Research Trends, Methodological Patterns, and Gaps in Faculty Publications at a Center of Excellence in Teacher Education in Northern Philippines

Research Trends, Methodological Patterns, and Gaps in Faculty Publications at a Center of Excellence in Teacher Education in Northern Philippines | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research Trends, Methodological Patterns, and Gaps in Faculty Publications at a Center of Excellence in Teacher Education in Northern Philippines Leonardo Daquioag Tejano This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8552880/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines research trends, methodological patterns, and systematic gaps in faculty publications from a designated Center of Excellence (COE) in Teacher Education in the Philippines, employing systematic content analysis across multiple analytical dimensions. The research addresses a critical gap in understanding institutional research portfolios within the context of COE designation criteria and national research productivity challenges. Analysis revealed concentration in Science Education and Language Education, with predominant use of quantitative and mixed-methods approaches. Significant methodological gaps included limited sample sizes, heavy geographic concentration in just one province, overwhelming reliance on cross-sectional designs, and absence of true experimental studies with randomized control groups. These patterns align with documented national challenges where Philippine research productivity ranks below ASEAN neighbors with faculty facing heavy teaching loads, insufficient incentives, and limited research skills. Thematic analysis identified ten distinct research clusters, with Digital Transformation and Laboratory-Based Science Education as dominant themes. Gap analysis categorized eight types of systematic limitations, including sample constraints, temporal limitations, geographic restrictions, and underexploration of emerging technologies. Findings have direct implications for COE sustainability, faculty development programming, and institutional research policy. The study contributes methodologically rigorous, context-specific analysis to the limited literature on teacher education research patterns in Southeast Asian contexts, providing evidence-based recommendations for strengthening research portfolios through longitudinal designs, multi-site collaborations, enhanced faculty capacity building, and strategic focus on emerging educational technologies including artificial intelligence. teacher education research research productivity center of excellence faculty publications Philippine higher education Introduction Teacher education institutions serve as critical nodes in education systems, simultaneously preparing future educators and generating knowledge through systematic research and scholarship (Cochran-Smith & Villegas, 2015). The quality and relevance of research produced by these institutions directly influences evidence-based practice, policy formation, and responses to contextual educational challenges (Zeichner, 2009). In the Philippine context, the Teacher Education Council has established a comprehensive framework for identifying and designating Centers of Excellence (COEs) in teacher education. This framework emphasizes research productivity, faculty qualifications, curriculum quality, and institutional capacity as central criteria for designation and sustained recognition (Teacher Education Council, 2025). Recent mapping studies of excellence in teacher education reveal that while COEs generally demonstrate superior performance in licensure examinations, significant regional disparities exist in distribution, with concentration in the National Capital Region, and persistent tensions between compliance-focused and impact-focused approaches to excellence (Sinsay-Villanueva et al., 2024). Philippine higher education institutions face well-documented challenges in research productivity that contextualize any institutional analysis. Vinluan's (2011) seminal bibliometric analysis comparing Philippine research output in education and psychology with ASEAN neighbors revealed that the Philippines ranks consistently low in research productivity, particularly since the 1990s, compared to Singapore, Thailand, Malaysia, and Indonesia. The analysis documented that a small cadre of researchers from limited institutions contribute the majority of publications, with concerning patterns of low citation counts and publication in journals with low impact factors. Contributing factors identified include economic constraints, insufficient research funding, local focus in social sciences research limiting international visibility, characteristics of researchers including heavy teaching loads, and epistemic culture related to knowledge production. Notably, collaboration rates—both domestic and international—remain markedly low compared to regional counterparts. Recent empirical studies have further documented these challenges across Philippine higher education institutions. Research examining faculty productivity at Central Bicol State University-Calabanga Campus (2020-2022) found that only 25.6% of approved research proposals resulted in completed projects, with merely 5.1% published and 1.7% cited, categorizing overall productivity between very low and low levels across colleges (Palmiano, 2024). Systematic investigation of deterrents to research pursuits among 90 university faculty in Northern Philippines identified significant barriers in both personal (M=4.99) and professional (M=4.92) domains, with predominant issues including lack of time, excessive teaching loads, insufficient incentives, and inadequate research skills, with no significant demographic differences across age, gender, or employment status (Landingin et. al., 2024). Faculty competency assessments consistently reveal average research competency at practitioner level rather than expert level, with most faculty reporting 'little to some knowledge' in crucial areas including conceptualizing research problems, designing studies, and writing scholarly papers (Rodriguez et. al., 2021; (Gacrama and Baptista, 2019). Studies of Higher Education Institutions in Region 8 found research capability perceived as moderate, with research often viewed as additional burden rather than integral function, and persistent inadequacies in funding and institutional support (Villarino, 2025). Understanding institutional research patterns requires attention to broader methodological trends in educational research. Systematic review of 80 empirical studies on teacher leadership (2018-2022) identified predominance of qualitative methods with focus on K-12 educational settings in North America and Asia, though consensus on core construct definitions remains elusive, with definitions varying by context and school culture (Li et al., 2024). Analysis of 23 empirical studies in educational leadership from 11 peer-reviewed journals (2016-2019) documented notable shifts toward mixed methods and case study designs, with interviews and surveys as predominant data collection methods, average sample sizes of 30 for qualitative and 400 for quantitative studies, and content analysis and thematic analysis as primary techniques for qualitative data interpretation (Karimi & Khawaja, 2023). Analysis of 454 teacher education articles published in SSCI Q1 indexed journals (2024) revealed nine major research themes with 'teacher professional development' most prominent, qualitative research methods predominant at 58.81%, and teacher-centered perspectives overshadowing voices of other stakeholders including teacher educators and K-12 students (Karataş, 2025). These international patterns provide crucial comparative context for understanding methodological choices and research foci in Philippine teacher education research. Systematic identification of research gaps constitutes essential scholarship for advancing fields and informing strategic research planning. Chand's (2015) framework for identifying research gaps emphasizes methodical approaches including citation analysis to recognize prevalent issues through studying cited works, content analysis for extracting and analyzing themes from qualitative data, meta-analysis for synthesizing results across multiple studies, systematic reviews for comprehensive overview of current research distinguishing unaddressed questions, and examination of future research suggestions and limitations within existing studies. Application of such frameworks to institutional research portfolios enables identification of systematic patterns requiring strategic response rather than merely documenting individual study limitations. The scoping review methodology applied to critical thinking research in the Philippines (1971-2017) demonstrates the value of systematic gap analysis, revealing predominant emphasis on ability over disposition, significant lack of studies at kindergarten and elementary levels despite importance of early cultivation, and limited exploration of infusion and mixed teaching strategies (Lopez et al., 2023). Research capacity building emerges as critical intervention for addressing productivity challenges. Assessment of research training needs among 70 basic education teachers at University of Saint Louis (2023-2024) found all identified training elements perceived as 'Very Important,' with particular emphasis on using digital tools in research, data analysis and interpretation, and developing research designs and methods (Tinduwen & Baquitaran, 2024). Studies of teacher educator productivity patterns reveal that professional characteristics including years of service, educational attainment, faculty rank, research teaching experience, and seminar participation significantly influence research output, with educators possessing advanced degrees and higher ranks generally producing more research, while those with limited teaching experience and fewer seminar participations show lower output (Amanonce et al., 2025). These findings underscore necessity for targeted faculty development initiatives addressing specific competency gaps and professional characteristics. Emerging educational technologies, particularly artificial intelligence, represent rapidly evolving research domains with implications for teacher education. Bibliometric analysis of AI integration in pre-service teacher education (2020-2025) reveals substantial publication increases following emergence of generative AI tools like ChatGPT, with Germany and Finland leading in citation impact while China demonstrates high volume but lower citation density (Kuzu, 2025). Central themes include generative AI applications and evolving AI-TPACK (Technological Pedagogical Content Knowledge) framework, highlighting paradigm shifts in educational technology models. However, persistent gaps exist between theoretical AI concepts and practical implementation in educational settings, requiring research bridging this divide. Analysis of world-class research universities emphasizes strategic focus, quality faculty hiring from prestigious institutions, internationalization fostering collaborations, and robust research infrastructure as key success factors, providing benchmarks for institutional development aspirations (Altbach and Salmi, 2011). Despite recognition of research importance in teacher education and documented national challenges, systematic analyses of institutional research portfolios within Philippine COEs remain limited. Most existing studies focus on productivity metrics—publication counts, citation rates, completion percentages—rather than comprehensive examination of research patterns, methodological approaches, thematic coherence, and systematic gaps. Understanding not merely how much research is produced, but what research is conducted, how it is conducted, what patterns emerge, and what gaps persist, becomes essential for strategic planning aligned with COE criteria and for developing targeted interventions to strengthen research capacity and impact. This study addresses that gap by providing rigorous, systematic content analysis of 40 faculty publications from a designated COE in Northern Luzon, examining research areas, methodological patterns, sample characteristics, geographic scope, thematic clusters, and systematic limitations. The study addresses six research questions: What are the dominant research areas and thematic patterns in faculty publications from the Center of Excellence in Teacher Education? What methodological approaches and research designs are employed, and how do they compare with documented national and international trends? What are the characteristics of research samples in terms of size, type, and geographic distribution? What thematic clusters emerge across the research portfolio, and what coherence do they demonstrate? What systematic research gaps and methodological limitations are identified across studies? What evidence-based recommendations can strengthen the research portfolio in alignment with COE designation criteria and national research development priorities? Methodology Research Design This study employed systematic content analysis as the primary research design, appropriate for examining manifest and latent content in communication materials including research publications (Krippendorff, 2018; Neuendorf, 2017). Content analysis enables systematic, objective, and quantitative description of content while also facilitating qualitative interpretation of themes and patterns (Mayring, 2014). The approach combines quantitative dimensions—frequencies, distributions, percentages—with qualitative analysis of thematic content, methodological approaches, and identified gaps, providing comprehensive understanding of the research portfolio. The analytical framework was guided by established dimensions for evaluating educational research (American Educational Research Association, 2006) and frameworks for systematic gap identification (Chand, 2015). Six primary analytical dimensions structured the investigation: (a) research focus and thematic areas, employing both deductive coding from established taxonomies and inductive coding allowing themes to emerge from data (Becher & Trowler, 2001); (b) methodological approaches and research designs, categorized as quantitative, qualitative, or mixed-methods with specification of specific designs following patterns in educational leadership research (Karimi & Khawaja, 2023) (c) sample characteristics including size, type, and composition; (d) geographic scope and contextual settings; (e) key findings and contributions to knowledge; and (f) identified limitations and research gaps explicitly acknowledged by authors or evident through systematic analysis. This multidimensional approach enabled both breadth—comprehensive coverage of multiple analytical dimensions—and depth—detailed examination of patterns within each dimension. The design aligns with recommendations for systematic content analysis in educational research (Mayring, 2014) while incorporating domain-specific frameworks from teacher education scholarship (Cochran-Smith & Villegas, 2015; Zeichner, 2009). Data Sources and Selection Criteria The data corpus consisted of 40 research publications authored or co-authored by faculty members affiliated with Mariano Marcos State University, a designated Center of Excellence in Teacher Education in Northern Luzon, Philippines. Publications were obtained from the institutional repository and represented peer-reviewed journal articles published from January to December 2025. Selection