Journal of Science and Information Technology
Journal of Science and Information Technology (JOSIT) is an inter- and interdisciplinary scientific journal devoted to improving scientific collaboration, enhancing access to Nigerian research, and enhancing academic research capability in Nigeria and African as a whole. The journal welcome papers from all scientific disciplines in the general categories listed below with the purpose of providing a contemporary, highly visible venue for publishing Nigerian research: Biological Sciences Chemical Sciences Computer Science and Information Technology Health Education Human Kinetics Mathematics Physical Sciences Telecommunication Science The journal accepts full-text research article submissions and reviews.
College of Science and Information Technology (COSIT), Tai Solarin Federal University of Education (TASUED)
A Fine-Tuned GPT-2 For Intelligent Auto-Response System Using Transfer Learning with Contrived Email Data
This study involves improving intelligent auto-response systems by fine-tuning GPT-2 using a contrived email dataset through transfer learning. Existing natural language models lacks the contextual specificity and understanding necessary for generating coherent and relevant email responses. Initial experiments using the standard Enron corpus revealed that general-purpose datasets often lack the contextual specificity required for high-quality email generation. To tackle this gap, GPT-2 was fine-tuned on a specialised dataset developed to mimic diverse email interactions to adapt its generative capabilities for various email contexts, such as professional communication, non-academic questions, and academic inquiries. The results of experiments show significant improvements with an increase in BLEU score from 0.53 to 0.66 compared to the baseline model, in response quality. Also, the contrived fine-tuned model achieved substantial gains in ROUGE scores across all variants indicating better contextual understanding and relevance (e.g., ROUGE-1 F1: 0.580) over the baseline. The human evaluations and qualitative analysis conducted support these findings, confirming the ability of the model to generate relevant replies that is largely close to expected human behaviour. The potential of transfer learning in fine-tuning large language models for domain-specific applications is illustrated. This study provides a robust framework for deploying intelligent auto-response systems in real-life. In the future, reinforcement learning techniques to mitigate current limitations will be explored, such as handling ambiguous prompts and improving response diversity.Transfer Learning, Domain-Specific Language Models, Fine Tuning, Generative Pre-trained Transformer, Automated Response
Copyright (c) 2026 Journal of Science and Information Technology
FedExamGuard: A Privacy-Preserving AI Framework for Intelligent Examination Governance
Examination governance in higher education is increasingly challenged by unauthorized examination participation, examination malpractice, inefficient venue allocation, collusion among students, and concerns over student data privacy. Existing examination management systems are predominantly rule-based and provide limited support for intelligent fraud detection, privacy-preserving collaboration, and transparent decision-making. This paper proposes FedExamGuard, a Privacy-Preserving Artificial Intelligence framework for intelligent examination governance. The framework integrates Federated Learning (FL), Multi-Agent Systems (MAS), Explainable Artificial Intelligence (XAI), Differential Privacy (DP), and Graph-Based Optimization to support secure examination eligibility verification, fraud detection, intelligent venue allocation, and anti-collusion seat assignment across higher education institutions. The framework introduces three key components: the Collaborative Fraud Intelligence Network (CFI-Net) for privacy-preserving fraud detection, the XExam-Agent for autonomous examination governance, and the Privacy-Preserving Anti-Collusion Seat Optimization (PACSO) model for intelligent seating arrangements. A simulation-based proof-of-concept evaluation was conducted using a synthetically generated multi-institutional examination dataset that emulates realistic examination processes, including fee payment verification, course registration, examination scheduling, and student behavioural patterns. Simulation results indicate that the proposed framework achieved 97.8% eligibility verification accuracy, 98.4% fraud detection accuracy, 95.6% venue utilization efficiency, and an estimated 80.6% reduction in collusion risk under the defined experimental conditions. The Explainable AI component further enhanced transparency by providing interpretable explanations for automated decisions. The findings demonstrate the feasibility of FedExamGuard as a scalable and privacy-preserving framework for next-generation examination governance and provide a foundation for future validation using real institutional datasets.
Demographic Factors and Computer Anxiety among Education Officers in Nigeria: Implications for Digital Transformation in Education
This study examined the influence of gender, years of service, and their interaction on computer anxiety among education officers using a cross-sectional survey design with 143 respondents from Ogun State, South-West Nigeria. The instrument employed was the Computer Anxiety Scale (CAS). Cross-tabulation results indicated that most officers (83.9%) reported no computer anxiety, while a minority (16.1%) experienced anxiety. Mean scores suggested slightly higher anxiety among females and lower anxiety among officers with longer service; however, two-way ANOVA revealed no significant main effects of gender (F(1,135) = 2.515, p = .115, η_p² = .018), years of service (F(3,135) = .456, p = .714, η_p² = .010), or their interaction (F(1,135) = 3.130, p = .942, η_p² = .003). These results suggest that demographic variables are becoming less predictive of computer anxiety, possibly reflecting digitalization efforts within Nigeria’s education sector. The persistence of anxiety among a minority highlights the need for interventions such as peer mentoring, digital literacy workshops, and confidence-building programmes. The study concludes that computer anxiety among education officers is shaped less by demographic factors while contextual and organizational influences may play a more significant role, with implications for ICT readiness and Nigeria’s digital transformation agenda.
