This study examined publicly disclosed Common Vulnerabilities and Exposures (CVE) records for Moodle, Chamilo, Canvas LMS, Open edX, and Sakai to assess their value for higher education security governance. A non-intrusive secondary-data audit analyzed 236 CVEs from 2018 to 3 June 2026 using platform, National Vulnerability Database (NVD) Published Date year, revised CVSS severity categories, and OWASP Top 10:2021 classifications. Six zero-score records were separated from low severity. A sensitivity analysis compared the full corpus with 211 NVD API-verified records. A blinded independent IT-faculty coder recoded 24 records; severity agreement was 100.0% (Cohen's kappa = 1.000), and OWASP agreement was 91.7% (Cohen's kappa = 0.814). Moodle and Chamilo represented 91.5% of the corpus, a disclosure-weighted pattern rather than a product ranking. A03 Injection (54.2%) and A01 Broken Access Control (31.4%) dominated. Among records with numeric CVSS scores, including six zero-score records, high/critical proportions were 45.3% (67/148) in the full corpus and 44.9% (66/147) in the NVD-verified subset. Canvas and Sakai findings were interpreted cautiously because of small denominators. Public CVE evidence is affected by disclosure asymmetry, incomplete CVSS metadata, NVD non-returned records, and deployment differences. Because identifiable CVSS version/vector metadata were not preserved, severity comparisons are descriptive rather than version-normalized. The audit offers a reproducible governance framework linking vulnerability patterns to patching, authorization review, content-ingestion controls, plugin governance, and supplier accountability without treating raw CVE counts as inherent security rankings.
In humanitarian logistics, the planning of medical relief supply is sensitive to accurate demand forecasting due to its intermittent and volatile demand pattern that constrain the usefulness of conventional statistical techniques. This paper provides an empirical analysis of the increase in ensemble learning models that are optimized through Bayesian searching of hyperparameters to predict demand of medical relief. Using the Team Data Science Process (TDSP) framework, a quantitative methodology is used to evaluate the performance of the model based on predictive accuracy and robustness performance with extreme values, and generalizability on a real-world dataset in the National Capital Region (NCR), Philippines. There are five ensemble models such as AdaBoost, CatBoost, Gradient Boosting, LightGBM, and XGBoost. Findings indicate that Bayesian optimization produces a tangible performance gain, especially on Gradient Boosting and LightGBM. The CatBoost model produces the lowest RMSE, WAPE, and MASE, and the most consistent cross-validation results, which means that it is more accurate and stable in the model tested. On the contrary, XGBoost and AdaBoost exhibits relatively poorer performance and low robustness. Although the results illustrate the efficiency of optimized boosting ensembles to handle complex and irregular demand shapes, the research study is limited by the coverage of datasets, possible temporal leakage and lack of actual deployment. Thus, it is possible to arrive at conclusions only based on the assessed data and experimental conditions.
Accreditation is a critical process in Higher Education Institutions (HEIs) that ensures quality, accountability, and continuous improvement. However, many institutions face challenges in managing accreditation documents due to fragmented storage systems, a lack of centralized monitoring, and limited use of data-driven tools. This study presents the design and development of a web-based Accreditation Online Management Registry (AOMR) integrated with predictive readiness analytics using TensorFlow. The system provides a centralized platform for document management, compliance monitoring, and real-time tracking of accreditation requirements. The predictive module utilizes institutional data, including document completeness, submission timelines, and historical compliance indicators, to estimate accreditation readiness levels. A developmental research design using Agile methodology was employed. The system was evaluated using the ISO/IEC 25010 software quality model by end-users involved in accreditation processes. Results show an overall mean score of 4.48 (Excellent), indicating high levels of usability, functionality, and performance efficiency. The predictive module shows potential to support proactive decision-making, though further validation is recommended. The study contributes a replicable framework that integrates document management and predictive analytics to enhance accreditation readiness in HEIs.
This study aimed to develop and psychometrically validate a tool to gauge students' attitudes concerning the game-based learning method in mathematics. Recognizing the growing integration of digital and game-based pedagogies in mathematics classes, the research intended to build a reliable and valid scale that measured cognitive, affective, and behavioral elements of students' perceptions. The study involves three phases: expert validation, pilot testing, and factor validation. Six experts in mathematics education and educational research established the content validity of the initial 25-item instrument. The instrument was pilot-tested with 120 senior high school students. Results showed positive attitudes toward GBL in math (mean=3.44, SD=0.53), with high agreement across cognitive, affective, and behavioral aspects. Based on the findings, the Scale-Level CVI (S-CVI/Ave) of 0.86 indicates adequate content validity for the instrument measuring students' attitudes toward game-based learning in mathematics. KMO (0.890) and Bartlett’s Test (χ² = 1.489, df = 190, p< .001) confirm data suitability for factor analysis. Cronbach’s Alpha values demonstrate excellent internal consistency: Cognitive Attitude (α = 0.923), Affective Attitude (α = 0.912), and Behavioral Attitude (α = 0.904), with an overall reliability of 0.882. After Varimax rotation, three distinct dimensions emerged: cognitive (7 items), behavioral (7 items), and affective (6 items), all with strong factor loadings (0.755 to 0.851). These findings highlight the GBMAS as a useful instrument that promotes evidence-based instructional design, assessment of innovative pedagogies, and research in mathematics education. The study's limitations include small sample size and single-institution focus, suggesting a need for broader validation and correlation with academic success.
The cattle egret (Bubulcus ibis) is a widespread, medium-sized heron recognized for its ecological adaptability and significant role as an indicator species in wetland ecosystems. Despite its global distribution and extensive research on its ecology, limited data exist on its behavioral ecology in Ethiopia. Therefore, the objectives of this study were to investigate the activity patterns, foraging behavior, abundance, and seasonal habitat utilization of cattle egrets in the Chelekleka Wetland, a seasonally inundated area located near Debre Zeyit in the Ethiopian Rift Valley. Fieldwork was conducted across wet (June to August 2023) and dry (January to February 2024) seasons using direct observation methods, including scan sampling and focal animal sampling, across defined habitat blocks. Descriptive and inferential statistical methods were utilized to analyze the data. Counts revealed higher egret abundance during the wet season (n=5,441) compared to the dry season (n=3,335), highlighting seasonal fluctuations linked to habitat conditions and resource availability. Diurnal activity patterns showed significant variations across time slots and seasons, with feeding and flying being predominant during early and late hours. Foraging observations revealed diverse prey selection, including insects and small vertebrates, and frequent use of livestock pastures and farmlands. The observed seasonal variation in population size and habitat utilization of the cattle egret indicates that wetland hydrology and resource availability are key factors influencing the species’ ecology. Therefore, it is recommended that Wetland conservation and management efforts be strengthened to maintain water levels and vegetation structure throughout the year, ensuring suitable foraging and breeding habitats.