criteria ensured corpus representativeness and analytical validity. Inclusion criteria specified: (a) primary authorship or significant co-authorship by faculty affiliated with the institution's Teacher Education programs; (b) substantive focus on teacher education or directly related educational topics including curriculum, pedagogy, educational psychology, or educational technology; (c) completion and formal publication status in peer-reviewed journals or conference proceedings; (d) availability of full text enabling comprehensive content analysis; and (e) publication from January to December 2025 ensuring recency and relevance to current COE designation criteria. Exclusion criteria eliminated: (a) non-peer-reviewed materials including institutional reports or working papers; (b) publications where institutional affiliation was unclear or tangential; (c) duplicate publications or substantially overlapping content; and (d) publications in languages other than English or Filipino precluding systematic analysis by the research team. The resulting corpus of 40 publications represented the complete accessible population of recent faculty research output meeting these criteria, rather than a sample. This approach enabled comprehensive institutional analysis without sampling bias, though it constrains generalizability to this specific institutional context during this specific time period—a limitation acknowledged and addressed through comparison with documented national and international patterns. Each publication had been converted to markdown format as part of institutional digital repository processes, with comprehensive metadata extracted including titles, authors, publication venues, abstracts, methodological descriptions, sample characteristics, geographic settings, key findings, conclusions, and authors' identified limitations. This pre-processing facilitated systematic extraction and coding while maintaining fidelity to original content. Data Analysis Procedures Data analysis proceeded through four distinct but interconnected phases following established protocols for systematic content analysis (Braun & Clarke, 2006; Krippendorff, 2018; Mayring, 2014). Each phase employed specific analytical techniques with attention to validity, reliability, and transparency. Phase 1: Structured Information Extraction. A standardized extraction framework was developed specifying variables to be systematically captured from each publication: bibliographic details (title, authors, year, publication venue); research area classification (primary and secondary); research sub-topic or specific focus; methodological approach (quantitative, qualitative, mixed-methods) with rationale for classification; specific research design (experimental, quasi-experimental, correlational, descriptive, case study, ethnographic, etc.); participant characteristics (type, role, educational level); sample size with specification of sampling approach; geographic location and setting characteristics; key findings summarized in 2-3 sentences; main recommendations for policy or practice; and identified research gaps or limitations explicitly stated by authors. Extraction was conducted independently by two researchers with discrepancies resolved through discussion and reference to original texts, establishing intercoder reliability. Variables were extracted into structured database enabling systematic comparison and quantitative analysis. Phase 2: Categorical Analysis and Coding. Extracted data were systematically coded across multiple dimensions. Research areas employed hybrid coding combining deductive categories from established taxonomies of educational research (Becher & Trowler, 2001) with inductive categories emerging from the specific corpus. This approach balanced theoretical grounding with openness to context-specific patterns. Initial deductive categories included Science Education, Language Education, Mathematics Education, Teacher Education, Educational Technology, and Curriculum Studies. Additional categories emerged inductively including Cultural Studies, Community Safety, and Student Welfare. Methodological approaches followed standard categorization (quantitative, qualitative, mixed-methods) with subcategorization of specific designs within each approach. Sample sizes were categorized following patterns observed in educational leadership research (Karimi & Khawaja, 2023): very small (<20), small (20-49), medium (50-99), large (100-199), and very large (200+), with additional category for unspecified samples. Geographic locations were initially coded as stated, then grouped into broader categories: MMSU main campus, other Ilocos Norte schools, other Philippine locations, international, and unspecified. All coding decisions were documented with rationales, and coding scheme was iteratively refined through constant comparison. Phase 3: Thematic Analysis. Thematic analysis (Braun & Clarke, 2006) identified cross-cutting themes and patterns transcending individual research area classifications. This involved: familiarization through repeated reading of extracted data and original texts; initial code generation identifying interesting features systematically across dataset; searching for themes by collating codes into potential themes; reviewing themes against coded extracts and entire dataset; defining and naming themes with clear definitions and scope; and producing thematic analysis report. Ten major thematic clusters emerged representing coherent areas of sustained scholarly focus. Theme identification emphasized both semantic themes (explicit surface meanings) and latent themes (underlying ideas and conceptualizations). Themes were validated through multiple passes through data and checking for internal homogeneity (coherence within themes) and external heterogeneity (clear distinctions between themes). Phase 4: Systematic Gap Analysis. Research gaps and limitations were analyzed at two levels: (a) explicit gaps—limitations directly acknowledged by authors in discussion or conclusion sections; and (b) systematic gaps—patterns evident through comparative analysis of methodological approaches, sample characteristics, and research designs. Gap categories emerged inductively from data and were organized into broader types: methodological gaps (sample size limitations, absence of control groups, cross-sectional designs, self-reported data limitations); contextual gaps (geographic concentration, single-site limitations, limited diversity); temporal gaps (short-term interventions, lack of follow-up, absence of longitudinal tracking); topical gaps (underexplored content areas, emerging technologies not addressed); and implementation gaps (limited attention to fidelity, scalability, or sustainability). Frequency of each gap type was calculated to identify systematic patterns requiring institutional response. Throughout all phases, attention to validity and reliability employed multiple strategies: systematic procedures documented through audit trail; dual coding with intercoder reliability checks on subset of data; constant comparison ensuring coding consistency; member checking where researchers validated interpretations against original texts; triangulation across data sources and analytical methods; and reflexivity acknowledging researchers' positionality as members of the institution being studied, addressed through explicit criteria and systematic procedures. Statistical Analysis Quantitative dimensions employed descriptive statistics including frequencies, percentages, means, and ranges calculated using Python programming language (version 3.11) with pandas library (version 2.0) for data manipulation and analysis. Cross-tabulations examined relationships between variables including research area and methodology, sample size and research design, and geographic location and research focus. Results were organized into comprehensive tables facilitating interpretation and comparison. While inferential statistics were considered, the corpus represented complete population rather than sample, making inferential techniques unnecessary for addressing research questions focused on describing institutional patterns rather than generalizing to broader populations. Ethical Considerations The study analyzed published research already in public domain, eliminating concerns about confidentiality or informed consent for data collection. However, researchers attended to ethical dimensions of institutional research. Analysis focused on aggregate patterns rather than critiquing individual studies or researchers. Interpretations were grounded in systematic evidence rather than subjective judgments. Findings emphasize institutional patterns and systemic issues rather than attributing limitations to individual researchers, recognizing that faculty work within structural constraints including heavy teaching loads, limited resources, and institutional priorities. Recommendations focus on institutional responses and system-level improvements rather than individual remediation. Researchers acknowledged positionality as institutional members with vested interest in positive portrayal, addressed through systematic procedures, explicit criteria, and attention to both strengths and limitations. Results Results are organized according to the six research questions, presenting findings from systematic content analysis of 40 faculty publications. Tables present quantitative distributions while narrative text provides interpretive context and highlights patterns warranting attention. Research Areas and Thematic Distribution Analysis identified 20 distinct research areas represented across the 40 publications, with marked concentration in specific domains. Table 1 presents the distribution ranked by frequency. Science Education emerged as dominant focus with 10 studies (25.0% of portfolio), more than double the next most frequent area. Language Education followed with 5 studies (12.5%), then Teacher Education with 3 studies (7.5%). Four areas—Early Childhood Education, Educational Technology, Technology Integration, Cultural Studies, and Teacher Professional Development—each comprised 2 studies (5.0% each). The remaining 12 areas each had single studies, collectively representing 30.0% of the portfolio. Within Science Education, laboratory-based instruction dominated with 6 studies examining laboratory competence, resource availability, teaching approaches, challenges, manual development, and training programs. Physics teaching comprised 4 studies investigating communicative difficulties, strategic barriers, and localized curriculum materials. This concentration reflects institutional priorities in STEM education and faculty expertise in science disciplines, aligning with national emphases despite persistent challenges in Philippine science laboratory instruction (Ganal & Guiab, 2014). Language Education research concentrated on literacy development across multilingual contexts characteristic of Philippine education, where students navigate mother tongue, Filipino, and English. Studies addressed sight word recognition, spelling skills enhancement, reading comprehension strategies, oral language teaching competence, and mastery of English collocations, reflecting policy frameworks emphasizing mother tongue-based multilingual education. The portfolio demonstrates diversity with 20 distinct areas, yet also reveals concentration with top three areas comprising 42.5% of output and top eight comprising 60% of output. Twelve areas represented by single studies suggest either exploratory investigations without sustained programs of research, or emerging interests not yet developed into coherent research agendas. This pattern contrasts with recommendations for sustained research programs enabling cumulative knowledge building (Menter, et. al., 2011). Table 1. Distribution of Research Areas in Faculty Publications (N=40) Rank Research Area No. of Studies Percentage Representative Sub-topics 1 Science Education 10 25.0 Laboratory instruction (6), Physics teaching (4), Localized materials (2) 2 Language Education 5 12.5 Sight words, Spelling skills, Reading comprehension, Oral language, Collocations 3 Teacher Education 3 7.5 Online teaching challenges, Interactive lectures, Transversal skills 4 Early Childhood Education 2 5.0 Fine motor skills (scissors), Student engagement (play-based) 4 Educational Technology 2 5.0 Digital assessment tools, Alternative delivery modes 4 Technology Integration 2 5.0 Augmented reality in fashion education 4 Cultural Studies 2 5.0 Heritage tourism (Tumba Festival), Migration songs 4 Teacher Professional Development 2 5.0 Onboarding programs, Laboratory training (SCILAW) 9-20 Other 12 areas (1 study each) 12 30.0 Physical Education, Film Studies, Community Safety, Social Studies, etc. Methodological Approaches and Research Designs Methodological analysis revealed predominant use of quantitative and mixed-methods approaches. Table 2 presents the distribution of methodologies employed. Quantitative methods were most prevalent with 16 studies (40.0%), followed by mixed-methods with 14 studies (35.0%), qualitative methods with 9 studies (22.5%), and one review article (2.5%). Within quantitative studies, quasi-experimental designs with pre-test/post-test comparisons dominated with 9 studies, followed by descriptive-correlational designs (4 studies) and survey research (3 studies). Notably, no true experimental studies employed random assignment to treatment and control groups, limiting capacity for causal inference. Most studies labeled 'experimental' were actually quasi-experimental, lacking random assignment and often lacking control groups entirely. Mixed-methods studies typically combined surveys or standardized assessments with qualitative interviews, observations, or reflective journals. Seven studies used survey-plus-interview designs, five employed pre-test/post-test with observations, and two utilized multiple integrated methods. However, many studies claiming mixed-methods employed sequential rather than integrated designs, with qualitative data supplementing rather than being genuinely integrated with quantitative findings. Qualitative studies employed thematic analysis (4 studies), semi-structured interviews (3 studies), and ethnographic approaches (2 studies). These tended to be smaller-scale investigations with purposive sampling focusing on depth of understanding rather than breadth of generalization. This distribution differs markedly from international patterns where qualitative methods dominated at 58.81% in 2024 SSCI teacher education publications (Karataş, 2025) and in systematic review of teacher leadership research 2018-2022 (Li et al., 2024). The institutional preference for quantitative and mixed-methods may reflect: (a) emphasis on measurable outcomes aligned with COE criteria; (b) faculty training backgrounds in quantitative methods; (c) perception that quantitative research carries greater legitimacy; or (d) availability of statistical analysis support. However, the shift toward mixed methods aligns with documented trends in educational leadership research 2016-2019 (Karimi & Khawaja, 2023). Table 2. Distribution of Methodological Approaches and Research Designs (N=40) Rank Methodology No. of Studies Percentage Common Research Designs 1 Quantitative 16 40.0 Quasi-experimental (9), Descriptive-correlational (4), Survey (3) 2 Mixed-Methods 14 35.0 Survey+interviews (7), Pre/post-test+observations (5), Multiple methods (2) 3 Qualitative 9 22.5 Thematic analysis (4), Semi-structured interviews (3), Ethnographic (2) 4 Review Article 1 