Mhel Cedric D. Bendo

This study examines how disparities in digital access are associated with participation conditions and learning environments in open and distance learning (ODL) across Philippine regions using a system-level secondary data approach. Drawing on recent data from the Philippine Statistics Authority, Department of Education, and related national sources, the study analyzes regional variations in internet connectivity, device availability, and participation conditions using descriptive statistics, one-way analysis of variance (ANOVA), Pearson correlation, and multiple regression analysis. Results reveal substantial structural inequalities in digital access, with internet use ranging from 40.0% in the Bangsamoro Autonomous Region in Muslim Mindanao (BARMM) to 79.3% in the National Capital Region (NCR), while household internet access increased nationally from 17.7% in 2019 to 48.8% in 2024. Despite these improvements, disparities in infrastructure quality, device ownership, and connectivity type persist, limiting equitable participation in ODL environments. The findings further indicate that enrollment alone does not adequately reflect participation, as regions with high learner populations but limited digital resources experience constrained and fragmented engagement conditions. Correlation and regression analyses demonstrate a statistically significant relationship between digital access and participation conditions, indicating that unequal infrastructure contributes to uneven learning environments. These findings reinforce the relevance of Digital Divide Theory and Structural Inequality Theory in explaining participation disparities and highlight the need for policies that promote equitable digital access across regions.

Artificial intelligence (AI) has emerged as a transformative technology reshaping business operations and economic productivity across industries and countries. As organizations increasingly integrate AI-driven systems into operational processes, understanding the relationship between AI adoption and productivity outcomes has become a critical research concern. This study employs a cross-country quantitative secondary data analysis using harmonized datasets from the OECD Artificial Intelligence Policy Observatory, the Stanford Artificial Intelligence Index, the World Bank World Development Indicators, and the McKinsey & Company. The sample includes five economies representing varying levels of digital development. Descriptive statistics, correlation analysis, and multivariate regression modeling are applied to examine relationships among AI adoption, digital infrastructure, and business productivity. Findings reveal a positive and statistically significant effect of AI adoption on productivity, with digital infrastructure strengthening this relationship. Industries characterized by higher data intensity demonstrate greater AI adoption rates. The findings contribute a structured cross-country empirical framework linking artificial intelligence adoption, digital infrastructure, and productivity performance across developed and emerging economies. The results emphasize the importance of technological readiness, innovation ecosystems, and data-driven capabilities in supporting productivity growth within digitally transforming economies. The findings provide insights for policymakers, leaders, and digital transformation strategists seeking to strengthen economic competitiveness.

Artificial intelligence (AI) literacy has emerged as a critical competency in elementary education as learners increasingly engage with AI-mediated and technology-rich learning environments. This study developed and validated a multidimensional AI literacy instrument for elementary school students using a research and development approach grounded in the ADDIE framework and informed by AI literacy, constructivist learning, and digital citizenship theories. The instrument comprised five dimensions: AI recognition, conceptual understanding, functional application, evaluation and creation, and AI ethics. Content validation by four experts demonstrated strong theoretical relevance across all dimensions. Empirical testing involving 250 Indonesian elementary school students examined the instrument’s psychometric properties. Exploratory factor analysis supported a five-factor structure with acceptable factor loadings and sampling adequacy (KMO > .80; Bartlett’s test, p < .001), explaining a substantial proportion of total variance. Reliability analysis indicated satisfactory internal consistency across retained subscales (Cronbach’s α > .70), although conceptual understanding required minor item revision. Descriptive findings revealed stronger competencies in ethical AI use and functional application than in conceptual understanding of AI processes. The study contributes a psychometrically grounded and developmentally appropriate instrument supporting AI literacy assessment, curriculum innovation, instructional evaluation, and responsible AI integration within primary education contexts for early twenty-first-century educational transformation.

