Sultan Kudarat State University

This work examined the development of amylose-lipid complexes in green banana flour (Musa x paradisiaca) incorporated with virgin coconut oil (VCO), focusing on their spectral, thermal, and in vitro digestibility characteristics. Firstly, the native banana flour was analyzed for apparent amylose content using a spectrophotometric assay. To facilitate amylose-lipid complexation, both hot-pressed and cold-pressed VCO were incorporated into the banana flour under controlled thermal conditions, after which amylose-lipid interactions were characterized using Fourier-transform infrared and Raman spectroscopy for spectral features and differential scanning calorimetry for thermal behavior. The banana flour exhibited an AAC of 26.40 +/- 0.002%. GCMS analysis of FAME derivatized VCO detected medium- to long-chain fatty acids, including octanoic (C8:0), decanoic (C10:0), dodecanoic (C12:0), tetradecanoic (C14:0), and hexadecanoic acids (C16:0) stearic acid (C18:0) and oleic acid (C18:1). FTIR coupled with multivariate analysis and Raman spectra confirmed lipid incorporation/retention in green banana flour through characteristic O-H, C-H, and C=O bands. While DSC revealed distinct endothermic transitions at 89.56 +/- 2.17 degrees C (Delta H-m = 0.8587 +/- 0.1014 J g(-1)) for hot-pressed VCO and 89.18 +/- 0.98 degrees C (Delta H-m = 0.6267 +/- 0.0777 J g(-1)) for cold-pressed VCO, consistent with the melting of V-type amylose-lipid complexes. Morphological analysis revealed that thermal treatment transformed native banana flour from irregular granular structures into an amorphous matrix via starch gelatinization, whereas subsequent incorporation of VCO promoted aggregation. In vitro enzymatic digestion showed a slight reduction in starch hydrolysis in VCO-treated samples. The incorporation of an exogenous lipid, such as VCO, into green banana flour promotes the formation of thermally stable amylose-lipid complexes that reduce enzymatic digestibility.

Higher education across ASEAN is shaped by the interplay of cultural diversity, historical legacies, and evolving leadership demands. Sociocultural norms—such as collectivism, respect for authority, and hierarchical relationships—continue to influence teaching–learning practices, governance structures, and institutional reform efforts. Integrating recent research on leadership models, this chapter examines hierarchical, transformational, distributed, participatory, and community-based approaches, emphasizing how their effectiveness depends on culturally embedded values and organizational contexts. Regional case examples demonstrate that adaptive, culturally responsive, and relational leadership practices foster innovation, inclusivity, and institutional resilience. Attention is also given to cultural tensions in curriculum design, assessment, mobility programs, and academic governance. By foregrounding culturally grounded leadership, the chapter highlights pathways for strengthening equity, collaboration, and sustainable development across ASEAN higher education institutions.

This systematic review examined the challenges faced by Mathematics teachers under the MATATAG curriculum and the documented effects on teaching performance and student academic outcomes in Philippine basic education (2019–2025).Following PRISMA 2020, we searched peer-reviewed journals and reputable sources (e.g., DepEd policy repositories, local academic outlets, and indexed databases) for empirical and policy-relevant studies on (a) MATATAG implementation or closely aligned national math reforms, (b) teacher-level constraints (time/pacing, resources, administrative load, assessment practices, professional development), and (c) outcomes (teaching performance indicators, student mathematics achievement). Records identified: 178 (databases = 142; other sources = 36); duplicates removed: 41; screened: 137; full-texts assessed: 42; studies included: 20.Convergent evidence indicates four persistent constraints: (1) compressed instructional time and pacing pressures (45-minute periods) that limit problem-solving depth and formative assessment cycles; (2) learning-resource gaps (contextualized materials, manipulative, technology) that hinder differentiated instruction; (3) administrative workloads (reporting/compliance) reducing planning and feedback time; and (4) variable access to targeted professional development. Studies linking these constraints to outcomes show (a) lower observation rubric ratings where pacing and materials are inadequate and (b) modest but consistent gains where supports exist (pacing guidance, lesson exemplars, formative assessment tools, and coaching). Comparative evidence suggests public schools face more acute barriers than private schools. The weight of evidence supports system-level supports—refined pacing guidance, resource augmentation, and sustained content-focused PD with coaching—to translate MATATAG goals into higher-quality mathematics instruction and improved learner achievement. Future work should prioritize quasi-experimental and longitudinal designs that jointly track teacher performance metrics and student math outcomes under clearly specified support packages.

Artificial intelligence (AI) is increasingly transforming educational environments by enabling data-driven teaching, learning, assessment, and institutional management. This chapter examines the role of data governance in AI-enabled education systems and the implications of large-scale educational data use. It discusses key AI applications in education, including learning analytics, intelligent tutoring systems, automated assessment tools, and educational data mining, highlighting their potential to support personalized learning, predictive student support, and efficient administrative processes. At the same time, the discussion addresses major ethical and governance challenges such as digital surveillance, algorithmic bias, third-party data sharing, and cybersecurity risks. The chapter further explores governance frameworks, legal and policy considerations, and institutional strategies—such as privacy-by-design, data minimization, and capacity building—to strengthen responsible, transparent, and ethical data practices in AI-driven educational environments.

Inclusive pedagogy, faculty development, and accreditation intersect as key drivers of equity and excellence in higher education. Rooted in frameworks such as Universal Design for Learning (UDL), culturally responsive teaching, and differentiated instruction, inclusive practices extend beyond access to emphasize belonging, engagement, and success. Faculty development equips educators with strategies for accessible curriculum design and flexible assessments, while accreditation frameworks embed inclusivity into quality standards, compelling institutions to demonstrate measurable outcomes. This chapter illustrates how these elements converge through case studies that highlight best practices in accreditation, professional learning, and institutional collaboration. While challenges such as limited resources and resistance to change remain, future directions point to AI-driven personalization, intercultural competencies, and neurodiversity inclusion as vital to shaping sustainable and inclusive higher education systems.