An Offline-First Adaptive Learning (OFALearn) Framework for Low-Bandwidth Educational Environments Using Lightweight AI and Dynamic Proficiency Modelling
The increasing dependence of contemporary e-learning systems on continuous internet connectivity presents significant challenges for learners in low-bandwidth and resource-constrained environments. This study proposes OFALearn (Offline-First Adaptive Learning Framework), an intelligent educational platform that integrates adaptive learning, edge artificial intelligence, and local-first data management to support personalized learning without persistent network access. The framework employs a lightweight proficiency modeling approach that dynamically adapts learning content based on learner performance while enabling delayed synchronization when connectivity becomes available. To evaluate the effectiveness of the proposed framework, simulation experiments were conducted using a synthetic dataset comprising 10,000 learner interactions generated to reflect realistic educational usage patterns in low-bandwidth environments. Comparative evaluation was performed against Rule-Based Adaptive Learning (RBAL), Deep Knowledge Tracing (DKT), and Self-Attentive Knowledge Tracing (SAKT). Simulation results indicate that OFALearn achieved a learning gain improvement of 28.7%, adaptation accuracy of 82.9%, reduced data usage by 65.3%, and lowered system latency from 1200 ms to 410 ms when compared with conventional cloud-dependent learning systems. Although the findings are based on simulation experiments using synthetic data, they provide promising evidence for the feasibility of offline-first adaptive learning architectures in underserved educational contexts. Future work will focus on real-world deployment and validation with actual learners.
Development and Evaluation of a Machine Learning Framework for Predicting Patient Adherence to Mobile Health Applications in Chronic Disease Management
Chronic diseases constitute one of the foremost public health challenges of the twenty-first century, and mobile health (mHealth) applications have emerged as promising adjuncts to conventional care. However, inconsistent and unsustained patient engagement with these tools substantially diminishes their clinical value. Predicting which patients are likely to sustain engagement with mHealth applications remains an underexplored area of health informatics research, particularly in low- and middle-income country (LMIC) settings. This study sought to develop and evaluate a machine learning framework capable of predicting patient adherence to mHealth applications for chronic disease management. A cross-sectional survey design was employed, and data were collected from 487 patients recruited from tertiary health facilities across three sub-Saharan African countries. Nine predictor variables were examined: age, gender, education, health literacy, trust, privacy concern, disease type, duration of illness, and social influence. The outcome variable, mHealth adherence, was operationalized into three categories High Usage, Low Usage, and Sustained Engagement. Five algorithms were developed and compared: Logistic Regression, Random Forest, Support Vector Machine, Artificial Neural Network, and XGBoost. Data preprocessing involved imputation of missing values, outlier detection, feature encoding, normalization, and feature engineering. All models were trained on an 80/20 train-test split with stratified ten-fold cross-validation and grid-search hyperparameter optimization. XGBoost achieved the highest performance (Accuracy = 93.8%, F1 Score = 0.937, AUC-ROC = 0.978), followed by Random Forest (Accuracy = 91.2%), and Artificial Neural Network (Accuracy = 88.6%). Health literacy, trust, and social influence were the most influential predictors. These findings underscore the viability of ensemble-based machine learning in supporting clinical decision-making for chronic disease mHealth adherence and offer actionable insights for application designers, healthcare providers, and policymakers.
Assessing Student Perceptions and Attitudes toward AI enabled Digital Learning Resources in Nigeria’s Higher Education Open and Distance Learning Context
The integration of Artificial Intelligence (AI) in the higher education system through the use of technology which enhances an intelligent, personalized and flexible learning process has caused revolutions in the teaching and learning process particularly in the Open and Distance Learning (ODL). Though many educational technologies driven by Artificial Intelligence have been adopted in different universities around the world, little is known about the awareness, adoption and perception of AI driven educational technology by undergraduates in Nigerian universities. This paper aims to investigate the awareness, adoption and perception of artificial intelligence technologies for open and distance learning by undergraduates in some selected universities in Nigeria; University of Ibadan, Ladoke Akintola University of Technology, Ogbomosho and Ahmadu Bello University, Zaria. This study used a survey research design. The population of the study was undergraduates in the selected universities and the sample size was 342 participants selected through the use of multistage sampling technique. The instrument used for data collection was SAUPAILRQ with a reliability coefficient of 0.84 obtained through Cronbach Alpha. The research questions were analysed using descriptive statistics like frequency, percentage, mean and standard deviation. The hypotheses were analysed using one way ANOVA. Based on the obtained results, it can be seen that undergraduate students are highly aware of AI technologies (Grand Mean = 3.12), are highly utilizing AI technology in educational aspects (Grand Mean = 3.04) and have positive attitudes towards the utilization of AI learning material (Grand Mean = 3.22). It can be observed from the ANOVA results that there is no significant difference in the awareness levels and attitude of the students regarding AI technologies from the perspective of universities (p > .05). However, there is significant difference in the level of utilization of AI technologies by the students in the universities (p < .05). In conclusion, it can be said that Artificial Intelligence has become an important aspect of ODL in Nigeria with great potentials in terms of implementation provided that appropriate technological and policy framework is available in the institution. So, it is very important to improve the technological framework, to raise the awareness of students and teachers about the use of AI technologies, to set ethical guidelines for AI technology and to integrate it into the strategy of teaching and learning.