2.5 Literature review Sample Characteristics and Geographic Distribution Analysis of sample characteristics revealed concerning patterns regarding size, diversity, and geographic concentration. Table 3 presents sample size distribution across studies. Among studies with specified sample sizes (N=28), 62.5% employed fewer than 50 participants (combining very small and small categories: 9+7=16 studies). Only 17.5% utilized samples exceeding 100 participants (combining large and very large: 5+2=7 studies). Twelve studies (30.0%) did not specify sample sizes, typically qualitative studies, document analyses, or reviews where traditional sampling frameworks do not apply. Small sample sizes raise concerns about statistical power and generalizability (Button et al., 2013). Effect sizes detected in small samples are often overestimated and may not replicate in larger populations. For quantitative and mixed-methods studies making inferential claims, samples below 30 lack adequate power for detecting effects unless very large, while samples below 50 remain underpowered for most educational interventions. This pattern aligns with documented challenges in Philippine HEI research where moderate research capability and resource constraints limit sample diversity (Villarino, 2025). Table 3. Distribution of Sample Sizes in Research Studies (N=40) Rank Sample Size Category No. of Studies Percentage Typical Study Types 1 Not Specified 12 30.0 Qualitative studies, document analysis, review 2 Very Small (<20) 9 22.5 Pilot studies, small group interventions, case studies 3 Small (20-49) 7 17.5 Classroom-based interventions, single-section studies 4 Medium (50-99) 5 12.5 Grade-level cohorts, multi-section studies 5 Large (100-199) 5 12.5 School-wide or division surveys 6 Very Large (200+) 2 5.0 Division-wide large-scale surveys Geographic analysis revealed striking concentration. Table 4 presents distribution of research sites. Geographic concentration in Ilocos Norte was substantial with 72.5% of studies conducted in the province (combining categories 1 and 2: 19+10=29 studies). Within this, 47.5% were in other Ilocos Norte schools while 25.0% were at MMSU main campus. Only 12.5% had broader 'Philippines (General)' scope, typically review articles or policy analyses. Another 12.5% were classified as 'Other/Not Specified.' This concentration reflects patterns documented in mapping excellence studies revealing regional disparities in COE distribution with concentration in National Capital Region (Sinsay-Villanueva et al., 2024). While geographic concentration enables deep contextual knowledge and sustained partnerships facilitating research access and intervention implementation, it significantly limits generalizability to other Philippine contexts with different linguistic communities, cultural characteristics, and socioeconomic conditions. Table 4. Geographic Distribution of Research Studies (N=40) Rank Geographic Location No. of Studies Percentage Setting Types 1 Ilocos Norte (other schools) 19 47.5 Public schools, colleges, universities 2 MMSU 10 25.0 Laboratory school, university programs 3 Philippines (General) 5 12.5 Policy analysis, national reviews 3 Other/Not Specified 5 12.5 Varied or unspecified locations 5 Laoag/Batac City specific 1 2.5 Urban school division Thematic Patterns and Research Clusters Thematic analysis identified ten major research clusters transcending individual research area classifications. Table 5 presents these patterns ranked by frequency. Two themes tied for dominance: Digital Transformation & Technology (6 studies, 15.0%) and Laboratory-Based Science Education (6 studies, 15.0%), followed by Language & Literacy Development (5 studies, 12.5%). Digital Transformation prominence reflects institutional response to pandemic-driven shifts in educational delivery (Toquero, 2020), encompassing augmented reality applications, online teaching challenges, digital assessment tools, microlearning innovations, and alternative delivery modes. Laboratory-Based Science Education demonstrates sustained focus addressing documented challenges in Philippine STEM education. These coherent clusters suggest intellectual communities with sustained research programs rather than isolated one-off studies. Table 5. Thematic Research Patterns and Clusters (N=40) Rank Thematic Cluster Studies % Representative Topics 1 Digital Transformation & Technology 6 15.0 AR, online teaching, digital assessment, microlearning, ADMs 1 Laboratory-Based Science Education 6 15.0 Lab competence, resources, approaches, challenges, training 3 Language & Literacy Development 5 12.5 Sight words, spelling, reading, oral language, collocations 4 Physics Teaching Challenges 4 10.0 Communication barriers, localized materials, strategic approaches 4 Innovative Pedagogies 4 10.0 Flip-J, SkIT, Motor imagery, Gamification (Lexis-Spell) 4 Teacher Competence Assessment 4 10.0 Onboarding, food hygiene, laboratory skills, oral language 7 Early Childhood Development 3 7.5 Scissors skills, engagement, play-based activities 7 Inclusive & Multicultural Education 3 7.5 Foreign students, inclusive attitudes, multilingual literacy 7 Student Support & Welfare 3 7.5 COVID-19 impact, parental competence, transition support 10 Community & Cultural Studies 2 5.0 Electrical safety, heritage tourism, film analysis, migration Systematic Research Gaps and Limitations Systematic gap analysis identified eight categories of limitations explicitly acknowledged by authors or evident through comparative analysis. Table 6 presents gaps ranked by frequency of occurrence across the 40 studies. Limited sample sizes emerged as most prevalent gap, explicitly identified in 62.5% of studies. Cross-sectional designs represented second most frequent limitation at 57.5%, with authors acknowledging inability to assess developmental trajectories or sustained intervention effects. Geographic concentration was noted in 47.5% of studies. These high-frequency gaps suggest systematic institutional patterns rather than isolated deficiencies, warranting strategic institutional response rather than merely individual study improvements. Table 6. Categories of Research Gaps and Limitations Across Studies (N=40) Rank Gap Category Studies % Description and Impact 1 Limited Sample Sizes 25 62.5 Small samples constrain statistical power and generalizability 2 Cross-Sectional Designs 23 57.5 Single time-point data prevents assessment of sustained effects 3 Geographic Concentration 19 47.5 Ilocos Norte focus limits broader applicability 4 Control Group Absence 15 37.5 Difficult to attribute outcomes solely to interventions 5 Self-Reported Data Reliance 12 30.0 Survey data without observational validation risks bias 6 Emerging Technology Gap 8 20.0 AI, VR, learning analytics underexplored 7 Implementation Fidelity 6 15.0 Limited discussion of intervention consistency 8 Cost-Effectiveness Missing 5 12.5 Sustainability and scalability rarely considered Discussion Research Portfolio in National and International Context The research portfolio must be interpreted within documented patterns of Philippine research productivity. Vinluan's (2011) bibliometric analysis established that Philippine research output in education and psychology ranks consistently low compared to ASEAN neighbors, with limited researchers from few institutions contributing most publications, concerning patterns of low citation counts, and publication predominantly in journals with low impact factors. Contributing factors include economic constraints limiting research investment, insufficient funding for research activities, local focus in social sciences research constraining international visibility, heavy teaching loads reducing time for research, and epistemic culture characteristics affecting knowledge production patterns. Collaboration rates—both domestic and international—remain substantially below regional comparators. Findings of this study align closely with these national patterns. Geographic concentration (72.5% in Ilocos Norte) mirrors documented patterns where research concentrates in limited institutions and regions. Limited sample sizes (62.5% using fewer than 50 participants) reflect resource constraints and access limitations. Recent studies continue documenting these challenges: Central Bicol State University achieved only 25.6% research project completion with 5.1% publication rate (Palmiano, 2024); teacher educator productivity studies reveal low output particularly in externally funded research (Amanonce et al., 2025); and faculty competency assessments show average research competency at practitioner rather than expert levels (Rodriguez et. al., 2021; (Gacrama and Baptista, 2019). The finding that both personal and professional factors significantly deter research participation, with lack of time and insufficient incentives as predominant barriers showing no demographic variation (Landingin et. al., 2024), suggests systemic rather than individual issues. Comparison with international patterns reveals both similarities and differences. The finding of predominant quantitative and mixed-methods approaches (75% combined) contrasts with international teacher education research where qualitative methods dominated at 58.81% in 2024 (Karataş, 2025) and in teacher leadership research 2018-2022 (Li et al., 2024). This suggests institutional preference for measurable outcomes aligned with evidence-based practice movements (Slavin, 2019) and possibly COE designation criteria emphasizing quantifiable impacts. However, the shift toward mixed methods aligns with documented trends in educational leadership research (Karimi & Khawaja, 2023), reflecting broader recognition that integration of quantitative and qualitative data provides richer understanding than either approach alone (Creswell & Clark, 2017). Methodological Quality and Rigor The scarcity of true experimental designs with randomized control groups represents the most significant methodological limitation, severely constraining causal inference capability. Only one study among the 40 employed random assignment, with most 'experimental' studies actually quasi-experimental lacking random assignment and often lacking comparison groups entirely. This limitation is not unique to this institution—randomized controlled trials remain rare in educational research generally due to ethical constraints (withholding potentially beneficial interventions from control groups), practical challenges (obtaining permission for random assignment, maintaining treatment fidelity), and political considerations (stakeholder resistance to controlled experimentation) (Gorard, 2013; Shadish et al., 2002). Nevertheless, the near-complete absence of RCTs limits confidence in causal claims about intervention effectiveness. The prevalence of small sample sizes (62.5% using fewer than 50 participants) requires nuanced interpretation. Small samples may be entirely appropriate for qualitative investigations seeking depth of understanding, pilot studies testing feasibility, or exploratory research generating hypotheses. However, small samples severely limit quantitative studies' statistical power. Post-hoc power analysis suggests that most quantitative studies with samples below 30 had inadequate power (typically below .60) for detecting small to moderate effects, while studies with samples 30-50 achieved acceptable power only for large effects. Effect sizes detected in underpowered studies are typically overestimated and often fail to replicate (Button et al., 2013). This pattern suggests need for increased multi-site collaboration enabling adequately powered investigations, or explicit framing of small-sample studies as exploratory requiring replication. The overwhelming reliance on cross-sectional designs (57.5% of studies explicitly noting this limitation) prevents understanding of developmental trajectories, sustained intervention effects, and temporal dynamics. Educational interventions frequently show initial novelty effects that fade over time (Hawthorne effects), delayed effects emerging only with sustained implementation, or differential effects across developmental stages. Without longitudinal tracking, the field cannot determine whether promising interventions produce lasting change or merely temporary improvement. Even modest longitudinal extensions—six-month or one-year follow-ups—would substantially strengthen evidence base beyond immediate post-test assessments. The absence of longitudinal research likely reflects time and resource limitations, institutional reward structures favoring shorter-term publication outputs, and challenges in maintaining participant contact and institutional access over extended periods. Research Capacity and Faculty Development Implications The identified gaps connect directly to documented faculty research capacity patterns. Research training needs assessment found that basic education teachers rated all training elements as 'Very Important,' with particular emphasis on digital tools in research, data analysis and interpretation, and developing research designs and methods (Tinduwen & Baquitaran, 2024). The proposed holistic Project RESEARCH program addresses these needs across all research stages. However, assessment of faculty research competencies at Basilan State College found average competency across five key areas—conceptualization, research design formulation, data collection, data processing and analysis, and research application—with faculty categorized as practitioners indicating readiness but lacking proficiency compared to expert researchers (Rodriguez et. al., 2021). Similarly, evaluation of full-time faculty at Northern Luzon private university found most reported 'little to some knowledge' in crucial areas with no significant difference between those teaching research courses and those not, indicating broader institutional culture issues (Gacrama and Baptista, 2019). These competency patterns help explain observed methodological limitations. Limited understanding of power analysis and sample size determination contributes to underpowered quantitative studies. Insufficient training in longitudinal research designs perpetuates cross-sectional approaches. Limited exposure to experimental designs with random assignment and control groups results in predominant quasi-experimental approaches. The heavy teaching loads documented as primary deterrent to research (Landingin et. al., 2024) combine with moderate research competency to constrain both quantity and methodological sophistication of research output. Addressing these interconnected issues requires systematic faculty development initiatives alongside structural changes in workload allocation and research support infrastructure. Thematic Coherence and Strategic Focus The emergence of coherent thematic clusters suggests healthy intellectual communities pursuing sustained research programs. Digital Transformation (15%) clearly reflects pandemic-driven institutional response, addressing urgent needs around online pedagogy, teacher preparedness, student engagement, and technological infrastructure (Toquero, 2020). The rapid growth of AI in pre-service teacher education research internationally—with substantial publication increases following generative AI emergence (Kuzu, 2025) suggests this theme will continue expanding. However, local research remains limited in AI exploration, representing gap relative to international trends. Laboratory-Based Science Education (15%) demonstrates sustained institutional commitment to STEM teaching quality, addressing documented challenges in Philippine science education where