Indonesian language learning in elementary schools continues to face challenges in improving students’ reading comprehension, critical thinking, and oral and written communication skills. Limited instructional media and a lack of contextual learning resources grounded in local culture often reduce student engagement and learning outcomes. This study aimed to develop and evaluate the effectiveness of a problem-based learning model integrated with interactive folklore videos to strengthen early literacy and Indonesian language skills. The study employed a research and development approach using an instructional design model comprising analysis, design, development, implementation, and evaluation stages. The development process included expert validation, limited classroom trials, product revision, and large-scale implementation involving 142 students from five elementary schools. The results demonstrated that the developed learning model achieved high levels of pedagogical, technical, and cultural feasibility. Students showed significant improvement in language skills, active participation during learning activities, and positive responses to the learning media. The findings indicate that integrating problem-based learning with interactive folklore videos provides an effective, contextual, and culturally responsive approach for strengthening early literacy and improving Indonesian language learning in elementary schools.
This study examined the role of education in addressing insecurity and enhancing human capital development in North-East Nigeria. The study was guided by four objectives and four corresponding research questions. A descriptive survey research design was adopted for the study. The study was conducted in the six states of North-East Nigeria, namely Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe States. The population of the study consisted of 2,450 respondents comprising educational administrators, teachers, lecturers, government officials, and community leaders. A sample size of 490 respondents was selected using multi-stage sampling technique involving purposive and simple random sampling techniques. Data were collected using a structured questionnaire titled “Role of Education in Addressing Insecurity and Human Capital Development Questionnaire (REAIHCDQ).” The instrument was validated by experts in Educational Management, Peace and Conflict Studies, and Measurement and Evaluation, while a reliability coefficient of 0.84 was obtained using Cronbach Alpha method. Data collected were analyzed using frequency counts, percentages, mean, and standard deviation. The findings revealed that education plays significant roles in addressing insecurity through peacebuilding, civic orientation, vocational training, and promotion of social integration. The study also found that insecurity negatively affects educational development through destruction of schools, disruption of academic activities, poor school enrollment, and shortage of qualified teachers. Furthermore, the study revealed that education contributes significantly to human capital development through skill acquisition, entrepreneurship, innovation, and productivity enhancement. The study equally identified inadequate funding, poor infrastructure, poverty, shortage of qualified teachers, and persistent insecurity as major challenges affecting the role of education in addressing insecurity and enhancing human capital development in North-East Nigeria. The study concluded that education remains a vital instrument for promoting peace, stability, and sustainable human capital development in the region. The study recommended increased funding of education, strengthening of peace and vocational education, improved security around schools, and recruitment of qualified teachers in insecurity-prone areas.
OBJECTIVES:Dyslipidemia is prevalent among Nigerians with diabetes mellitus (DM), but its treatment has not been well-studied. The objective of this study was to determine the prevalence, treatment rates and control of dyslipidemia among DM patients in northern Nigeria. METHODS:We conducted a multicenter, cross-sectional study of dyslipidemia in DM patients, and noted cardiovascular disease (CVD) risk factors, lipid-lowering treatments, body mass index, blood pressure, HbA1c, lipid profile, glomerular filtration rate and proteinuria. Outcome measures were the rate and treatment of dyslipidemia and attainment of low density lipoprotein cholesterol target for primary prevention of CVD. Binomial logistic regression was used to analyze associations between participant characteristics and dyslipidemia. Hosmer-Lemeshow goodness-of-fit test was used to evaluate the model fit. Statistical analysis was performed with the SPSS version 25 program. RESULTS:The study enrolled 403 participants (58.8% females), of whom 59.6% had dyslipidemia. Besides DM and dyslipidemia, other risk factors for CVD were hypertension (56.8%), obesity (52.6%), chronic kidney disease (36.5%), atrial fibrillation (7.9%), heart failure (5.0%), cigarette smoking (4.7%), excess alcohol use (2.0%), and previous CVD (14.4%). Logistic regression analysis showed dyslipidemia was significantly associated with female gender (odds ratio = 1.68, P = 0.029) and proteinuria (odds ratio = 2.26, P = 0.004). Among those with dyslipidemia, 51.3% took lipid-lowering treatments comprising statins (50.8%) and clofibrate (2.9%). None took other lipid-lowering treatments, and only 17.1% attained the target for primary prevention of CVD. CONCLUSION:Three-fifths of patients had dyslipidemia, but only a sixth attained the treatment target. Treatment for dyslipidemia included statins and fibrates, but not niacin, ezetimibe, bempedoic acid, icosapent ethyl, inclisiran or PCSK9 inhibitors recommended for those who failed intensive statin therapy. There is the need for better access to non-statin treatment and physician adherence to clinical practice guidelines.
The rapid expansion of Wireless Local Area Networks (WLANs) has introduced significant performance challenges, particularly due to the increasing number of mobile and connected devices. Traditional static network management techniques are inadequate for handling the dynamic and complex nature of modern WLAN environments, often resulting in latency, congestion, and interference. This study systematically examines the potential of machine learning (ML) approaches, including advanced algorithms such as Q-learning and Support Vector Machines (SVM), for WLAN performance optimisation. By enabling predictive traffic analysis, adaptive configuration, and intelligent resource allocation, ML techniques offer opportunities to enhance throughput and minimise delay. The study aims to: (1) identify and categorise ML algorithms addressing key WLAN challenges such as latency reduction, interference mitigation, and load balancing; (2) analyze the performance metrics used across studies using standardised formulations; (3) evaluate the generalisability of simulation results to real-world deployments; and (4) identify computational, scalability, and dataset limitations affecting real-time implementation. Despite promising laboratory results, challenges persist due to the scarcity of large, high-quality, real-world datasets required for robust training. The paper highlights the critical role of data efficiency and advocates for open-source WLAN datasets and methods such as transfer learning and few-shot learning to reduce data dependence. Ultimately, this study emphasises that overcoming data constraints is key to realising adaptive, real-time, and scalable ML-driven WLAN optimisation for future wireless communication systems.
Diabetes mellitus is rapidly increasing across low- and middle-income countries, yet rural communities face persistent inequities in chronic disease care. In Northeastern Nigeria, family members often provide essential support for diabetes self-management, but their role is influenced by poverty, cultural beliefs, and fragmented primary healthcare systems. This study examined healthcare providers’ perspectives on how family dynamics, socioeconomic conditions, and community resources shape diabetes care in rural Adamawa State. A descriptive qualitative design was used across five Local Government Areas. Twenty-five healthcare providers were purposively sampled based on their direct involvement in diabetes care. Semi-structured interviews were conducted in English or Hausa, audio-recorded, transcribed, and analyzed through inductive content analysis with triangulation across sites. Six themes emerged. Providers emphasized that poverty constrained access to medications, glucose monitoring, and recommended diets. Food insecurity and limited local substitutions made dietary adherence challenging. Knowledge gaps, spiritual interpretations of disease, and reliance on herbal treatments influenced care-seeking behaviors. Families served as frontline caregivers, assisting with transportation, food preparation, and medication adherence, although their capacity varied. Stigma and neglect were context-dependent, shaped by gender norms and misconceptions. Community support was described as sporadic and dependent on individual or religious leaders rather than structured systems. Diabetes care in rural Nigeria is shaped by intertwined socioeconomic, cultural, and structural determinants. Strengthening primary care platforms with family-centered diabetes education, culturally tailored outreach, community led home support, and affordability measures could reduce inequities and enhance self-management. Policies that institutionalize sustained community partnerships are essential for improving outcomes in low-resource settings.