Effects of Theobroma cacao Pod Husk Extract on the Body Weight, Prothrombin Time and Fasting Blood Glucose of Staphylococcus aureusinduced Albino Rats
The increasing burden of Staphylococcus aureus related skin diseases, has driven interest in plant-derived compounds with therapeutic potential. Theobroma cacao pod husk is an agricultural by-product which has been traditionally explored for medicinal purposes. This study evaluated the effect of Theobroma cacao pod husk extract on the body weight, prothrombin time, and the fasting blood glucose in Staphylococcus aureus–induced albino rats. Amelonado cocoa pods were processed as ash and blend powder, then extracted with 70% ethanol at room temperature. Thirty-six albino rats were divided into four groups viz: treatment only group, infected and treated group, infection only group and control group respectively. Infection was induced using Staphylococcus aureus, after which varying concentrations of the extract were topically administered for consecutive 5 (five) weeks. A marked elevation in the body weight of the treatment only group (142.33±0.15) was observed, when compared with the infection only group (120.10±0.36). There was a significant decrease in prothrombin time at higher concentrations of treatment only group (63.40 ± 0.56) when compared to the control group (76.60 ± 0.35). Additionally, a significant reduction in the fasting blood glucose levels of the treated only group when compared to the infection only group (91.67 ± 0.12) was observed. These findings therefore suggest that the Theobroma cacao pod husk extract possess hematological- associated effects in Staphylococcus aureus–induced skin conditions.
Changes associated with administration of Garcinia kola seed hydroethanol extract on DMBA-Induced Mammary Gland Toxicity in Female Wistar Rats
Oxidative stress plays a critical role in the pathogenesis of 7,12-dimethylbenz[a]anthracene (DMBA)-induced mammary gland toxicity and associated systemic complications. Garcinia kola seeds are rich in bioactive phytochemicals with established antioxidant and anti-inflammatory properties. However, there is paucity of information on its potential effect on cancer related diseases. This study evaluated the protective effects of hydroethanolic seed extract of Garcinia kola on haematological and renal changes associated with DMBA-induced mammary gland toxicity in female Wistar rats. Thirty-five female Wistar rats were randomly assigned to five groups: normal control, DMBA control, DMBA plus Garcinia kola extract, DMBA plus vitamin C, and Garcinia kola extract only. Mammary gland toxicity was induced by a single oral dose of DMBA (50 mg/kg body weight). Following a seven-day induction period, animals received daily oral treatments of Garcinia kola extract or vitamin C for four weeks. Haematological parameters and renal function biomarkers were subsequently evaluated using standard analytical procedures. DMBA administration revealed a statistically significant changes in red blood cell count (8.54±0.33 to 8.87±0.14), haemoglobin concentration (16.20±0.21 to 19.91±0.16), haematocrit level (52.20±6.7 to 58.90±1.8) and neutrophil count (19.12±0.12 to 13.11±0.02) but insignificant in the white blood cell count indicating systemic inflammation and oxidative stress. Electrolyte imbalance was evidenced with a significant decrease in potassium (6.14±0.06 to 5.62±0.23) and chloride concentration (103.2±1.32 to 100±2.7). Treatment with Garcinia kola extract significantly ameliorated these abnormalities, restoring haematological and renal parameters toward normal values. Hydroethanolic seed extract of Garcinia kola exhibits haematoprotective and nephroprotective activities against DMBA-induced toxicity, likely through antioxidant and anti-inflammatory properties. These findings support its potential as a natural therapeutic agent for mitigating carcinogen-induced systemic damage.
Ethnomedicine and the Future of Entomotherapy: Navigating Traditional Practices in a Changing World
Modern pharmacognosy faces a critical crisis of mounting antimicrobial resistance and novel therapeutic agents to combat it. However, entomotherapy which represents a critical but historically marginalized component of global ethnomedicine offers unprecedented pharmacological potential and yield diverse bioactive compounds with potent analgesic, antibacterial, and immunological properties that could address this bottleneck. This review aims to systemically evaluate the intersection of traditional entomotherapeutic practices and contemporary scientific validation, to identify viable pathways for modern drug discovery. The literature search was conducted on Google Scholar database using keywords such as entomotherapy, ethnomedicine and insect active compounds. The search yielded over 200 peer-reviewed published articles and citations, which were critically analyzed for this review. The scope of this study encompasses global traditional entomotherapeutic practices, focusing specifically on insect-derived bioactive compounds demonstrating documented analgesic, antibacterial, and immunomodulatory properties, as well as the ethical and ecological challenges of their modern integration. The significance of this work lies in establishing a balanced framework that ethically pairs indigenous wisdom with advanced biotechnological evaluation. By championing sustainable conservation practices like entomoculture, this review demonstrates how the scientific community can exploit the vast, untapped chemical landscape of insects to combat global health crises while actively safeguarding cultural heritage and fragile forest ecosystems.
Innovation Skills and Institutional Support in Sustainable Aquaculture Start-Up Development among Open and Distance Learning Students in Nigeria
Sustainable entrepreneurship in aquaculture has gained increasing attention due to its potential to enhance food security, generate employment, and promote environmentally responsible business practices in developing economies. However, the sustainability of aquaculture start-ups remains constrained by limited innovation capabilities and inadequate institutional support. This study examined the influence of innovation skills and institutional support on sustainable aquaculture start-up development among Open and Distance Learning students in Nigeria. A quantitative cross-sectional survey design was adopted, and data were collected from 300 respondents using a structured questionnaire. Innovation skills and institutional support were measured using validated multi-item scales, while sustainable aquaculture start-up development was assessed in terms of business viability, sustainability practices, continuity intention, and growth orientation. Data were analyzed using descriptive statistics, correlation analysis, and multiple regression analysis. The results showed that innovation skills had a positive and significant effect on sustainable aquaculture start-up development (β = 0.38, p < 0.01). Institutional support also exerted a positive and significant influence (β = 0.29, p < 0.01). Furthermore, the joint effect of innovation skills and institutional support significantly enhanced sustainable aquaculture start-up development. The regression model explained approximately 47% (R² = 0.47) of the variation in sustainable aquaculture start-up development.The study concluded that sustainable aquaculture entrepreneurship among Open and Distance Learning students depends on both innovation-oriented skills and supportive institutional environments. It was recommended that educational institutions strengthen innovation-focused entrepreneurship education, while policymakers improve access to institutional support mechanisms to promote sustainable aquaculture start-up development in Nigeria.