Artificial intelligence (AI) has emerged as a transformative technology reshaping business operations and economic productivity across industries and countries. As organizations increasingly integrate AI-driven systems into operational processes, understanding the relationship between AI adoption and productivity outcomes has become a critical research concern. This study employs a cross-country quantitative secondary data analysis using harmonized datasets from the OECD Artificial Intelligence Policy Observatory, the Stanford Artificial Intelligence Index, the World Bank World Development Indicators, and the McKinsey & Company. The sample includes five economies representing varying levels of digital development. Descriptive statistics, correlation analysis, and multivariate regression modeling are applied to examine relationships among AI adoption, digital infrastructure, and business productivity. Findings reveal a positive and statistically significant effect of AI adoption on productivity, with digital infrastructure strengthening this relationship. Industries characterized by higher data intensity demonstrate greater AI adoption rates. The findings contribute a structured cross-country empirical framework linking artificial intelligence adoption, digital infrastructure, and productivity performance across developed and emerging economies. The results emphasize the importance of technological readiness, innovation ecosystems, and data-driven capabilities in supporting productivity growth within digitally transforming economies. The findings provide insights for policymakers, leaders, and digital transformation strategists seeking to strengthen economic competitiveness.

Mathematical reasoning is a fundamental competency in contemporary mathematics education, yet many junior high school students continue to experience difficulties in constructing logical arguments, connecting mathematical concepts, and solving complex problems. This study examined changes in junior high school students' mathematical reasoning following the implementation of Problem-Based Learning (PBL) integrated with a Deep Learning approach. A quantitative one-group pretest–posttest quasi-experimental design was employed involving 30 Grade VIII students from a public junior high school in North Sumatra, Indonesia. The instructional intervention was implemented across four mathematics learning sessions on Systems of Linear Equations in Two Variables to promote meaningful learning, reflective thinking, and active problem solving. Data were collected using a validated mathematical reasoning test administered before and after the intervention. Descriptive statistics, the Kolmogorov–Smirnov normality test, paired-sample t-test, and N-Gain analysis were used. The findings revealed a statistically significant improvement in students' mathematical reasoning following the instructional intervention, with mean scores increasing from 41.73 in the pretest to 79.40 in the posttest (t(29) = 18.063, p < .001, Cohen's d = 3.30). Because the study employed a one-group pretest–posttest design, these findings indicate substantial within-group improvement following the intervention rather than definitive evidence of causal effectiveness. The mean N-Gain score of 0.64 indicated a moderate level of improvement. The results suggest that integrating PBL with a Deep Learning approach is associated with improvements in mathematical reasoning. The study contributes to innovative mathematics instruction and offers practical implications for improving teaching and learning in education.

This study employed a Convergent Mixed-Methods Design to examine how mobile gaming influences students’ behaviour, study habits, physical well-being, and academic obligations within a senior high school educational context. The quantitative component involved 58 Grade 11 students from Section X in the Philippines who answered a 10-item Likert-scale survey assessing gaming habits, academic distractions, sleep problems, physical symptoms, and coping strategies. Data were analyzed using weighted means, frequency distributions, and Pearson’s correlations. The qualitative component consisted of semi-structured interviews with ten purposively selected participants. The thematic analysis revealed three major themes: (1) gaming for enjoyment and escape, (2) physical discomfort and sleep loss, and (3) difficulty managing academic responsibilities alongside failed attempts at self-regulation. Findings indicated that greater gaming involvement was associated with reduced focus, sleep deprivation, decreased motivation, and neglect of academic tasks. Quantitative results revealed a moderate negative relationship between mobile game addiction and academic performance (r = −.52, p < .001). Qualitative narratives further explained how sleep loss, cognitive fatigue, and reinforcement-driven habits contributed to academic disengagement despite students’ awareness of negative consequences. The study highlights the importance of digital literacy programs, structured guidance interventions, and school-based strategies that promote responsible gaming habits and academic self-regulation among learners.