laboratory activities remain underdeveloped despite recognized importance for science learning (Ganal & Guiab, 2014). The comprehensive attention to laboratory instruction competence, resource availability, teaching approaches, and training programs suggests systematic program of research rather than isolated studies. This coherence enhances cumulative knowledge building and facilitates deeper expertise development (Menter, et. al., 2011). However, some critical areas receive limited attention. Mathematics education—often considered equally important as science in STEM initiatives—is notably underrepresented with no dedicated studies. Social studies education, arts education, and values education similarly appear absent or minimal. While no institution excels simultaneously in all areas, these gaps warrant strategic consideration for a COE expected to address comprehensive teacher education needs. International analysis of 454 teacher education articles found broader thematic distribution across nine major themes (Karataş, 2025) suggesting opportunities for expanded coverage. Implications for COE Framework and Sustainability Findings have direct implications for COE designation and sustainability within the framework established by Teacher Education Council (2025). The rubric for COE identification assesses institutions across performance levels evaluating faculty qualifications, research output quality and quantity, curriculum comprehensiveness, facilities and resources, and support services. The identified research gaps—particularly limited sample diversity, geographic concentration, and methodological constraints—represent areas requiring attention for maintaining COE status and demonstrating continuous improvement. The mapping study of excellence in Philippine teacher education (Sinsay-Villanueva et al., 2024) revealed that while COEs generally perform better in licensure examinations compared to non-designated institutions, significant challenges persist including regional disparities in COE distribution, compliance-focused rather than impact-focused selection frameworks, and questions about whether designation truly drives improvement or merely recognizes existing excellence. These findings suggest that research portfolio strengthening should emphasize genuine impact—measurable improvements in teaching and learning, sustainable innovations, scaled implementations—rather than merely meeting quantitative publication targets. Implications and Recommendations For Research Policy and Institutional Strategy Findings carry direct implications for institutional research policy aligned with COE designation criteria (Teacher Education Council, 2025) and national research development priorities. First, the institution should develop strategic research agenda balancing continued strength in established areas (Science Education, Language Development) with deliberate expansion into underrepresented domains. This requires targeted faculty hiring in gap areas, research capacity building through workshops and mentoring, and strategic partnerships addressing coverage limitations. Second, research support mechanisms should align with identified methodological gaps through: workshops on advanced research designs including RCTs and longitudinal methods; statistical consulting support for power analysis and sample size determination; grants specifically for multi-site collaborative research; technical support for sophisticated data management; and structured mentoring programs pairing early-career faculty with experienced researchers (Bland et al., 2005). Third, promotion and tenure criteria should recognize time and resource demands of rigorous research designs. Longitudinal studies, large-scale surveys, and multi-site collaborations require longer time horizons than single-site cross-sectional studies. Evaluation systems emphasizing annual publication counts may inadvertently discourage more ambitious designs. Balanced evaluation considering both productivity and methodological rigor would support quality improvement. Fourth, establishing formal research clusters or centers around identified themes (Digital Learning, Laboratory Science Education, Multilingual Education) could facilitate collaboration, resource sharing, cumulative knowledge building, and competitive external funding acquisition. For Faculty Development and Capacity Building The identified gaps point to specific faculty development needs. Professional development should address: advanced research methodology including experimental designs, longitudinal methods, and power analysis; mixed-methods research design and integration strategies given its prevalence (35%) but often superficial integration (Creswell & Clark, 2017); qualitative research methods and rigorous analysis techniques; grant writing and funding acquisition given low external funding rates (Palmiano, 2024); research ethics and human subjects protection; and scholarly writing and publication strategies. The emphasis on digital tools, data analysis, and research design methods in training needs assessments (Tinduwen & Baquitaran, 2024) should guide program development. For Addressing National Research Productivity Challenges The study reveals that institutional patterns reflect broader national challenges (Vinluan, 2011), suggesting need for coordinated national response alongside institutional initiatives. Geographic concentration could be addressed through regional research consortia pooling resources and participants across institutions. Sample size limitations might be mitigated through collaborative multi-site designs. Cross-sectional design dominance could shift through national funding programs specifically supporting longitudinal research with multi-year commitments. The perception of research as burden rather than integral function (Villarino, 2025; Landingin et. al., 2024) requires systemic changes in workload policies, incentive structures, and institutional cultures valuing research alongside teaching. Conclusion This systematic content analysis of 40 faculty publications from a designated Center of Excellence in Teacher Education provides rigorous, data-driven examination of research trends, methodological patterns, and systematic gaps within the context of Philippine higher education challenges and COE designation frameworks. The research portfolio demonstrates vibrant scholarship with distinctive strengths in Science Education (25%) and Language Education (12.5%), methodological diversity with balanced quantitative (40%) and mixed-methods (35%) approaches, and coherent thematic clusters suggesting sustained research programs in Digital Transformation and Laboratory-Based Science Education. However, systematic gaps constrain quality, generalizability, and impact. Limited sample sizes (62.5% using fewer than 50 participants), overwhelming cross-sectional designs (57.5%), heavy geographic concentration (72.5% in Ilocos Norte), and near-absence of true experimental designs with randomized control groups represent patterns requiring strategic institutional response. These gaps align with documented national challenges where Philippine research productivity ranks below ASEAN neighbors (Vinluan, 2011), faculty face substantial deterrents including time constraints and insufficient incentives (Landingin et. al., 2024), and research competencies remain at practitioner rather than expert levels (Rodriguez et. al., 2021). The study contributes to limited literature on institutional research portfolio analysis in Southeast Asian teacher education contexts, providing methodologically rigorous, comprehensive examination that extends beyond simple productivity metrics to analyze patterns, coherence, and systematic limitations. The findings demonstrate that research challenges are systemic rather than individual, requiring coordinated responses addressing faculty development (Tinduwen & Baquitaran, 2024), structural constraints, incentive systems, and strategic partnerships. Moving forward, the institution is positioned to build on existing strengths while addressing identified gaps through enhanced faculty capacity building, multi-site collaborations, longitudinal research initiatives, attention to emerging technologies including artificial intelligence (Kuzu, 2025), and sustained commitment to methodological rigor and research impact. Limitations of this study warrant acknowledgment. The analysis focuses on single institution during one year period, constraining generalizability to other institutions or time periods. The corpus of 40 publications, while representing complete accessible population for this institution during this period, remains relatively small for some quantitative analyses. The study examines published research without analyzing unpublished studies, work in progress, or research that did not reach publication, potentially introducing publication bias. Analysis relied on content of publications without interviews with authors about methodological choices, resource constraints, or strategic decisions, limiting understanding of contextual factors shaping research. 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Rethinking the connections between campus courses and field experiences in College- and University-Based teacher education. Journal of Teacher Education , 61 (1–2), 89–99. https://doi.org/10.1177/0022487109347671 Additional Declarations The authors declare potential competing interests as follows: The author is a faculty member of the university being studied. This affiliation is disclosed for transparency and did not affect the study design, methodology, data collection, analysis, interpretation of findings, or the conclusions reported in this preprint. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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This affiliation is disclosed for transparency and did not affect the study design, methodology, data collection, analysis, interpretation of findings, or the conclusions reported in this preprint.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eResearch Trends, Methodological Patterns, and Gaps in Faculty Publications at a Center of Excellence in Teacher Education in Northern Philippines\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTeacher education institutions serve as critical nodes in education systems, simultaneously preparing future educators and generating knowledge through systematic research and scholarship (Cochran-Smith \u0026amp; Villegas, 2015). The quality and relevance of research produced by these institutions directly influences evidence-based practice, policy formation, and responses to contextual educational challenges (Zeichner, 2009). In the Philippine context, the Teacher Education Council has established a comprehensive framework for identifying and designating Centers of Excellence (COEs) in teacher education. This framework emphasizes research productivity, faculty qualifications, curriculum quality, and institutional capacity as central criteria for designation and sustained recognition (Teacher Education Council, 2025). Recent mapping studies of excellence in teacher education reveal that while COEs generally demonstrate superior performance in licensure examinations, significant regional disparities exist in distribution, with concentration in the National Capital Region, and persistent tensions between compliance-focused and impact-focused approaches to excellence (Sinsay-Villanueva et al., 2024).\u003c/p\u003e\n\u003cp\u003ePhilippine higher education institutions face well-documented challenges in research productivity that contextualize any institutional analysis. Vinluan\u0026apos;s (2011) seminal bibliometric analysis comparing Philippine research output in education and psychology with ASEAN neighbors revealed that the Philippines ranks consistently low in research productivity, particularly since the 1990s, compared to Singapore, Thailand, Malaysia, and Indonesia. The analysis documented that a small cadre of researchers from limited institutions contribute the majority of publications, with concerning patterns of low citation counts and publication in journals with low impact factors. Contributing factors identified include economic constraints, insufficient research funding, local focus in social sciences research limiting international visibility, characteristics of researchers including heavy teaching loads, and epistemic culture related to knowledge production. Notably, collaboration rates\u0026mdash;both domestic and international\u0026mdash;remain markedly low compared to regional counterparts.\u003c/p\u003e\n\u003cp\u003eRecent empirical studies have further documented these challenges across Philippine higher education institutions. Research examining faculty productivity at Central Bicol State University-Calabanga Campus (2020-2022) found that only 25.6% of approved research proposals resulted in completed projects, with merely 5.1% published and 1.7% cited, categorizing overall productivity between very low and low levels across colleges (Palmiano, 2024). Systematic investigation of deterrents to research pursuits among 90 university faculty in Northern Philippines identified significant barriers in both personal (M=4.99) and professional (M=4.92) domains, with predominant issues including lack of time, excessive teaching loads, insufficient incentives, and inadequate research skills, with no significant demographic differences across age, gender, or employment status (Landingin et. al., 2024). Faculty competency assessments consistently reveal average research competency at practitioner level rather than expert level, with most faculty reporting \u0026apos;little to some knowledge\u0026apos; in crucial areas including conceptualizing research problems, designing studies, and writing scholarly papers (Rodriguez et. al., 2021; (Gacrama and Baptista, 2019). Studies of Higher Education Institutions in Region 8 found research capability perceived as moderate, with research often viewed as additional burden rather than integral function, and persistent inadequacies in funding and institutional support (Villarino, 2025).\u003c/p\u003e\n\u003cp\u003eUnderstanding institutional research patterns requires attention to broader methodological trends in educational research. Systematic review of 80 empirical studies on teacher leadership (2018-2022) identified predominance of qualitative methods with focus on K-12 educational settings in North America and Asia, though consensus on core construct definitions remains elusive, with definitions varying by context and school culture (Li et al., 2024). Analysis of 23 empirical studies in educational leadership from 11 peer-reviewed journals (2016-2019) documented notable shifts toward mixed methods and case study designs, with interviews and surveys as predominant data collection methods, average sample sizes of 30 for qualitative and 400 for quantitative studies, and content analysis and thematic analysis as primary techniques for qualitative data interpretation (Karimi \u0026amp; Khawaja, 2023). Analysis of 454 teacher education articles published in SSCI Q1 indexed journals (2024) revealed nine major research themes with \u0026apos;teacher professional development\u0026apos; most prominent, qualitative research methods predominant at 58.81%, and teacher-centered perspectives overshadowing voices of other stakeholders including teacher educators and K-12 students (Karataş, 2025). These international patterns provide crucial comparative context for understanding methodological choices and research foci in Philippine teacher education research.