UGBOWO CAMPUS

This study investigated the prevalence, types, and impact of anxiety on academic performance and wellbeing among undergraduate students at the University of Benin, Ugbowo Campus, Benin City, Nigeria. Anxiety, characterized by excessive worry, fear, physiological arousal, and cognitive interference, has become a pervasive mental health challenge among university students globally and in Nigeria, exacerbated by academic pressures, economic hardships, infrastructural deficits, erratic academic calendars, and socio-cultural factors. The study was guided by four specific objectives: (1) to determine the prevalence of anxiety and its relationship with academic performance; (2) to identify the prevalent types of anxiety; (3) to examine the relationship between anxiety and Cumulative Grade Point Average (CGPA); and (4) to explore the effects of anxiety on students’ overall wellbeing. It adopted an ethnographic research design with survey elements, underpinned by Power-Control Theory and Cognitive Interference Theory. A sample of 120 full-time undergraduate students was selected through multi-stage sampling techniques (random selection of faculties and departments, followed by stratified sampling by level of study and gender). Data were collected using a semi-structured questionnaire administered face-to-face and analyzed with descriptive statistics (frequencies, percentages, means, and tables) using SPSS. The findings revealed a high and escalating prevalence of anxiety among undergraduates, with levels significantly higher than at entry into the university. Test/examination anxiety was the most dominant type, followed by general worry, career/future anxiety, and financial worries. Anxiety was perceived to substantially impair concentration, study habits, class attendance, and CGPA, although moderate anxiety was noted by some as potentially facilitative. On wellbeing, anxiety was associated with poor sleep, physical symptoms, emotional exhaustion, social withdrawal, and strained relationships. Students predominantly relied on informal coping strategies such as prayer and peer support, with low awareness and utilization of formal university mental health services. The study concludes that anxiety constitutes a significant barrier to academic success and holistic student development at the University of Benin. It recommends the strengthening of institutional counselling services, integration of mental health education into academic programs, policy reforms to reduce academic stress, and broader governmental support for student mental health. These interventions are essential for improving academic performance, retention, and the overall quality of higher education in Nigeria. Keywords: Anxiety, academic performance, CGPA, undergraduate students, University of Benin, mental health, wellbeing, test anxiety.

In Nigeria, road construction is often followed by neglect, with little or no provision for maintenance. This accelerates pavement deterioration due to factors such as rain fall, traffic loads, poor drainage, and harsh environmental conditions. This study aims to evaluate the condition of a 0.25 km road connecting Ethiope River Road and the University of Benin Back Gate (Ugbowo Campus). The study involved a field survey to document visible deterioration, followed by an assessment using the Pavement Condition Index (PCI) and Pavement Condition Rating (PCR) methods. The procedure adhered to ASTM D6433-07 standards, with the roadway divided into 5 sample units. Each unit was inspected for distress type, severity, and extent, and PCI values were calculated and categorized from “Failed” to “Excellent.” The results were analyzed and mapped using ArcGIS software for spatial visualization. The calculated PCI values for the five sample units, S1, S2, S3, S4 and S5 were 75, 49, 62, 55, and 32 respectively. These values correspond to pavement condition ratings (PCR) spanning from Satisfactory to Very Poor. The overall PCI for the road section was determined by computing the average of the individual unit PCI values, yielding an overall PCI of 54.6 for the Ekosodin Road, which classifies the pavement as being in a poor condition according to ASTM D6433-07 standards. The GIS-based spatial analysis revealed a concentration of severe pavement distresses toward the mid and lower portions of the road, suggesting localized structural and drainage-related problems. The results (Overall PCI of 54.6 and the digitized road section) indicates that while the pavement remains marginally serviceable, it requires urgent maintenance and partial rehabilitation to restore its functionality and prevent further deterioration. The combined use of PCI, PCR, and GIS tools proved to be a reliable and effective approach for comprehensive pavement condition assessment and should be adopted for routine pavement management within the University of Benin and similar environments

This study investigates the influence of satellite constellation configurations on the positional accuracy of post-processed static GNSS data at the University of Benin, Ugbowo Campus. Static observations were collected at five known control points using a Tersus David30 receiver. Data was processed in Tersus Geomatics Office across seven constellation setups: GPS-only, GLONASS-only, BEIDOU-only, and their combinations. A detailed epoch-based analysis was also conducted at one control point using RTKLIB. Accuracy was assessed using coordinate residuals (∆E, ∆N, ∆H), RMSE, standard deviation, CEP, and 2DRMS, supplemented by classical
and robust statistics and time-series analysis. Results demonstrated that GPS-based solutions consistently delivered superior performance. The GPS+BEIDOU combination achieved the best accuracy (2DRMS = 0.160 m, CEP = 0.067 m), closely followed by GPS-only. In contrast, BEIDOU-only yielded the poorest results (2DRMS = 0.587 m), while GLONASS-only was notably weak and unstable. RTKLIB processing confirmed that multi-constellation setups, particularly GPS+GLONASS+BEIDOU, produced highly precise solutions with sub-centimeter standard deviations. Conversely, the GLONASS-only solution exhibited severe instability, with significant errors and outliers. Time-series analysis revealed that stable constellations maintained narrow error bands, while error spikes in other configurations corresponded directly to drops in satellite visibility