The rapid expansion of digital payment systems has transformed financial transactions in the Philippines, with mobile wallet platforms becoming central to everyday economic activities. This study examines the market dominance of GCash within the Philippine digital payment ecosystem using a secondary data analysis approach. Data were synthesized from official statistics, industry reports, and fintech market analyses to examine trends in digital payment adoption, mobile wallet usage, merchant integration, and market growth. Findings indicate an increase in digital payment adoption, with the share of digital retail payments rising from approximately 1% in 2013 to 57.4% in 2024. Concurrently, GCash experienced user expansion, growing from 20 million registered users in 2019 to more than 81 million in 2023, alongside a merchant network exceeding 2.5 million partners nationwide. Comparative analysis also shows that GCash maintains the most comprehensive service ecosystem among Philippine e-wallet platforms, contributing to its widespread adoption among consumers and small businesses. The results suggest that platform accessibility, trust, convenience, and network effects contribute to the platform’s dominant position. This study provides empirical insight into the structural and behavioral factors shaping digital payment adoption in the Philippines and highlights the role of fintech platforms in supporting the country’s expanding digital economy.

Maintaining sustained attention has become increasingly challenging in digitally saturated learning environments, where frequent notifications and habitual task switching are normalized. Although digital distraction has been widely examined, limited research has focused on digital micro-procrastination—brief, repetitive digital interruptions—and its cognitive consequences across educational levels. This study examined the relationships among Digital Micro-Procrastination (DMP), Perceived Cognitive Load (PCL), and Deep Work (DW) among junior high school, senior high school, and college students. Using a quantitative descriptive-comparative design, survey data were collected from 45 students equally distributed across three academic levels. All measures demonstrated acceptable internal consistency following reliability refinement. Data were analyzed using descriptive statistics, Spearman’s rank-order correlation, and Kruskal–Wallis tests. Results revealed a significant positive association between digital micro-procrastination and perceived cognitive load, indicating that frequent short digital interruptions are linked to heightened mental strain and attention fragmentation. Significant cross-level differences were observed, with college students reporting the highest levels of digital micro-procrastination and cognitive load. Findings related to deep work suggest a more nuanced relationship, wherein sustained focus may coexist with elevated cognitive effort rather than reduced task demands. Overall, the study underscores the cognitive implications of everyday digital practices and highlights the need for instructional and self-regulatory strategies that mitigate attention fragmentation in contemporary educational contexts.

The rapid expansion of digital payment systems has transformed financial transactions in the Philippines, with mobile wallet platforms becoming central to everyday economic activities. This study examines the market dominance of GCash within the Philippine digital payment ecosystem using a secondary data analysis approach. Data were synthesized from official statistics, industry reports, and fintech market analyses to examine trends in digital payment adoption, mobile wallet usage, merchant integration, and market growth. Findings indicate an increase in digital payment adoption, with the share of digital retail payments rising from approximately 1% in 2013 to 57.4% in 2024. Concurrently, GCash experienced user expansion, growing from 20 million registered users in 2019 to more than 81 million in 2023, alongside a merchant network exceeding 2.5 million partners nationwide. Comparative analysis also shows that GCash maintains the most comprehensive service ecosystem among Philippine e-wallet platforms, contributing to its widespread adoption among consumers and small businesses. The results suggest that platform accessibility, trust, convenience, and network effects contribute to the platform’s dominant position. This study provides empirical insight into the structural and behavioral factors shaping digital payment adoption in the Philippines and highlights the role of fintech platforms in supporting the country’s expanding digital economy.

This study examines the association between invitation message tone and student participation in digitally mediated learning environments. Using a field-based quasi-experimental design, 40 students in the Philippines were assigned through an alternating allocation procedure to receive either a formal, context-rich invitation or a brief informal message directing them to the same online task. Participation was measured through link activation, task completion, and response timing using automated digital tracking. Results showed higher completion rates and faster response patterns in the formal message condition. Completion reached 100% in the formal condition compared to 50% in the informal condition (χ²(1, N = 40) = 13.33, p < .001, V = .58). These findings show that message structure is associated with how learners respond to academic requests in digitally mediated environments. While limited by a small sample size and context-specific design, the study provides applied evidence consistent with principles of transactional distance and teaching presence. The findings suggest that low-effort improvements in communication design may support participation in asynchronous open and distance learning systems.