Effect of Intelligent Tutoring System on Secondary School Students’ Academic Achievement in Biology in Esan West, Edo State
Within the domain of educational biology, despite the recognized societal importance of biological knowledge, student achievement in this discipline has persistently underperformed. The present study was on the effect of intelligent tutoring system on secondary school students’ academic achievement in Biology in Edo state. The study adopted a quasi-experimental research design, specifically the non-randomized pretest-posttest control group design. The population of the study comprised 1,833 senior secondary school II students from twelve public secondary schools, while a sample of 88 students (52 males and 36 females) drawn from two intact classes was used for the study. The instrument used for data collection was Biology Achievement Test (BAT). adopted from WAEC past questions, with a reliability coefficient of 0.82 obtained using the Kuder Richardson (K-21) formula. Two hypotheses were formulated and tested at a 0.05 level of significance. The data collected from the respondents were analysed using mean and standard deviation for research question one and two and Analysis of Covariance (ANCOVA) was used to test the hypotheses one and two formulated for the study. The findings revealed that students taught Biology using the Intelligent Tutoring System achieved higher post-test mean scores of 75.73 than those taught using the conventional teaching method was 69.12. The study further revealed that there was a significant effect of instructional strategies on students’ academic achievement in Biology, P < 0.05 However, no significant difference was found in the academic achievement of male and female students exposed to the Intelligent Tutoring System. Based on the finding, Intelligent Tutoring System is an effective instructional strategy for improving students’ academic achievement in Biology regardless of the gender. It was therefore recommended that Biology teachers should integrate Intelligent Tutoring Systems into classroom instruction to enhance students’ academic achievement and improve understanding of difficult Biology concepts.
Hooked on AI, or Hungry for Thinking? Interrogating the Interplay Between AI Addiction, Critical Thinking, and Mathematics Achievement in Senior Secondary Schools
Mathematics remains a foundational subject in Nigerian senior secondary education, essential for scientific, technological, and economic advancement. However, students’ achievement in the subject remains poor, particularly amid growing reliance on artificial intelligence (AI) tools for learning. This study investigated AI addiction and critical thinking ability as determinants of the mathematics achievement of senior secondary school two (SSS2) students in the Ijebu North Local Government Area, Ogun State, Nigeria. A descriptive survey design was adopted. The population comprised all SSS2 students in the area, from which a sample of 150 was drawn using purposive and simple random sampling techniques. Data were collected with three validated instruments: Mathematics Achievement Test (MAT, r = 0.69), Students’ Artificial Intelligence Addiction Rating Scale (SAARS, r = 0.73), and Critical Thinking Evaluation Scale (CTES, r = 0.76). Pearson product-moment correlation and regression analyses were employed for data analysis. Results showed that AI addiction significantly predicted mathematics achievement, accounting for 10.1% of the variance. Critical thinking ability was a stronger predictor, explaining 14.9% of the variance. Positive correlations were observed among AI addiction, critical thinking, and mathematics achievement, with critical thinking demonstrating the strongest relationship. The study concludes that while AI tools can support mathematics learning, cultivating critical thinking skills is vital for enhanced achievement. It was recommended, among other things, that teachers promote responsible AI use and integrate instructional strategies that develop students’ critical thinking in mathematics classrooms.
Assessment of the Environmental Impact of Selected Industrial Gaseous Waste on Outdoor Air in Ilorin Metropolis
Industrial air pollution remains a growing environmental and public health concern in many developing urban centres. This study assessed the environmental impact of selected industrial gaseous emissions on outdoor air quality in Ilorin Metropolis, Kwara State, Nigeria. The study evaluated the concentration levels of particulate matter (PM2.5 and PM10), carbon monoxide (CO), volatile organic compounds (VOCs), and formaldehyde (HCHO) within the vicinity of selected industries. A descriptive and longitudinal study design was adopted. Fifteen sampling points were selected around five industries, namely Dangote Group of Companies, Doyin Group of Companies Nigeria Limited, International Tobacco Company, Tuyil Pharmaceutical Company, and Glister Quarry Company. Air quality measurements were conducted using calibrated handheld multi-gas monitors. In addition, 150 questionnaires were administered to residents to assess health impacts and awareness of air pollution regulations. Data were analysed using descriptive statistics and a paired-sample t-test at p < 0.05. The findings revealed that PM2.5, PM10, HCHO, and CO concentrations exceeded permissible limits in several locations, indicating poor air quality and potential environmental and health risks. VOC concentrations remained largely within acceptable limits. Significant differences were observed between morning and evening concentrations for HCHO, VOCs, and CO (p < 0.05), while PM2.5 and PM10 showed no significant variation. Frequent cough, wheezing, and nasal congestion were identified as major health complications associated with industrial emissions. Furthermore, 80% of respondents were unaware of legal provisions regulating industrial air pollution. The study concludes that industrial emissions significantly contribute to outdoor air pollution in Ilorin Metropolis and pose considerable health risks to nearby residents. Strengthening environmental regulations, enhancing public awareness, and implementing effective pollution control measures are recommended.