\u003c/p\u003e\n\u003cp\u003eSystematic identification of research gaps constitutes essential scholarship for advancing fields and informing strategic research planning. Chand\u0026apos;s (2015) framework for identifying research gaps emphasizes methodical approaches including citation analysis to recognize prevalent issues through studying cited works, content analysis for extracting and analyzing themes from qualitative data, meta-analysis for synthesizing results across multiple studies, systematic reviews for comprehensive overview of current research distinguishing unaddressed questions, and examination of future research suggestions and limitations within existing studies. Application of such frameworks to institutional research portfolios enables identification of systematic patterns requiring strategic response rather than merely documenting individual study limitations. The scoping review methodology applied to critical thinking research in the Philippines (1971-2017) demonstrates the value of systematic gap analysis, revealing predominant emphasis on ability over disposition, significant lack of studies at kindergarten and elementary levels despite importance of early cultivation, and limited exploration of infusion and mixed teaching strategies (Lopez et al., 2023).\u003c/p\u003e\n\u003cp\u003eResearch capacity building emerges as critical intervention for addressing productivity challenges. Assessment of research training needs among 70 basic education teachers at University of Saint Louis (2023-2024) found all identified training elements perceived as \u0026apos;Very Important,\u0026apos; with particular emphasis on using digital tools in research, data analysis and interpretation, and developing research designs and methods (Tinduwen \u0026amp; Baquitaran, 2024). Studies of teacher educator productivity patterns reveal that professional characteristics including years of service, educational attainment, faculty rank, research teaching experience, and seminar participation significantly influence research output, with educators possessing advanced degrees and higher ranks generally producing more research, while those with limited teaching experience and fewer seminar participations show lower output (Amanonce et al., 2025). These findings underscore necessity for targeted faculty development initiatives addressing specific competency gaps and professional characteristics.\u003c/p\u003e\n\u003cp\u003eEmerging educational technologies, particularly artificial intelligence, represent rapidly evolving research domains with implications for teacher education. Bibliometric analysis of AI integration in pre-service teacher education (2020-2025) reveals substantial publication increases following emergence of generative AI tools like ChatGPT, with Germany and Finland leading in citation impact while China demonstrates high volume but lower citation density (Kuzu, 2025). Central themes include generative AI applications and evolving AI-TPACK (Technological Pedagogical Content Knowledge) framework, highlighting paradigm shifts in educational technology models. However, persistent gaps exist between theoretical AI concepts and practical implementation in educational settings, requiring research bridging this divide. Analysis of world-class research universities emphasizes strategic focus, quality faculty hiring from prestigious institutions, internationalization fostering collaborations, and robust research infrastructure as key success factors, providing benchmarks for institutional development aspirations (Altbach and Salmi, 2011).\u003c/p\u003e\n\u003cp\u003eDespite recognition of research importance in teacher education and documented national challenges, systematic analyses of institutional research portfolios within Philippine COEs remain limited. Most existing studies focus on productivity metrics\u0026mdash;publication counts, citation rates, completion percentages\u0026mdash;rather than comprehensive examination of research patterns, methodological approaches, thematic coherence, and systematic gaps. Understanding not merely how much research is produced, but what research is conducted, how it is conducted, what patterns emerge, and what gaps persist, becomes essential for strategic planning aligned with COE criteria and for developing targeted interventions to strengthen research capacity and impact. This study addresses that gap by providing rigorous, systematic content analysis of 40 faculty publications from a designated COE in Northern Luzon, examining research areas, methodological patterns, sample characteristics, geographic scope, thematic clusters, and systematic limitations.\u003c/p\u003e\n\u003cp\u003eThe study addresses six research questions:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eWhat are the dominant research areas and thematic patterns in faculty publications from the Center of Excellence in Teacher Education?\u003c/li\u003e\n \u003cli\u003eWhat methodological approaches and research designs are employed, and how do they compare with documented national and international trends?\u003c/li\u003e\n \u003cli\u003eWhat are the characteristics of research samples in terms of size, type, and geographic distribution?\u003c/li\u003e\n \u003cli\u003eWhat thematic clusters emerge across the research portfolio, and what coherence do they demonstrate?\u003c/li\u003e\n \u003cli\u003eWhat systematic research gaps and methodological limitations are identified across studies?\u003c/li\u003e\n \u003cli\u003eWhat evidence-based recommendations can strengthen the research portfolio in alignment with COE designation criteria and national research development priorities?\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Methodology","content":"\u003ch2\u003eResearch Design\u003c/h2\u003e\n\u003cp\u003eThis study employed systematic content analysis as the primary research design, appropriate for examining manifest and latent content in communication materials including research publications (Krippendorff, 2018; Neuendorf, 2017). Content analysis enables systematic, objective, and quantitative description of content while also facilitating qualitative interpretation of themes and patterns (Mayring, 2014). The approach combines quantitative dimensions\u0026mdash;frequencies, distributions, percentages\u0026mdash;with qualitative analysis of thematic content, methodological approaches, and identified gaps, providing comprehensive understanding of the research portfolio.\u003c/p\u003e\n\u003cp\u003eThe analytical framework was guided by established dimensions for evaluating educational research (American Educational Research Association, 2006) and frameworks for systematic gap identification (Chand, 2015). Six primary analytical dimensions structured the investigation: (a) research focus and thematic areas, employing both deductive coding from established taxonomies and inductive coding allowing themes to emerge from data (Becher \u0026amp; Trowler, 2001); (b) methodological approaches and research designs, categorized as quantitative, qualitative, or mixed-methods with specification of specific designs following patterns in educational leadership research (Karimi \u0026amp; Khawaja, 2023) (c) sample characteristics including size, type, and composition; (d) geographic scope and contextual settings; (e) key findings and contributions to knowledge; and (f) identified limitations and research gaps explicitly acknowledged by authors or evident through systematic analysis.\u003c/p\u003e\n\u003cp\u003eThis multidimensional approach enabled both breadth\u0026mdash;comprehensive coverage of multiple analytical dimensions\u0026mdash;and depth\u0026mdash;detailed examination of patterns within each dimension. The design aligns with recommendations for systematic content analysis in educational research (Mayring, 2014) while incorporating domain-specific frameworks from teacher education scholarship (Cochran-Smith \u0026amp; Villegas, 2015; Zeichner, 2009).\u003c/p\u003e\n\u003ch2\u003eData Sources and Selection Criteria\u003c/h2\u003e\n\u003cp\u003eThe data corpus consisted of 40 research publications authored or co-authored by faculty members affiliated with Mariano Marcos State University, a designated Center of Excellence in Teacher Education in Northern Luzon, Philippines. Publications were obtained from the institutional repository and represented peer-reviewed journal articles published from January to December 2025.\u003c/p\u003e\n\u003cp\u003eSelection criteria ensured corpus representativeness and analytical validity. Inclusion criteria specified: (a) primary authorship or significant co-authorship by faculty affiliated with the institution\u0026apos;s Teacher Education programs; (b) substantive focus on teacher education or directly related educational topics including curriculum, pedagogy, educational psychology, or educational technology; (c) completion and formal publication status in peer-reviewed journals or conference proceedings; (d) availability of full text enabling comprehensive content analysis; and (e) publication from January to December 2025 ensuring recency and relevance to current COE designation criteria. Exclusion criteria eliminated: (a) non-peer-reviewed materials including institutional reports or working papers; (b) publications where institutional affiliation was unclear or tangential; (c) duplicate publications or substantially overlapping content; and (d) publications in languages other than English or Filipino precluding systematic analysis by the research team.\u003c/p\u003e\n\u003cp\u003eThe resulting corpus of 40 publications represented the complete accessible population of recent faculty research output meeting these criteria, rather than a sample. This approach enabled comprehensive institutional analysis without sampling bias, though it constrains generalizability to this specific institutional context during this specific time period\u0026mdash;a limitation acknowledged and addressed through comparison with documented national and international patterns.\u003c/p\u003e\n\u003cp\u003eEach publication had been converted to markdown format as part of institutional digital repository processes, with comprehensive metadata extracted including titles, authors, publication venues, abstracts, methodological descriptions, sample characteristics, geographic settings, key findings, conclusions, and authors\u0026apos; identified limitations. This pre-processing facilitated systematic extraction and coding while maintaining fidelity to original content.\u003c/p\u003e\n\u003ch2\u003eData Analysis Procedures\u003c/h2\u003e\n\u003cp\u003eData analysis proceeded through four distinct but interconnected phases following established protocols for systematic content analysis (Braun \u0026amp; Clarke, 2006; Krippendorff, 2018; Mayring, 2014). Each phase employed specific analytical techniques with attention to validity, reliability, and transparency.\u003c/p\u003e\n\u003cp\u003ePhase 1: Structured Information Extraction. A standardized extraction framework was developed specifying variables to be systematically captured from each publication: bibliographic details (title, authors, year, publication venue); research area classification (primary and secondary); research sub-topic or specific focus; methodological approach (quantitative, qualitative, mixed-methods) with rationale for classification; specific research design (experimental, quasi-experimental, correlational, descriptive, case study, ethnographic, etc.); participant characteristics (type, role, educational level); sample size with specification of sampling approach; geographic location and setting characteristics; key findings summarized in 2-3 sentences; main recommendations for policy or practice; and identified research gaps or limitations explicitly stated by authors. Extraction was conducted independently by two researchers with discrepancies resolved through discussion and reference to original texts, establishing intercoder reliability. Variables were extracted into structured database enabling systematic comparison and quantitative analysis.\u003c/p\u003e\n\u003cp\u003ePhase 2: Categorical Analysis and Coding. Extracted data were systematically coded across multiple dimensions. Research areas employed hybrid coding combining deductive categories from established taxonomies of educational research (Becher \u0026amp; Trowler, 2001) with inductive categories emerging from the specific corpus. This approach balanced theoretical grounding with openness to context-specific patterns. Initial deductive categories included Science Education, Language Education, Mathematics Education, Teacher Education, Educational Technology, and Curriculum Studies. Additional categories emerged inductively including Cultural Studies, Community Safety, and Student Welfare. Methodological approaches followed standard categorization (quantitative, qualitative, mixed-methods) with subcategorization of specific designs within each approach. Sample sizes were categorized following patterns observed in educational leadership research (Karimi \u0026amp; Khawaja, 2023): very small (\u0026lt;20), small (20-49), medium (50-99), large (100-199), and very large (200+), with additional category for unspecified samples. Geographic locations were initially coded as stated, then grouped into broader categories: MMSU main campus, other Ilocos Norte schools, other Philippine locations, international, and unspecified. All coding decisions were documented with rationales, and coding scheme was iteratively refined through constant comparison.\u003c/p\u003e\n\u003cp\u003ePhase 3: Thematic Analysis. Thematic analysis (Braun \u0026amp; Clarke, 2006) identified cross-cutting themes and patterns transcending individual research area classifications. This involved: familiarization through repeated reading of extracted data and original texts; initial code generation identifying interesting features systematically across dataset; searching for themes by collating codes into potential themes; reviewing themes against coded extracts and entire dataset; defining and naming themes with clear definitions and scope; and producing thematic analysis report. Ten major thematic clusters emerged representing coherent areas of sustained scholarly focus. Theme identification emphasized both semantic themes (explicit surface meanings) and latent themes (underlying ideas and conceptualizations). Themes were validated through multiple passes through data and checking for internal homogeneity (coherence within themes) and external heterogeneity (clear distinctions between themes).\u003c/p\u003e\n\u003cp\u003ePhase 4: Systematic Gap Analysis. Research gaps and limitations were analyzed at two levels: (a) explicit gaps\u0026mdash;limitations directly acknowledged by authors in discussion or conclusion sections; and (b) systematic gaps\u0026mdash;patterns evident through comparative analysis of methodological approaches, sample characteristics, and research designs. Gap categories emerged inductively from data and were organized into broader types: methodological gaps (sample size limitations, absence of control groups, cross-sectional designs, self-reported data limitations); contextual gaps (geographic concentration, single-site limitations, limited diversity); temporal gaps (short-term interventions, lack of follow-up, absence of longitudinal tracking); topical gaps (underexplored content areas, emerging technologies not addressed); and implementation gaps (limited attention to fidelity, scalability, or sustainability). Frequency of each gap type was calculated to identify systematic patterns requiring institutional response.