Samar State University

Google Classroom can enhance the teaching process and support students’ learning. Hence, this study determines the effectiveness of Google Classroom on the respondents’ mathematics performance. The study utilized 159 first-year teacher education students at Eastern Samar State University-Salcedo, selected by stratified random sampling, descriptive-comparative and correlational research design, and a questionnaire. For descriptive analysis, frequency, percentage, mean, and standard deviation were used, as t-test, ANOVA, and Pearson’s correlation coefficient for comparison and relationship between variables. The study revealed that respondents owned a smartphone, which they used to connect to the internet at home and spent 1-2 hours doing internet-related activities. Respondents perceived Google Classroom as a highly effective tool for mathematics, and they had a good to excellent level of mathematics performance. Furthermore, respondents do not significantly differ in their mathematics performance based on their technological profile except for ownership of technology devices. Lastly, the study found that Google Classroom can improve students’ mathematics performance. Based on the findings, it is recommended that Google Classroom be integrated into mathematics as a teaching and learning tool.

This study explored the lived experiences of Social Studies students in the post-pandemic learning landscape, where Philippine higher education has shifted toward hybrid and increasingly digital learning environments. As traditional classroom structures resumed, many learners continued to navigate the lasting academic, technological, and psychosocial effects of pandemic-era distance education. Using a phenomenological approach, the study examined how students made sense of these long-term transitions and how their previous exposure to modular and online learning shaped their current study habits, confidence, and resilience. Data were gathered through semi-structured interviews and analyzed using Colaizzi’s (1978) descriptive method to derive themes reflecting students’ evolving learning realities. Findings revealed that students developed greater autonomy and digital adaptability, yet continued to struggle with residual challenges such as technology fatigue, uneven digital skills, and difficulty re-adjusting to face-to-face academic demands. Participants highlighted how pandemic learning cultivated self-regulation, resourcefulness, and digital competence—skills they now rely on in hybrid settings. However, they also emphasized the need for stronger institutional support systems to bridge learning gaps that persisted beyond the pandemic. The study underscores the importance of responsive educational policies that address long-term learning recovery, promote digital resilience, and strengthen students’ overall readiness for the transformed post-pandemic academic environment.

Severe motor impairments such as amyotrophic lateral sclerosis and locked-in syndrome lead to partial or complete loss of speech, severely restricting communication as voluntary motor control deteriorates. In this study, we developed a non-invasive, wearable EEG-based brain–computer interface that reconstructs coherent natural language sentences by decoding linguistic components directly from neural activity. EEG data were collected from 20 healthy volunteers using two bilateral temporal electrodes (T7/T8) sampled at 128 Hz, with signals segmented into 4-second windows (512 samples) and normalized to a [−1, 1] range. Participants silently generated 6 pronouns, 80 verbs, and 217 object nouns across six languages under controlled cognitive tasks. Separate 1D convolutional neural networks were trained to classify pronouns and to regress verbs and nouns into 300-dimensional FastText semantic embeddings using cosine similarity loss, with data splits of 70/30 or 80/20 for training and validation and augmentation applied exclusively to training data. Pronoun decoding achieved over 60% training accuracy but showed reduced generalization (validation AUC ≈ 0.55), reflecting the context-dependent nature of indexical language, while verb and noun models demonstrated stable convergence over 200–300 epochs and successfully mapped EEG features into semantic space. A Siamese network integrated decoded components into a shared embedding space to ensure semantic coherence prior to sentence generation, enabling the production of grammatically correct and contextually appropriate sentences aligned with experimental conditions such as hunger and thirst. Validtaion cohort of 10 individuals was used to predict their thoughts related to hunger, while fasting. 80% accuracy was achieved. These findings demonstrate that bilateral temporal EEG signals alone are sufficient to recover structured linguistic intent when combined with similarity-based semantic modeling, advancing a scalable, non-invasive communication framework for clinical translation.

Flexible learning system (FLS) became a centerpiece continuity strategy among Philippine higher education institutions during COVID−19. However, its structure and variations across groups of students were unknown. Data were collected from a sample of 12,525 students across higher education institutions in Luzon, Visayas, and Mindanao to study psychometric properties of an FLS questionnaire and relationship of retained domain scores to student profile and teaching-learning technology variables. Sample largely consisted of female respondents (67.5%), individuals who utilized a learning management system during the pandemic (57.9%), and individuals with slow internet connectivity (57.8%) while only 11.8% had fast internet connectivity. Results from our exploratory study provided support for the four-domain structure (Support, Resources and Digital Proficiency, Readiness for Self-Regulated Learning, Barriers and Challenges) which showed good factorability [KMO = 0.944; Bartlett's χ²(325) = 178,200, p < 0.001]. Confirmatory factor analysis indicated model fit was better than a one-factor model, however fit indices were not ideal (CFI = 0.850, TLI = 0.834, RMSEA = 0.098, SRMR = 0.071). Internal consistency was strong for Support (α = 0.862), Resources and Digital Proficiency (α = 0.824), and Readiness (α = 0.832), but weaker for Barriers (α = 0.658). In FDR-adjusted HC3-robust multivariable models, poor vs. fast internet was associated with lower Resources (B = −0.542, p < 0.001) and lower Readiness (B = −0.172, p < 0.001). In contrast, higher digital literacy and prior LMS experience were associated with more favorable scores across several domains (all p < 0.05, FDR-adjusted). Qualitative themes identified connectivity as the dominant challenge (69.6%), while better understanding or learning effectiveness (32.0%) and accessibility or convenience (14.6%) shaped preferred modes of instruction. Overall, FLS during the COVID-19 period was workable but uneven, with support and learner persistence rated more favorably than resource sufficiency and barrier relief. The instrument may serve as a low-stakes institutional monitoring tool during COVID-analogous disruptions, although the Barriers domain still requires refinement and revalidation.

Teaching in fishing-dependent coastal communities involves complex educational realities shaped by poverty, unstable livelihoods, and limited access to resources. This study explored the lived experiences of elementary teachers teaching in a fishing-dependent coastal community under the Department of Education, Catbalogan City Division. Specifically, the study examined how teachers navigated poverty-related challenges, adapted pedagogical practices, managed emotional demands, and viewed education as a source of hope and transformation for learners living in economically vulnerable environments. The study employed a qualitative phenomenological research design, in which seven elementary teachers were purposively selected as participants based on their direct teaching experience in a coastal community. Data were collected through semi-structured in-depth interviews and analyzed using thematic analysis following Braun and Clarke's framework. The findings generated four major themes: (1) Confronting Poverty as a Daily Classroom Reality, (2) Adapting Pedagogical Practices to Learners' Economic Realities, (3) Navigating Emotional Burdens Through Compassion and Resilience, and (4) Viewing Education as a Pathway to Hope and Transformation. The findings revealed that teachers continuously navigated challenges associated with poverty, absenteeism, limited resources, and emotional demands while remaining committed to sustaining learner participation and engagement. Teachers demonstrated adaptability through contextualized instruction, instructional improvisation, and compassionate teaching practices responsive to learners’ lived realities. The study further highlighted the important role of education in empowering and transforming marginalized coastal communities. The study concludes that teaching in fishing-dependent coastal communities extends beyond instructional responsibilities and requires continuous pedagogical, emotional, and social engagement with learners and their families. The findings provide insights for strengthening contextualized educational programs, learner support initiatives, and teacher support mechanisms in marginalized coastal communities.