Effects of Tillage Systems and Organic Amendments on Nitrogen Mineralisation and Soil Erodibility in a Tropical Alfisol
Tropical soils are inherently low in fertility and highly susceptible to erosion, hence affecting crop productivity. Few studies have examined the effect of biochar from different sources and cow dung compost on tropical soils. This study assessed the influence of compost and biochar produced from cow dung and maize cobs on the physical and chemical properties of Alfisols. It investigated their mineralisation rates and the influence on the soil erodibility when these organic amendments are applied under different tillage practices. The experiment was laid out in a randomised complete block design during the 2018 wet and dry seasons. Soil amendments were 100% cow dung compost (A100), 100% cow dung biochar (B100), 50% cow dung compost and 50% cow dung biochar (AB50), and a combination of the three amendments, i.e., cow dung biochar, cow dung compost, and maize cobs biochar at 33% each (C33), and a control (D). Amendments were applied at 4 t ha-1. Soils treated with B100 and A100 under conventional tillage had the highest total nitrogen and available phosphorus values of 3.92 g/kg and 10.37 mg/kg, respectively. The highest mineralisation rate was observed in soils treated with A100, with 142.87 μgNg-1 under conventionally tilled soils. However, erodibility rate was lower under minimum tillage, with C33 having the lowest at 0.35. The results suggest that the combined application of cow dung and biochar under conventional tillage can improve soil fertility in the short term; however, minimum tillage would be a preferable alternative for long-term soil health and productivity.
Influence of Artificial Intelligence Awareness and Digital Pedagogical Skills on Job Performance among Public Secondary School Teachers in Ogun State, Nigeria
Medical science generates large volumes of data stored in medical repositories that could be useful for extraction This study investigated the influence of artificial intelligence (AI) awareness and digital pedagogical skills on the job performance of public secondary school teachers in Ogun State. A descriptive survey research design was adopted, with a population comprising 22,806 teachers and 517 principals across 517 public secondary schools. A sample of 900 teachers and 90 principals was selected using simple random sampling. The study used two research instruments tagged Artificial Intelligence Awareness and Digital Pedagogical Skills Questionnaire (AIADPSQ) and Job Performance Scale (JPS) with the reliability co-efficient of .84 and .79. Regression Analysis was used to test hypotheses at a .05 significance level. Findings revealed that AI awareness had a significant positive influence on job performance (β = .162, t = 8.29, p < .05. Digital pedagogical skills also significantly influenced job performance (β = .199, t = 6.78, p < .05). The composite contribution of both variables was significant (F(2, 87) = 4.113, p < .05). The study concluded that AI awareness and digital pedagogical skills are significant predictors of teachers’ job performance. It was recommended that the Ogun State Ministry of Education organise capacity-building programmes on AI applications, provide continuous professional development in digital pedagogy, and invest in digital infrastructure to enhance teaching effectiveness. These findings underscore the imperative of equipping teachers with AI competencies and digital skills to improve professional performance and educational outcomes in public secondary schools.
Impact Evaluation of E-Learning on Senior Secondary Students during Covid-19 Pandemic in Ogun State
No doubt, the COVID-19 pandemic had significant negative influence on educational sector particularly secondary education. Proactive intervention with the use of e-learning technologies was put in place by schools particularly in Ogun State to bridge the learning gap created by the pandemic. However, while there is evidence of e-learning technologies introduction in secondary education in Ogun state, little is known on the impact of these e-learning technologies in bridging the pedagogical gap created by the pandemic in Ogun State secondary schools, thus necessitate the need for this study. This study therefore evaluates the impact of e-learning on senior secondary school students in Ogun State. This study adopts the descriptive survey research design embedded with CIPP evaluation model. Four (4) research questions and two hypotheses were raised. The population for this study comprised of all public and private secondary schools in Ogun State, while 1 Senatorial district, 2 local governments, 5 public and 5 private secondary schools and 150 SS2 students constitute the study sample. E-learning Impact Evaluation Assessment Scale (ELIEAS) (r = 0.77) was the instrument used in data collection. Descriptive statistics was adopted to answer the 4 research questions raised, while t-test was used to test the two stated hypotheses at 0.05 alpha level of significance. Results reveals high students’ proficiency level with e-learning technologies and high level of utilization of e-learning technologies for knowledge-based inquiry by students in Ogun East Senatorial district. Results also affirms that inadequate power supply, internet network problem, financial implications associated with procurement of laptop or smartphones, poor institution provision for e-learning facilities and students’ mishap during e-learning class are challenges experienced by students in the use of e-learning technologies. Results showed high level of student’s perception of e-learning effectiveness in bridging the gap in education created by COVID-19 in Ogun State. Findings of the study however established significant difference in e-learning technological proficiency (t = 3.22; df= 148, p = 0.002 < 0.05) and level of utilization (t = -2.03; df= 148, p = 0.044 < 0.05) i among students based on their school type. Based on the findings of the study, it was concluded that e-learning technologies had significant positive impact bridging the educational gap created by the COVID-19 pandemic in Ogun State. On the bases of these findings, the study recommends that governments should invest heavily in e-learning technologies in schools particularly public secondary schools so as to prevent future interruption of learning by possible pandemic, while stakeholders, PTA and NGOs are encouraged to complement the efforts of government in equipping schools with e-learning technologies.