\u003c/p\u003e\n\u003cp\u003eThroughout all phases, attention to validity and reliability employed multiple strategies: systematic procedures documented through audit trail; dual coding with intercoder reliability checks on subset of data; constant comparison ensuring coding consistency; member checking where researchers validated interpretations against original texts; triangulation across data sources and analytical methods; and reflexivity acknowledging researchers\u0026apos; positionality as members of the institution being studied, addressed through explicit criteria and systematic procedures.\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eQuantitative dimensions employed descriptive statistics including frequencies, percentages, means, and ranges calculated using Python programming language (version 3.11) with pandas library (version 2.0) for data manipulation and analysis. Cross-tabulations examined relationships between variables including research area and methodology, sample size and research design, and geographic location and research focus. Results were organized into comprehensive tables facilitating interpretation and comparison. While inferential statistics were considered, the corpus represented complete population rather than sample, making inferential techniques unnecessary for addressing research questions focused on describing institutional patterns rather than generalizing to broader populations.\u003c/p\u003e\n\u003ch2\u003eEthical Considerations\u003c/h2\u003e\n\u003cp\u003eThe study analyzed published research already in public domain, eliminating concerns about confidentiality or informed consent for data collection. However, researchers attended to ethical dimensions of institutional research. Analysis focused on aggregate patterns rather than critiquing individual studies or researchers. Interpretations were grounded in systematic evidence rather than subjective judgments. Findings emphasize institutional patterns and systemic issues rather than attributing limitations to individual researchers, recognizing that faculty work within structural constraints including heavy teaching loads, limited resources, and institutional priorities. Recommendations focus on institutional responses and system-level improvements rather than individual remediation. Researchers acknowledged positionality as institutional members with vested interest in positive portrayal, addressed through systematic procedures, explicit criteria, and attention to both strengths and limitations.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eResults are organized according to the six research questions, presenting findings from systematic content analysis of 40 faculty publications. Tables present quantitative distributions while narrative text provides interpretive context and highlights patterns warranting attention.\u003c/p\u003e\n\u003ch2\u003eResearch Areas and Thematic Distribution\u003c/h2\u003e\n\u003cp\u003eAnalysis identified 20 distinct research areas represented across the 40 publications, with marked concentration in specific domains. Table 1 presents the distribution ranked by frequency. Science Education emerged as dominant focus with 10 studies (25.0% of portfolio), more than double the next most frequent area. Language Education followed with 5 studies (12.5%), then Teacher Education with 3 studies (7.5%). Four areas\u0026mdash;Early Childhood Education, Educational Technology, Technology Integration, Cultural Studies, and Teacher Professional Development\u0026mdash;each comprised 2 studies (5.0% each). The remaining 12 areas each had single studies, collectively representing 30.0% of the portfolio.\u003c/p\u003e\n\u003cp\u003eWithin Science Education, laboratory-based instruction dominated with 6 studies examining laboratory competence, resource availability, teaching approaches, challenges, manual development, and training programs. Physics teaching comprised 4 studies investigating communicative difficulties, strategic barriers, and localized curriculum materials. This concentration reflects institutional priorities in STEM education and faculty expertise in science disciplines, aligning with national emphases despite persistent challenges in Philippine science laboratory instruction (Ganal \u0026amp; Guiab, 2014).\u003c/p\u003e\n\u003cp\u003eLanguage Education research concentrated on literacy development across multilingual contexts characteristic of Philippine education, where students navigate mother tongue, Filipino, and English. Studies addressed sight word recognition, spelling skills enhancement, reading comprehension strategies, oral language teaching competence, and mastery of English collocations, reflecting policy frameworks emphasizing mother tongue-based multilingual education.\u003c/p\u003e\n\u003cp\u003eThe portfolio demonstrates diversity with 20 distinct areas, yet also reveals concentration with top three areas comprising 42.5% of output and top eight comprising 60% of output. Twelve areas represented by single studies suggest either exploratory investigations without sustained programs of research, or emerging interests not yet developed into coherent research agendas. This pattern contrasts with recommendations for sustained research programs enabling cumulative knowledge building (Menter, et. al., 2011).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1. Distribution of Research Areas in Faculty Publications (N=40)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResearch Area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of Studies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 286px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRepresentative Sub-topics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eScience Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 286px;\"\u003e\n \u003cp\u003eLaboratory instruction (6), Physics teaching (4), Localized materials (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eLanguage Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 286px;\"\u003e\n \u003cp\u003eSight words, Spelling skills, Reading comprehension, Oral language, Collocations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eTeacher Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 286px;\"\u003e\n \u003cp\u003eOnline teaching challenges, Interactive lectures, Transversal skills\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eEarly Childhood Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 286px;\"\u003e\n \u003cp\u003eFine motor skills (scissors), Student engagement (play-based)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eEducational Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 286px;\"\u003e\n \u003cp\u003eDigital assessment tools, Alternative delivery modes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eTechnology Integration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 286px;\"\u003e\n \u003cp\u003eAugmented reality in fashion education\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eCultural Studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 286px;\"\u003e\n \u003cp\u003eHeritage tourism (Tumba Festival), Migration songs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eTeacher Professional Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 286px;\"\u003e\n \u003cp\u003eOnboarding programs, Laboratory training (SCILAW)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9-20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eOther 12 areas (1 study each)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 286px;\"\u003e\n \u003cp\u003ePhysical Education, Film Studies, Community Safety, Social Studies, etc.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u0026nbsp;Methodological Approaches and Research Designs\u003c/h2\u003e\n\u003cp\u003eMethodological analysis revealed predominant use of quantitative and mixed-methods approaches. Table 2 presents the distribution of methodologies employed. Quantitative methods were most prevalent with 16 studies (40.0%), followed by mixed-methods with 14 studies (35.0%), qualitative methods with 9 studies (22.5%), and one review article (2.5%).\u003c/p\u003e\n\u003cp\u003eWithin quantitative studies, quasi-experimental designs with pre-test/post-test comparisons dominated with 9 studies, followed by descriptive-correlational designs (4 studies) and survey research (3 studies). Notably, no true experimental studies employed random assignment to treatment and control groups, limiting capacity for causal inference. Most studies labeled \u0026apos;experimental\u0026apos; were actually quasi-experimental, lacking random assignment and often lacking control groups entirely.\u003c/p\u003e\n\u003cp\u003eMixed-methods studies typically combined surveys or standardized assessments with qualitative interviews, observations, or reflective journals. Seven studies used survey-plus-interview designs, five employed pre-test/post-test with observations, and two utilized multiple integrated methods. However, many studies claiming mixed-methods employed sequential rather than integrated designs, with qualitative data supplementing rather than being genuinely integrated with quantitative findings.\u003c/p\u003e\n\u003cp\u003eQualitative studies employed thematic analysis (4 studies), semi-structured interviews (3 studies), and ethnographic approaches (2 studies). These tended to be smaller-scale investigations with purposive sampling focusing on depth of understanding rather than breadth of generalization.\u003c/p\u003e\n\u003cp\u003eThis distribution differs markedly from international patterns where qualitative methods dominated at 58.81% in 2024 SSCI teacher education publications (Karataş, 2025) and in systematic review of teacher leadership research 2018-2022 (Li et al., 2024). The institutional preference for quantitative and mixed-methods may reflect: (a) emphasis on measurable outcomes aligned with COE criteria; (b) faculty training backgrounds in quantitative methods; (c) perception that quantitative research carries greater legitimacy; or (d) availability of statistical analysis support. However, the shift toward mixed methods aligns with documented trends in educational leadership research 2016-2019 (Karimi \u0026amp; Khawaja, 2023).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2. Distribution of Methodological Approaches and Research Designs (N=40)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of Studies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 287px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommon Research Designs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eQuantitative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 287px;\"\u003e\n \u003cp\u003eQuasi-experimental (9), Descriptive-correlational (4), Survey (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eMixed-Methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e35.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 287px;\"\u003e\n \u003cp\u003eSurvey+interviews (7), Pre/post-test+observations (5), Multiple methods (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eQualitative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 287px;\"\u003e\n \u003cp\u003eThematic analysis (4), Semi-structured interviews (3), Ethnographic (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eReview Article\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 287px;\"\u003e\n \u003cp\u003eLiterature review\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eSample Characteristics and Geographic Distribution\u003c/h2\u003e\n\u003cp\u003eAnalysis of sample characteristics revealed concerning patterns regarding size, diversity, and geographic concentration. Table 3 presents sample size distribution across studies.\u003c/p\u003e\n\u003cp\u003eAmong studies with specified sample sizes (N=28), 62.5% employed fewer than 50 participants (combining very small and small categories: 9+7=16 studies). Only 17.5% utilized samples exceeding 100 participants (combining large and very large: 5+2=7 studies). Twelve studies (30.0%) did not specify sample sizes, typically qualitative studies, document analyses, or reviews where traditional sampling frameworks do not apply.\u003c/p\u003e\n\u003cp\u003eSmall sample sizes raise concerns about statistical power and generalizability (Button et al., 2013). Effect sizes detected in small samples are often overestimated and may not replicate in larger populations. For quantitative and mixed-methods studies making inferential claims, samples below 30 lack adequate power for detecting effects unless very large, while samples below 50 remain underpowered for most educational interventions. This pattern aligns with documented challenges in Philippine HEI research where moderate research capability and resource constraints limit sample diversity (Villarino, 2025).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 3. Distribution of Sample Sizes in Research Studies (N=40)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample Size Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of Studies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTypical Study Types\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eNot Specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 244px;\"\u003e\n \u003cp\u003eQualitative studies, document analysis, review\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eVery Small (\u0026lt;20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 244px;\"\u003e\n \u003cp\u003ePilot studies, small group interventions, case studies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eSmall (20-49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 244px;\"\u003e\n \u003cp\u003eClassroom-based interventions, single-section studies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eMedium (50-99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 244px;\"\u003e\n \u003cp\u003eGrade-level cohorts, multi-section studies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eLarge (100-199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 244px;\"\u003e\n \u003cp\u003eSchool-wide or division surveys\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eVery Large (200+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 244px;\"\u003e\n \u003cp\u003eDivision-wide large-scale surveys\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Geographic analysis revealed striking concentration. Table 4 presents distribution of research sites. Geographic concentration in Ilocos Norte was substantial with 72.5% of studies conducted in the province (combining categories 1 and 2: 19+10=29 studies). Within this, 47.5% were in other Ilocos Norte schools while 25.0% were at MMSU main campus. Only 12.5% had broader \u0026apos;Philippines (General)\u0026apos; scope, typically review articles or policy analyses. Another 12.5% were classified as \u0026apos;Other/Not Specified.\u0026apos;\u003c/p\u003e\n\u003cp\u003eThis concentration reflects patterns documented in mapping excellence studies revealing regional disparities in COE distribution with concentration in National Capital Region (Sinsay-Villanueva et al., 2024). While geographic concentration enables deep contextual knowledge and sustained partnerships facilitating research access and intervention implementation, it significantly limits generalizability to other Philippine contexts with different linguistic communities, cultural characteristics, and socioeconomic conditions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 4. Geographic Distribution of Research Studies (N=40)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeographic Location\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of Studies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSetting Types\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eIlocos Norte (other schools)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e47.