This study examines the association between invitation message tone and student participation in digitally mediated learning environments. Using a field-based quasi-experimental design, 40 students in the Philippines were assigned through an alternating allocation procedure to receive either a formal, context-rich invitation or a brief informal message directing them to the same online task. Participation was measured through link activation, task completion, and response timing using automated digital tracking. Results showed higher completion rates and faster response patterns in the formal message condition. Completion reached 100% in the formal condition compared to 50% in the informal condition (χ²(1, N = 40) = 13.33, p < .001, V = .58). These findings show that message structure is associated with how learners respond to academic requests in digitally mediated environments. While limited by a small sample size and context-specific design, the study provides applied evidence consistent with principles of transactional distance and teaching presence. The findings suggest that low-effort improvements in communication design may support participation in asynchronous open and distance learning systems.

This research note reports a small-scale exploratory study into how two AI writing detectors, ZeroGPT and Copyleaks, classify authentic student essays. A total of ten anonymised college-student essays were analysed to observe misclassification patterns, particularly false positives, where human-written content has been incorrectly flagged as AI-generated. The assessment was conducted focusing on essays produced by Filipino undergraduates whose nonnative English writing may have features that could lead to misclassification by the detectors. Results show that both detectors inconsistently classified the same set of essays: five essays were labelled as “AI-generated,” while the other five were labelled as “Human,” when in fact all the texts were authentically written by students. These results point to the potential misclassification risks when AI detection tools are used within educational contexts, where it is commonplace for teachers to make important decisions about academic integrity based on the outputs of detectors. The present study underlines the need to validate the outputs of AI detectors with human judgment and advises educators against the use of these tools as sole evidence of misconduct. Implications for ICT-supported assessment practices and policies of academic honesty are discussed, together with recommendations for a more responsible integration of AI detection tools in educational contexts. The results described above have implications for the practice of academic integrity in all countries, but also specifically for those countries with multilingual student bodies whose writing styles may differ from the writing style used to train the detectors.

This study aimed to determine the level of reading proficiency of Grade 7 students through the implementation of an English Reading Program at Public National High School, Philippines. A quantitative one-group pretest–posttest design was employed, involving 494 Grade 7 students across eleven sections. Data were gathered using validated standardized reading comprehension assessments administered before and after the intervention. Findings revealed that the English Reading Program was effective in improving students’ reading proficiency. Post-test results showed a notable increase in reading comprehension levels, with no students classified as non-readers after the intervention. The use of contextualized learning materials contributed to measurable improvements in students’ ability to decode, comprehend, and interpret texts. Overall, the study provides evidence-based support for the effectiveness of structured reading intervention programs in enhancing reading proficiency among junior high school learners. The findings offer practical implications for school administrators and teachers and highlight the importance of targeted reading programs in improving academic performance and promoting the overall well-being of Grade 7 students.

This study examined the mediating role of academic stress in the relationship between second language (L2) comprehension difficulty and test anxiety among early-stage Filipino university students in an English-medium instructional context. Using survey data from 80 undergraduate entrepreneurship students, the study employed correlational and mediation analyses with bootstrapped confidence intervals to examine the proposed relationships among variables. The results indicated that greater L2 academic comprehension difficulty was associated with higher levels of academic stress, which in turn was strongly related to increased test anxiety. Mediation analysis revealed that academic stress partially mediated the relationship between comprehension difficulty and test anxiety, as the direct association between comprehension difficulty and test anxiety was substantially reduced when academic stress was included in the model. These findings highlight academic stress as a key psychological mechanism through which linguistic challenges in English-medium instruction translate into test-related anxiety. The study contributes to educational psychology literature by clarifying the L2–stress–anxiety pathway among university students in non-native English contexts. Practically, the results underscore the importance of instructional and support interventions that integrate academic language development with stress-management strategies to reduce test anxiety and promote more effective learning experiences in English-medium higher education settings.