Adsorption characteristics of Fe-doped graphitic carbon nitride/PEDOT polymer hybrid material as sensors for dissolved gases in the transformer oil: A DFT study
This study evaluates the adsorption potency of a hybrid nanomaterial composed of Fe-doped graphitic carbon nitride-poly (3,4 -ethylenedioxythiophene) (Fe@C3N4-PEDOT) towards acetylene (C2H2), methane (CH4), and hydrogen (H2) gases. Density functional theory (DFT) was employed to optimize the structures and to evaluate or examine the electronic properties, adsorption, and interaction behaviour. Adsorption energies (Eads) for C2H2 (-0.871 eV), H2 (-0.381 eV), and CH4 (-0.381 eV) specify exothermic and spontaneous interactions. C2H2 (-0.871 eV) shows moderate chemisorption, while CH4 and H2 (-0.381 eV each) show weak chemisorption, within the reusable sensor range. The C2H2 showed greater interaction, reducing the energy gap to 2.256 eV, thereby improving conductivity and reactivity. Density of States (DOS) was equally confirmed, as it reveals a significant orbital overlap between the gas and the studied material. Natural Bond Orbital (NBO) analysis also showed a good donor-acceptor interaction with high stabilization energy, which affirms chemisorption. The hybrid nanomaterial’s interaction with these gases, especially acetylene, suggests its high suitability for selective detection and adsorption of hazardous gases in oil and oil-polluted environments. In all, Fe@C3N4-PEDOT shows promising potential as a sensor material with high sensitivity and environmental importance.
Sports and Games Preference and Mental Well-Being among Community Health Officers-In-Training at University of Ilorin Teaching Hospital, Kwara State, Nigeria
The study assessed sports and games preference and mental well-being among community health officers-in-training at University of Ilorin Teaching Hospital, Kwara State, Nigeria. A descriptive research design of the survey type was adopted. The population of the study comprised all CHOs-in-training at the College of Community Health, UITH, Kwara State totalling 104. A purposive sampling technique was used to select all 104 students as the sample size. A validated structured questionnaire was used for data gathering. The reliability of the instrument was carried out using split half method and data were correlated using Spearman-Brown Rank Order which yielded R coefficient of 0.79. Research questions were answered and presented using percentage, composite bar chart and mean score. The null hypothesis was tested using Chi-square statistics at 0.05 level of significance. The findings revealed that volleyball was the most preferred sport (30.8%), while ludo was the most preferred game (15.4%). On gender basis, male mostly preferred football (62%), while females preferred volleyball (75%). Participation in sports and games enhanced mental well-being (grand mean = 4.20). Also, males and females students differ significantly in their selection of sports and games (p-value = 0.0043, p < .05). It was recommended that students should focus more attention on both volleyball and football, while other sports and games should not be neglected. Also, students should continue to engage in sports and games as a means of promoting mental health for optimal academic performance.
Self-Efficacy and Test Anxiety as Predictors of Academic Performance of Biology Students in Senior Secondary Schools
This study examined the roles of self-efficacy and test anxiety as predictors of academic performance among Senior Secondary School Biology students in Ijebu-Ode, Ogun State. A descriptive research design was used, with 600 students selected through simple random sampling from ten schools. Data was collected using a self-designed close-ended questionnaire. This study results revealed high self-efficacy and low test anxiety levels among the students, with no significant gender differences in self-efficacy and test anxiety, although females exhibited higher levels overall. No significant differences were found in these factors based on students’ age. However, students from educated backgrounds displayed higher self-efficacy. A significant positive relationship was found between self-efficacy and academic performance (r = 0.315; p = 0.000), while test anxiety showed no significant relationship with academic performance (r = 0.018; p = 0.669), though it did correlate with self-efficacy. The regression model confirmed that self-efficacy had a significant impact on academic performance (p = 0.000). Based on these findings, the study recommends initiatives to boost self-efficacy, greater parental involvement in education, and strategies to reduce test anxiety. Future research should focus on the long-term effects of self-efficacy on academic outcomes and the effectiveness of interventions addressing Self-efficacy and test anxiety in diverse student populations.
Technology-Enhanced Learning Approaches for Teaching Abstract Chemical Concepts in Resource-Limited Settings
This review evaluates technology-enhanced learning resources that include virtual laboratories, simulations and mobile applications to teach abstract concepts of chemistry in resource-limited learning environments. Findingshave indicated that digital learning materials are significant in enhancing the understanding of abstract chemistry concepts among students, improve academic success, motivation and involvement of learners through interactive and multimedia-based learning. However, the effective implementation is limited by critical factors including infrastructural limitations, device access, internet connectivity, teacher preparation and pedagogical approaches of integrating with other approaches. Virtual laboratories and the augmented reality (AR) applications helped in the visualisation of the molecular structure and reactions that may be difficult to achieve in resource-limited setting of developing countries. Despite the established benefit, the current obstacles of the high cost of implementation, technical problems, digital literacy, the reluctance to alter the ongoing pedagogical practices restrain its usage. Meanwhile, best practices revolve around blended learning systems that combine traditional and online systems, intensive and comprehensive teacher training programs, curriculum alignment, and solutions sensitive and adaptable to offline functionality and low bandwidth environments. This review is appropriate in the discussion of educational policy as it identifies evidence-based methods of quality chemistry education through the combination of the appropriate technology in resource constrained facilities and the necessity of making a systemic investment in digital infrastructure and capacity building in order to ensure equitable educational achievement.