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003ePublic schools, colleges, universities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eMMSU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eLaboratory school, university programs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003ePhilippines (General)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003ePolicy analysis, national reviews\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eOther/Not Specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eVaried or unspecified locations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eLaoag/Batac City specific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eUrban school division\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eThematic Patterns and Research Clusters\u003c/h2\u003e\n\u003cp\u003eThematic analysis identified ten major research clusters transcending individual research area classifications. Table 5 presents these patterns ranked by frequency. Two themes tied for dominance: Digital Transformation \u0026amp; Technology (6 studies, 15.0%) and Laboratory-Based Science Education (6 studies, 15.0%), followed by Language \u0026amp; Literacy Development (5 studies, 12.5%).\u003c/p\u003e\n\u003cp\u003eDigital Transformation prominence reflects institutional response to pandemic-driven shifts in educational delivery (Toquero, 2020), encompassing augmented reality applications, online teaching challenges, digital assessment tools, microlearning innovations, and alternative delivery modes. Laboratory-Based Science Education demonstrates sustained focus addressing documented challenges in Philippine STEM education. These coherent clusters suggest intellectual communities with sustained research programs rather than isolated one-off studies.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 5. Thematic Research Patterns and Clusters (N=40)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThematic Cluster\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRepresentative Topics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eDigital Transformation \u0026amp; Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003eAR, online teaching, digital assessment, microlearning, ADMs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eLaboratory-Based Science Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003eLab competence, resources, approaches, challenges, training\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eLanguage \u0026amp; Literacy Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003eSight words, spelling, reading, oral language, collocations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003ePhysics Teaching Challenges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003eCommunication barriers, localized materials, strategic approaches\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eInnovative Pedagogies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003eFlip-J, SkIT, Motor imagery, Gamification (Lexis-Spell)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTeacher Competence Assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003eOnboarding, food hygiene, laboratory skills, oral language\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eEarly Childhood Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003eScissors skills, engagement, play-based activities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eInclusive \u0026amp; Multicultural Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003eForeign students, inclusive attitudes, multilingual literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eStudent Support \u0026amp; Welfare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003eCOVID-19 impact, parental competence, transition support\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eCommunity \u0026amp; Cultural Studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003eElectrical safety, heritage tourism, film analysis, migration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eSystematic Research Gaps and Limitations\u003c/h2\u003e\n\u003cp\u003eSystematic gap analysis identified eight categories of limitations explicitly acknowledged by authors or evident through comparative analysis. Table 6 presents gaps ranked by frequency of occurrence across the 40 studies. Limited sample sizes emerged as most prevalent gap, explicitly identified in 62.5% of studies. Cross-sectional designs represented second most frequent limitation at 57.5%, with authors acknowledging inability to assess developmental trajectories or sustained intervention effects. Geographic concentration was noted in 47.5% of studies. These high-frequency gaps suggest systematic institutional patterns rather than isolated deficiencies, warranting strategic institutional response rather than merely individual study improvements.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 6. Categories of Research Gaps and Limitations Across Studies (N=40)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGap Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription and Impact\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eLimited Sample Sizes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e62.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003eSmall samples constrain statistical power and generalizability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eCross-Sectional Designs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e57.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003eSingle time-point data prevents assessment of sustained effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eGeographic Concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e47.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003eIlocos Norte focus limits broader applicability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eControl Group Absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003eDifficult to attribute outcomes solely to interventions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eSelf-Reported Data Reliance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003eSurvey data without observational validation risks bias\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eEmerging Technology Gap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003eAI, VR, learning analytics underexplored\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eImplementation Fidelity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003eLimited discussion of intervention consistency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eCost-Effectiveness Missing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003eSustainability and scalability rarely considered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003ch2\u003eResearch Portfolio in National and International Context\u003c/h2\u003e\n\u003cp\u003eThe research portfolio must be interpreted within documented patterns of Philippine research productivity. Vinluan\u0026apos;s (2011) bibliometric analysis established that Philippine research output in education and psychology ranks consistently low compared to ASEAN neighbors, with limited researchers from few institutions contributing most publications, concerning patterns of low citation counts, and publication predominantly in journals with low impact factors. Contributing factors include economic constraints limiting research investment, insufficient funding for research activities, local focus in social sciences research constraining international visibility, heavy teaching loads reducing time for research, and epistemic culture characteristics affecting knowledge production patterns. Collaboration rates\u0026mdash;both domestic and international\u0026mdash;remain substantially below regional comparators.\u003c/p\u003e\n\u003cp\u003eFindings of this study align closely with these national patterns. Geographic concentration (72.5% in Ilocos Norte) mirrors documented patterns where research concentrates in limited institutions and regions. Limited sample sizes (62.5% using fewer than 50 participants) reflect resource constraints and access limitations. Recent studies continue documenting these challenges: Central Bicol State University achieved only 25.6% research project completion with 5.1% publication rate (Palmiano, 2024); teacher educator productivity studies reveal low output particularly in externally funded research (Amanonce et al., 2025); and faculty competency assessments show average research competency at practitioner rather than expert levels (Rodriguez et. al., 2021; (Gacrama and Baptista, 2019). The finding that both personal and professional factors significantly deter research participation, with lack of time and insufficient incentives as predominant barriers showing no demographic variation (Landingin et. al., 2024), suggests systemic rather than individual issues.\u003c/p\u003e\n\u003cp\u003eComparison with international patterns reveals both similarities and differences. The finding of predominant quantitative and mixed-methods approaches (75% combined) contrasts with international teacher education research where qualitative methods dominated at 58.81% in 2024 (Karataş, 2025) and in teacher leadership research 2018-2022 (Li et al., 2024). This suggests institutional preference for measurable outcomes aligned with evidence-based practice movements (Slavin, 2019) and possibly COE designation criteria emphasizing quantifiable impacts. However, the shift toward mixed methods aligns with documented trends in educational leadership research (Karimi \u0026amp; Khawaja, 2023), reflecting broader recognition that integration of quantitative and qualitative data provides richer understanding than either approach alone (Creswell \u0026amp; Clark, 2017).\u003c/p\u003e\n\u003ch2\u003eMethodological Quality and Rigor\u003c/h2\u003e\n\u003cp\u003eThe scarcity of true experimental designs with randomized control groups represents the most significant methodological limitation, severely constraining causal inference capability. Only one study among the 40 employed random assignment, with most \u0026apos;experimental\u0026apos; studies actually quasi-experimental lacking random assignment and often lacking comparison groups entirely. This limitation is not unique to this institution\u0026mdash;randomized controlled trials remain rare in educational research generally due to ethical constraints (withholding potentially beneficial interventions from control groups), practical challenges (obtaining permission for random assignment, maintaining treatment fidelity), and political considerations (stakeholder resistance to controlled experimentation) (Gorard, 2013; Shadish et al., 2002). Nevertheless, the near-complete absence of RCTs limits confidence in causal claims about intervention effectiveness.\u003c/p\u003e\n\u003cp\u003eThe prevalence of small sample sizes (62.5% using fewer than 50 participants) requires nuanced interpretation. Small samples may be entirely appropriate for qualitative investigations seeking depth of understanding, pilot studies testing feasibility, or exploratory research generating hypotheses. However, small samples severely limit quantitative studies\u0026apos; statistical power. Post-hoc power analysis suggests that most quantitative studies with samples below 30 had inadequate power (typically below .60) for detecting small to moderate effects, while studies with samples 30-50 achieved acceptable power only for large effects. Effect sizes detected in underpowered studies are typically overestimated and often fail to replicate (Button et al., 2013). This pattern suggests need for increased multi-site collaboration enabling adequately powered investigations, or explicit framing of small-sample studies as exploratory requiring replication.\u003c/p\u003e\n\u003cp\u003eThe overwhelming reliance on cross-sectional designs (57.5% of studies explicitly noting this limitation) prevents understanding of developmental trajectories, sustained intervention effects, and temporal dynamics. Educational interventions frequently show initial novelty effects that fade over time (Hawthorne effects), delayed effects emerging only with sustained implementation, or differential effects across developmental stages. Without longitudinal tracking, the field cannot determine whether promising interventions produce lasting change or merely temporary improvement. Even modest longitudinal extensions\u0026mdash;six-month or one-year follow-ups\u0026mdash;would substantially strengthen evidence base beyond immediate post-test assessments. The absence of longitudinal research likely reflects time and resource limitations, institutional reward structures favoring shorter-term publication outputs, and challenges in maintaining participant contact and institutional access over extended periods.\u003c/p\u003e\n\u003ch2\u003eResearch Capacity and Faculty Development Implications\u003c/h2\u003e\n\u003cp\u003eThe identified gaps connect directly to documented faculty research capacity patterns. Research training needs assessment found that basic education teachers rated all training elements as \u0026apos;Very Important,\u0026apos; with particular emphasis on digital tools in research, data analysis and interpretation, and developing research designs and methods (Tinduwen \u0026amp; Baquitaran, 2024). The proposed holistic Project RESEARCH program addresses these needs across all research stages. However, assessment of faculty research competencies at Basilan State College found average competency across five key areas\u0026mdash;conceptualization, research design formulation, data collection, data processing and analysis, and research application\u0026mdash;with faculty categorized as practitioners indicating readiness but lacking proficiency compared to expert researchers (Rodriguez et. al., 2021). Similarly, evaluation of full-time faculty at Northern Luzon private university found most reported \u0026apos;little to some knowledge\u0026apos; in crucial areas with no significant difference between those teaching research courses and those not, indicating broader institutional culture issues (Gacrama and Baptista, 2019).\u003c/p\u003e\n\u003cp\u003eThese competency patterns help explain observed methodological limitations. Limited understanding of power analysis and sample size determination contributes to underpowered quantitative studies. Insufficient training in longitudinal research designs perpetuates cross-sectional approaches. Limited exposure to experimental designs with random assignment and control groups results in predominant quasi-experimental approaches. The heavy teaching loads documented as primary deterrent to research (Landingin et. al., 2024) combine with moderate research competency to constrain both quantity and methodological sophistication of research output. Addressing these interconnected issues requires systematic faculty development initiatives alongside structural changes in workload allocation and research support infrastructure.