The increasing reliance on digital technologies in education has intensified concerns regarding unequal access to digital resources across countries, particularly in developing regions. While digital connectivity has the potential to enhance learning opportunities, disparities in internet access and technological infrastructure may contribute to unequal educational outcomes. This study examines the relationship between the digital divide and educational inequality in Asia through a cross-country secondary data analysis of connectivity, access, and student achievement. Using publicly available datasets from the World Bank, the Organisation for Economic Co-operation and Development (OECD), and the International Telecommunication Union (ITU), the study analyzes key indicators of internet access, digital infrastructure, student achievement, and educational inequality across selected Asian countries. Descriptive statistics, correlation analysis, and multiple regression models were employed to examine structural associations among variables. The results indicate that internet access is positively associated with student achievement and negatively associated with educational inequality. Digital infrastructure also demonstrates positive associations with educational performance and lower levels of inequality. In contrast, rural population is negatively associated with student achievement and positively associated with educational inequality. Overall, the findings highlight the importance of digital connectivity and technological capacity in shaping both the quality and equity dimensions of education systems across diverse national contexts.

This position paper formalizes the concept of the Categorical Ceiling Tax (CCT), an interpretive limitation observed in intervention studies that employ quantitative scoring rubrics to classify proficiency levels. The CCT describes a form of structural resistance that occurs when a population mean approaches, but does not cross, a predefined categorical threshold, even when measurable quantitative gains are documented. Using pre–post score data from a one-group design involving the total population of N = 494 Grade 7 students, this paper illustrates how categorical reporting obscured a documented positive mean shift (Δx̅ = +0.20), while the population classification remained at the “Instructional” level. This paper argues that categorical transitions, when interpreted independently, may misrepresent population-level progress in large samples. It further proposes that changes in the deficiency floor—defined as the proportion of learners classified at the Frustration level—be reported as a complementary interpretive indicator alongside categorical outcomes. These observations provide applied insights into the interpretation of outcomes generated through technology-supported assessment and reporting systems commonly used in contemporary educational settings.

This position paper critiques the widespread assumption that literature reviews should prioritize recent sources, often operationalized as a “5-year rule” in academic writing. While recency is valuable for capturing current developments, this practice may inadvertently exclude foundational theories and methodological principles that remain conceptually relevant. To address this issue, the paper introduces Citation Recency Bias, defined as the tendency to privilege newer sources based primarily on temporal proximity rather than functional relevance. Drawing on enduring contributions in research methodology and educational psychology, including measurement theory (Stevens, 1946), reliability estimation through Cronbach’s coefficient alpha (1951), statistical frameworks developed by Fisher (1925) and Cohen (1988), and foundational theories proposed by Bandura (1977) and Vygotsky (1978), the paper argues that many core elements of contemporary scholarship continue to rely on earlier works. Citation Recency Bias is therefore conceptualized as a methodological distortion that may weaken theoretical grounding and conceptual coherence when the publication date is treated as an indicator of scholarly value.

This position paper critiques the widespread assumption that literature reviews should prioritize recent sources, often operationalized as a “5-year rule” in academic writing. While recency is valuable for capturing current developments, this practice may inadvertently exclude foundational theories and methodological principles that remain conceptually relevant. To address this issue, the paper introduces Citation Recency Bias, defined as the tendency to privilege newer sources based primarily on temporal proximity rather than functional relevance. Drawing on enduring contributions in research methodology and educational psychology, including measurement theory (Stevens, 1946), reliability estimation through Cronbach’s coefficient alpha (1951), statistical frameworks developed by Fisher (1925) and Cohen (1988), and foundational theories proposed by Bandura (1977) and Vygotsky (1978), the paper argues that many core elements of contemporary scholarship continue to rely on earlier works. Citation Recency Bias is therefore conceptualized as a methodological distortion that may weaken theoretical grounding and conceptual coherence when the publication date is treated as an indicator of scholarly value.