Natural and Human-influenced Wildfire: A Review of Their Causes, Impacts and Prevention Strategies
Wildfires have emerged as one of the most devastating environmental hazards worldwide, increasing in frequency and intensity, driven by climate change and anthropogenic activities. Given the growing environmental, social, and economic impacts of wildfires, this review employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) framework to examine wildfire hazards, their causal factors, associated risks, and preventive strategies. Evidence from the reports of the Food and Agriculture Organization, the Intergovernmental Panel on Climate Change (IPCC), and peer-reviewed literature revealed that while some wildfires are triggered by natural factors such as lightning strikes and extreme heat, the majority are directly or indirectly linked to human activities, including deforestation, agricultural burning, industrial operations, and environmental mismanagement. The review identified that the environmental, health, social, and economic consequences of wildfires include biodiversity loss, air pollution, food insecurity, population, displacement, and infrastructure damage. Findings further indicate that effective wildfire management requires coordinated actions among governments, local communities, environmental agencies, and technological monitoring systems. Integrated Fire Management (IFM) emerged as a sustainable holistic framework for wildfire prevention, preparedness, suppression, recovery, and ecosystem restoration. The review concludes that strengthening climate adaptation measures, environmental education, and policy enforcement is essential for reducing wildfire risks and enhancing resilience to future climate-related disasters.
Adoption of Educational Technology in Teacher Education in Kwara State Universities: An Extended TAM Approach
This study investigated the factors influencing lecturers’ adoption of educational technology in Nigerian university teacher education programmes, using an extended Technology Acceptance Model (TAM) framework. Drawing on recent literature and policy sources, it reviewed how variables beyond TAM’s core constructs (perceived usefulness and ease-of-use) affect adoption. This study adopted a quantitative survey design, and stratified, purposive and simple random sampling techniques were used to select 150 respondents from Faculties of Education across 6 Nigerian universities in Kwara State. The study raised 4 research questions and formulated 1 hypothesis. Mean and Standard Deviation were used to answer the research questions, while Multiple regression analysis was used to test the hypothesis. The results show a high level of adoption (WM = 2.86), with strong use of technology for communication and engagement, though multimedia use is slightly lower. Respondents strongly perceive educational technology as useful (WM = 3.04), especially in improving teaching and learning outcomes. Ease of use is moderate (WM = 2.78), indicating general usability with minor technical challenges. External support is moderate (WM = 2.65), with infrastructure challenges limiting effective adoption. The multiple regression analysis revealed that perceived usefulness, perceived ease of use, and external factors significantly predict lecturers’ adoption of educational technology (R² = 0.50, p < 0.05). Perceived usefulness emerged as the strongest predictor; therefore, the null hypothesis was rejected. The study concludes that understanding contextual factors (training, management support, and curriculum relevance) is key to promoting educational technology use among lecturers. It recommended targeted interventions (enhanced professional development, robust ICT policies) and suggests areas for future research to fill the data gaps in this field.
Effects of Self-Regulated Learning Interventions on Motivation, Self-Efficacy, and Academic Performance of Biology Students: A PRISMA-Based Systematic Review
Self-regulated learning (SRL) has emerged as a critical competency for academic success in the 21st century, particularly in science education. This systematic review examined empirical studies on the effectiveness of SRL interventions in motivating, self-efficacy, and improving academic performance among biology students. Using the PRISMA 2020 framework, the study systematically analysed peer-reviewed articles published between 2014 and 2025 across the Google Scholar, Mendeley, and ResearchGate databases. A total of 84 relevant articles were initially identified, of which 16 studies met the inclusion criteria after the screening process. The findings reveal that SRL interventions consistently have positive effects on students' academic performance, with most studies reporting significant improvements in achievement. However, results on motivation and self-efficacy were mixed, with some studies reporting significant positive effects while others found no significant differences. The analysis revealed that only 16% of reviewed studies focused on pre-service teachers, highlighting a significant gap in teacher-centered SRL research. The review underscores the need for structured and context-specific SRL intervention modules tailored to biology education, particularly in developing-country contexts.
Comparative Study of Genetic and Memetic Algorithms for the Optimization of Customer Segmentation in Business Intelligence
Customer segmentation is a fundamental component of Business Intelligence, enabling organizations to identify homogeneous customer groups for personalized marketing, customer relationship management, and strategic decision-making. However, the performance of conventional clustering algorithms is often constrained by their sensitivity to data characteristics and local optima, motivating the application of evolutionary optimization techniques. This study compared the effectiveness of Genetic Algorithm (GA) and Memetic Algorithm (MA) in optimizing selected clustering models for customer segmentation. Customer transaction data were gathered and preprocessed using the Recency–Frequency–Monetary (RFM) framework, after which K-Means, Hierarchical Clustering, and DBSCAN were implemented as baseline clustering models. Each baseline model was subsequently optimized using GA and MA, and their performances were evaluated using the Silhouette Score, Davies–Bouldin Index (DBI), Within-Cluster Sum of Squares (WCSS), and runtime. The results showed that evolutionary optimization substantially improved the performance of Hierarchical Clustering, increasing the Silhouette Score from 0.4219 to 0.4816, reducing the Davies–Bouldin Index from 0.8027 to 0.7324, and decreasing the WCSS from 4188.94 to 3803.29. In contrast, K-Means exhibited virtually identical performance before and after optimization (Silhouette Score: 0.4818 versus 0.4816), indicating that the baseline solution had already converged to a near-optimal clustering configuration. Although both evolutionary approaches produced comparable results, the Memetic Algorithm achieved the best clustering quality with the lowest DBI (0.7324) while maintaining similar computational performance to the Genetic Algorithm. DBSCAN-based optimization produced non-evaluable clustering outputs under the experimental conditions because valid cluster structures could not be consistently formed. The study was limited to RFM-based customer transaction data and three clustering algorithms. Future research should investigate larger and more diverse datasets, alternative evolutionary optimization strategies, and additional clustering techniques to further enhance customer segmentation in Business Intelligence applications. The novelty of this study lies in providing a unified comparative evaluation of Genetic and Memetic Algorithms across centroid-based, hierarchical, and density-based clustering models, thereby offering practical insights into the suitability of evolutionary optimization techniques for customer segmentation in Business Intelligence.