\u003c/p\u003e\n\u003ch2\u003eThematic Coherence and Strategic Focus\u003c/h2\u003e\n\u003cp\u003eThe emergence of coherent thematic clusters suggests healthy intellectual communities pursuing sustained research programs. Digital Transformation (15%) clearly reflects pandemic-driven institutional response, addressing urgent needs around online pedagogy, teacher preparedness, student engagement, and technological infrastructure (Toquero, 2020). The rapid growth of AI in pre-service teacher education research internationally\u0026mdash;with substantial publication increases following generative AI emergence (Kuzu, 2025) suggests this theme will continue expanding. However, local research remains limited in AI exploration, representing gap relative to international trends.\u003c/p\u003e\n\u003cp\u003eLaboratory-Based Science Education (15%) demonstrates sustained institutional commitment to STEM teaching quality, addressing documented challenges in Philippine science education where laboratory activities remain underdeveloped despite recognized importance for science learning (Ganal \u0026amp; Guiab, 2014). The comprehensive attention to laboratory instruction competence, resource availability, teaching approaches, and training programs suggests systematic program of research rather than isolated studies. This coherence enhances cumulative knowledge building and facilitates deeper expertise development (Menter, et. al., 2011).\u003c/p\u003e\n\u003cp\u003eHowever, some critical areas receive limited attention. Mathematics education\u0026mdash;often considered equally important as science in STEM initiatives\u0026mdash;is notably underrepresented with no dedicated studies. Social studies education, arts education, and values education similarly appear absent or minimal. While no institution excels simultaneously in all areas, these gaps warrant strategic consideration for a COE expected to address comprehensive teacher education needs. International analysis of 454 teacher education articles found broader thematic distribution across nine major themes (Karataş, 2025) suggesting opportunities for expanded coverage.\u003c/p\u003e\n\u003ch2\u003eImplications for COE Framework and Sustainability\u003c/h2\u003e\n\u003cp\u003eFindings have direct implications for COE designation and sustainability within the framework established by Teacher Education Council (2025). The rubric for COE identification assesses institutions across performance levels evaluating faculty qualifications, research output quality and quantity, curriculum comprehensiveness, facilities and resources, and support services. The identified research gaps\u0026mdash;particularly limited sample diversity, geographic concentration, and methodological constraints\u0026mdash;represent areas requiring attention for maintaining COE status and demonstrating continuous improvement.\u003c/p\u003e\n\u003cp\u003eThe mapping study of excellence in Philippine teacher education (Sinsay-Villanueva et al., 2024) revealed that while COEs generally perform better in licensure examinations compared to non-designated institutions, significant challenges persist including regional disparities in COE distribution, compliance-focused rather than impact-focused selection frameworks, and questions about whether designation truly drives improvement or merely recognizes existing excellence. These findings suggest that research portfolio strengthening should emphasize genuine impact\u0026mdash;measurable improvements in teaching and learning, sustainable innovations, scaled implementations\u0026mdash;rather than merely meeting quantitative publication targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications and Recommendations\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eFor Research Policy and Institutional Strategy\u003c/h2\u003e\n\u003cp\u003eFindings carry direct implications for institutional research policy aligned with COE designation criteria (Teacher Education Council, 2025) and national research development priorities. First, the institution should develop strategic research agenda balancing continued strength in established areas (Science Education, Language Development) with deliberate expansion into underrepresented domains. This requires targeted faculty hiring in gap areas, research capacity building through workshops and mentoring, and strategic partnerships addressing coverage limitations. Second, research support mechanisms should align with identified methodological gaps through: workshops on advanced research designs including RCTs and longitudinal methods; statistical consulting support for power analysis and sample size determination; grants specifically for multi-site collaborative research; technical support for sophisticated data management; and structured mentoring programs pairing early-career faculty with experienced researchers (Bland et al., 2005).\u003c/p\u003e\n\u003cp\u003eThird, promotion and tenure criteria should recognize time and resource demands of rigorous research designs. Longitudinal studies, large-scale surveys, and multi-site collaborations require longer time horizons than single-site cross-sectional studies. Evaluation systems emphasizing annual publication counts may inadvertently discourage more ambitious designs. Balanced evaluation considering both productivity and methodological rigor would support quality improvement. Fourth, establishing formal research clusters or centers around identified themes (Digital Learning, Laboratory Science Education, Multilingual Education) could facilitate collaboration, resource sharing, cumulative knowledge building, and competitive external funding acquisition.\u003c/p\u003e\n\u003ch2\u003eFor Faculty Development and Capacity Building\u003c/h2\u003e\n\u003cp\u003eThe identified gaps point to specific faculty development needs. Professional development should address: advanced research methodology including experimental designs, longitudinal methods, and power analysis; mixed-methods research design and integration strategies given its prevalence (35%) but often superficial integration (Creswell \u0026amp; Clark, 2017); qualitative research methods and rigorous analysis techniques; grant writing and funding acquisition given low external funding rates (Palmiano, 2024); research ethics and human subjects protection; and scholarly writing and publication strategies. The emphasis on digital tools, data analysis, and research design methods in training needs assessments (Tinduwen \u0026amp; Baquitaran, 2024) should guide program development.\u003c/p\u003e\n\u003ch2\u003eFor Addressing National Research Productivity Challenges\u003c/h2\u003e\n\u003cp\u003eThe study reveals that institutional patterns reflect broader national challenges (Vinluan, 2011), suggesting need for coordinated national response alongside institutional initiatives. Geographic concentration could be addressed through regional research consortia pooling resources and participants across institutions. Sample size limitations might be mitigated through collaborative multi-site designs. Cross-sectional design dominance could shift through national funding programs specifically supporting longitudinal research with multi-year commitments. The perception of research as burden rather than integral function (Villarino, 2025; Landingin et. al., 2024) requires systemic changes in workload policies, incentive structures, and institutional cultures valuing research alongside teaching.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis systematic content analysis of 40 faculty publications from a designated Center of Excellence in Teacher Education provides rigorous, data-driven examination of research trends, methodological patterns, and systematic gaps within the context of Philippine higher education challenges and COE designation frameworks. The research portfolio demonstrates vibrant scholarship with distinctive strengths in Science Education (25%) and Language Education (12.5%), methodological diversity with balanced quantitative (40%) and mixed-methods (35%) approaches, and coherent thematic clusters suggesting sustained research programs in Digital Transformation and Laboratory-Based Science Education.\u003c/p\u003e\n\u003cp\u003eHowever, systematic gaps constrain quality, generalizability, and impact. Limited sample sizes (62.5% using fewer than 50 participants), overwhelming cross-sectional designs (57.5%), heavy geographic concentration (72.5% in Ilocos Norte), and near-absence of true experimental designs with randomized control groups represent patterns requiring strategic institutional response. These gaps align with documented national challenges where Philippine research productivity ranks below ASEAN neighbors (Vinluan, 2011), faculty face substantial deterrents including time constraints and insufficient incentives (Landingin et. al., 2024), and research competencies remain at practitioner rather than expert levels (Rodriguez et. al., 2021).\u003c/p\u003e\n\u003cp\u003eThe study contributes to limited literature on institutional research portfolio analysis in Southeast Asian teacher education contexts, providing methodologically rigorous, comprehensive examination that extends beyond simple productivity metrics to analyze patterns, coherence, and systematic limitations. The findings demonstrate that research challenges are systemic rather than individual, requiring coordinated responses addressing faculty development (Tinduwen \u0026amp; Baquitaran, 2024), structural constraints, incentive systems, and strategic partnerships. Moving forward, the institution is positioned to build on existing strengths while addressing identified gaps through enhanced faculty capacity building, multi-site collaborations, longitudinal research initiatives, attention to emerging technologies including artificial intelligence (Kuzu, 2025), and sustained commitment to methodological rigor and research impact.\u003c/p\u003e\n\u003cp\u003eLimitations of this study warrant acknowledgment. The analysis focuses on single institution during one year period, constraining generalizability to other institutions or time periods. The corpus of 40 publications, while representing complete accessible population for this institution during this period, remains relatively small for some quantitative analyses. The study examines published research without analyzing unpublished studies, work in progress, or research that did not reach publication, potentially introducing publication bias. Analysis relied on content of publications without interviews with authors about methodological choices, resource constraints, or strategic decisions, limiting understanding of contextual factors shaping research. Future research should address these limitations through comparative multi-institutional analyses, longitudinal tracking of institutional research portfolios over time, examination of publication processes and barriers, and integration of faculty perspectives on research challenges and opportunities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eDisclosure of AI Use\u003c/h3\u003e\n\u003cp\u003eNotebookLM was used in reviewing related literature. ChatGPT was used to assist in clarifying concepts and refining the wording of certain sections. QuillBot was used for grammar checking. All AI-assisted outputs were reviewed, edited, and verified by the authors to ensure accuracy and academic integrity.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAltbach, P. G., \u0026amp; Salmi, J. (2011). \u003cem\u003eThe road to academic excellence: The making of world-class research universities [El camino hacia la excelencia acad\u0026eacute;mica: La constituci\u0026oacute;n de universidades de investigaci\u0026oacute;n de rango mundial]\u003c/em\u003e. World Bank Publications. https://openknowledge.worldbank.org/server/api/core/bitstreams/9f215995-6e2c-546d-bfce-f94f7d8304c3/content\u003c/li\u003e\n \u003cli\u003eAmanonce, J. T., Temporal, C. M., Vecaldo, R. T., Calubaquib, J. B., Tamayao, A. I., Malana, M. F., Tamayo, R. A., \u0026amp; Calanoga, M. C. M. (2025). 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Research productivity in education and psychology in the Philippines and comparison with ASEAN countries. \u003cem\u003eScientometrics\u003c/em\u003e, \u003cem\u003e91\u003c/em\u003e(1), 277\u0026ndash;294.\u0026nbsp;https://doi.org/10.1007/s11192-011-0496-5\u003c/li\u003e\n \u003cli\u003eZeichner, K. (2009). Rethinking the connections between campus courses and field experiences in College- and University-Based teacher education. \u003cem\u003eJournal of Teacher Education\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e(1\u0026ndash;2), 89\u0026ndash;99. https://doi.org/10.1177/0022487109347671\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Mariano Marcos State University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"teacher education research, research productivity, center of excellence, faculty publications, Philippine higher education","lastPublishedDoi":"10.21203/rs.3.rs-8552880/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8552880/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines research trends, methodological patterns, and systematic gaps in faculty publications from a designated Center of Excellence (COE) in Teacher Education in the Philippines, employing systematic content analysis across multiple analytical dimensions. The research addresses a critical gap in understanding institutional research portfolios within the context of COE designation criteria and national research productivity challenges. Analysis revealed concentration in Science Education and Language Education, with predominant use of quantitative and mixed-methods approaches. Significant methodological gaps included limited sample sizes, heavy geographic concentration in just one province, overwhelming reliance on cross-sectional designs, and absence of true experimental studies with randomized control groups. These patterns align with documented national challenges where Philippine research productivity ranks below ASEAN neighbors with faculty facing heavy teaching loads, insufficient incentives, and limited research skills. Thematic analysis identified ten distinct research clusters, with Digital Transformation and Laboratory-Based Science Education as dominant themes. Gap analysis categorized eight types of systematic limitations, including sample constraints, temporal limitations, geographic restrictions, and underexploration of emerging technologies. Findings have direct implications for COE sustainability, faculty development programming, and institutional research policy. The study contributes methodologically rigorous, context-specific analysis to the limited literature on teacher education research patterns in Southeast Asian contexts, providing evidence-based recommendations for strengthening research portfolios through longitudinal designs, multi-site collaborations, enhanced faculty capacity building, and strategic focus on emerging educational technologies including artificial intelligence.\u003c/p\u003e","manuscriptTitle":"Research Trends, Methodological Patterns, and Gaps in Faculty Publications at a Center of Excellence in Teacher Education in Northern Philippines","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-09 10:25:16","doi":"10.21203/rs.3.rs-8552880/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"98f6cc08-9464-4f69-a4e2-b742292efbe2","owner":[],"postedDate":"January 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-09T10:25:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-09 10:25:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8552880","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8552880","identity":"rs-8552880","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}