five-fold cross-validation. Random forest achieved pooled out-of-fold accuracy of 0.784 (95% bootstrap CI: 0.722–0.846) and ROC-AUC of 0.843 (95% CI: 0.784–0.898), compared with 0.735 and 0.823 for logistic regression; repeated cross-validation gave slightly lower but consistent estimates. Attitude-related measures ranked highest, but their importance is predictive, not causal. Because the sample is small and non-probabilistic, the findings are a methodological proof of concept that requires external validation, not a tool ready for planning use. Keywords: adoption intention; charging infrastructure; electric vehicles; machine learning; random forest Journal: Milky Way Scientific Journal Pages: 200-214 Volume: 2 Issue: 3 Year: 2026 Month: September DOI: 10.65021/mwsj.v2.i3.51 File-URL: https://mwsjournal.com/index.php/mws/article/view/51 File-Function: Abstract page File-Format: text/html File-URL: https://mwsjournal.com/index.php/mws/article/download/51/18 File-Function: Full text File-Format: application/pdf Handle: RePEc:gyr:mwsjou:v:2:y:2026:i:3:id:51 Template-Type: ReDIF-Article 1.0 Author-Name: Ademola Omoniyi Ajibade Title: Beyond Compliance: Rethinking Pedagogy and Creative Epistemologies in Nigerian Postgraduate Education Publication-Status: Published Abstract: Nigerian postgraduate education drives the nation's research capacity, yet it risks pedagogical inertia when quality assurance, including the National Universities Commission's minimum academic standards and accreditation, is reduced to procedural compliance. This paper rethinks postgraduate education by shifting attention toward pedagogies that foster intellectual agency, dialogue, epistemic plurality and context-sensitive knowledge production. Drawing on critical pedagogy, decolonial theory, Ubuntu and Igwebuike, and situated-learning theory, it uses a critical-integrative conceptual method and a purposive review of policy documents and Nigerian, African and international literature. The evidence is strongest for structural constraints on supervision and staff retention and for the Eurocentric orientation of methodological training in African research, but thin or inferential for prevailing pedagogy and examination practice; these claims are therefore advanced as nine testable hypotheses rather than established facts. The paper proposes four connected commitments (relational supervision, epistemic plurality, creative rigour and public relevance), operationalised through the CARE model (Criticality, Agency, Relationality and Epistemic creativity). It argues that standards should be an enabling floor rather than an intellectual ceiling, that resourcing rather than intent is the main obstacle to reform, and that CARE can be adopted through a staged, low-cost, workload-neutral pathway viable for early-career supervisors and under-funded state universities. Keywords: Compliance; Creative epistemologies; Decolonisation; Nigerian postgraduate education; Pedagogy Journal: Milky Way Scientific Journal Pages: 215–240 Volume: 2 Issue: 3 Year: 2026 Month: September DOI: 10.65021/mwsj.v2.i3.58 File-URL: https://mwsjournal.com/index.php/mws/article/view/58 File-Function: Abstract page File-Format: text/html File-URL: https://mwsjournal.com/index.php/mws/article/download/58/19 File-Function: Full text File-Format: application/pdf Handle: RePEc:gyr:mwsjou:v:2:y:2026:i:3:id:58