Model Comparison, Uncertainty Quantification and Scenario-Based Projections of Nigeria Population Dynamics Using Classical and Fractional Growth Models
Precise population projections are indispensable for the effective formulation of sustainable development strategies, judicious resource allocation, and robust infrastructure development within burgeoning economies. Nigeria, as the most populous nation on the African continent, continues to exhibit substantial demographic expansion, consequently imposing escalating demands upon its healthcare, educational, housing, employment, and food systems. This investigation delves into Nigeria's population dynamics through the application of four distinct growth models: the Exponential, Logistic, Fractional Malthus, and Fractional Logistic models. Annual population data spanning the period 1991–2020 were meticulously calibrated employing a weighted parameter estimation methodology, wherein high weight was accorded to data derived from official census years. Model efficacy was rigorously assessed utilizing a suite of statistical criteria, including Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Parametric uncertainty was quantified via bootstrap confidence intervals, while comprehensive sensitivity and scenario analyses were executed to explore divergent demographic trajectories. The Fractional Malthus model yielded the optimal fit to Nigeria historical data between 1991-2020, by achieving the lowest RMSE (3.0944) and MAPE (1.9542%) and projected a population of approximately 680million by 2060. The derived fractional order substantiates the presence of memory effects inherent in Nigeria's population growth process. Baseline projections forecast Nigeria's population to attain approximately 680 million by 2060, with plausible confidence bounds ranging from 604 to 727 million. This study introduces a unified fractional demographic modelling framework, which comprehensively integrates memory effects, uncertainty quantification, sensitivity analysis, and policy-oriented scenario planning, thereby constituting a significant extension of extant approaches to long-term population forecasting in developing economies. The findings demonstrate the effectiveness of fractional-order models for long-term demographic forecasting and policy planning. The results demonstrate that fractional-order models, particularly the Fractional Malthus model, provide a more reliable framework for long term population forecasting in Nigeria than classical growth models.
Availability, Accessibility and Utilisation of Sex Education Information Resources among Undergraduates in a Nigerian University of Education
Access to accurate sex education information resources (SEIR) and its utilisation are critical for reproductive health well-being and informed decision-making of humans including undergraduates. However, the extent to which these undergraduates find and use reliable information remains inconsistent. Therefore this study examined availability, accessibility and utilisation of sex education information resources among undergraduates in Tai Solarin Federal University of Education. The descriptive survey research design of the ex-post facto type and mixed method of quantitative and qualitative method was adopted for the study. The population of the study consisted of undergraduates across the six colleges in the university. Using proportionate stratified random sampling technique the undergraduates were stratified based on the Colleges. From each stratum a proportionate 5% (1,001) sample size was drawn. Questionnaire and Focus Group discussion was used as the data collection instrument. A cohort of 18 students were randomly selected based on ballot from 38 academic departments across the six colleges. Findings revealed that hospitals (63.1%), primary health care centres (61.1%) and social media platforms (612, 61.1%) were moderately available while the most accessible resources were Radio/television (583, 58.2%), and family members (543, 54.2%). The findings further revealed a strong positive relationship between availability and accessibility (r = 0.762, p < 0.01), accessibility and utilisation(r = 0.783, p < 0.01) of SEIR. The study concluded that the strong positive relationship between availability, accessibility and utilisation of SEIR indicates that convenience, privacy, and ease of access are the primary determinants of whether the undergraduates will actually use the SEIR. The study recommended that the university should incorporate sex education in the curriculum of GNS course for all undergraduate students.
An Adaptive Milne Predictor-Corrector Method for Harvested Logistic Growth Model: Performance Compared with Standard Approaches
Population models that incorporate harvesting are essential for the sustainable management of renewable biological resources such as fisheries, forests, and wildlife populations. The harvested logistic growth model dx/dt=rx(1-x/K)-H, provides a simple yet effective framework for examining how populations respond to continuous exploitation, including conditions leading to persistence, equilibrium, or collapse. Although analytical solutions exist under ideal assumptions, practical applications often involve long simulation periods and dynamics near critical thresholds, thereby necessitating robust numerical techniques. This study develops an adaptive Milne predictor–corrector method equipped with local error estimation and automatic step-size control, and evaluates its performance against the classical fourth-order Runge–Kutta (RK4) method and the fixed-step Milne predictor–corrector scheme. The proposed method was implemented in MATLAB and tested using two biologically realistic parameter sets representing slow- and fast-growing populations under low, critical, and above-critical harvesting regimes. Numerical results show that all three methods achieved comparable levels of accuracy when validated against high-precision reference solutions. However, the adaptive Milne method consistently reduced the number of integration steps by approximately 50% in smooth regimes and by an even greater margin during population collapse, thereby improving computational efficiency. The study also reveals that the classical fixed-step Milne method may exhibit step-size dependent instability, particularly for systems with higher intrinsic growth rates, whereas the adaptive approach effectively mitigates this limitation through automatic adjustment of the step size. Furthermore, all methods predicted extinction times with errors of less than 1% relative to the reference solutions. These findings demonstrate that adaptive multistep methods provide an accurate, efficient, and numerically stable alternative for simulating harvested populations, particularly in applications involving repeated scenario analysis and decision-making near ecological thresholds.