http://repository.mocu.ac.tz/xmlui/handle/123456789/61 2026-04-07T12:44:28Z 2026-04-07T12:44:28Z Mpare, F.M Mohammed, M.M. Shayo, H.J. http://repository.mocu.ac.tz/xmlui/handle/123456789/2086 2026-03-06T09:31:18Z 2025-01-01T00:00:00Z

Online Learning Experiences and Adjustment Strategies D uring COVID 19: Mpare, F.M; Mohammed, M.M.; Shayo, H.J. The COVID-19 pandemic disrupted traditional education systems worldwide, necessitating institutions to adopt innovative teaching and learning modalities. While maintaining the learning programs amid the unpredictable plague, students, on the other hand, had to familiarize themselves with the introduced modality to match their institution’s demands. The cognitive constructivist learning theory, with an integration with Maslow’s theoretical framework, facilitated the examination of online learning experiences of postgraduate international students. Furthermore, they help,explain how these experiences have shaped students' perceptions in both their academic and personal lives, as well as their adaptation experiences during the transition back to a traditional learning modality. This research examined how learners adapted to changing learning approaches during the pandemic. Using a qualitative case study design, the study interviewed sixteen participants, including international students who experienced both online and in-person classes. It is intended to gain an understanding of their experiences with different learning modes. Findings suggested that students generally perceived in-person classes as more effective. The study’s insights can help educators and policymakers refine their teaching methods to better meet student needs, particularly when transitions between learning formats are required. Research Article

2025-01-01T00:00:00Z Katwale, Samwel Fujo, Mwapashua http://repository.mocu.ac.tz/xmlui/handle/123456789/2082 2026-03-06T09:17:31Z 2025-01-01T00:00:00Z

Enhancing Alumni Interaction and Progress Tracking through Innovative Web-Mobile Solutions Katwale, Samwel; Fujo, Mwapashua This study presents the design, development, and evaluation of a web-mobile alumni engagement platform for Moshi Co-operative University (MoCU). Recognising the critical role of alumni in supporting institutional growth and fostering lifelong academic relationships, the research aimed to create an inclusive, user-centred digital solution to enhance interaction, track alumni progress, and improve institutional communication. A mixed-methods approach was employed, involving a pre-implementation questionnaire to gather requirements from 100 purposively selected alumni and post implementation surveys to assess usability, adoption, and satisfaction. System development followed a User-Centred Design (UCD) framework integrated with iterative prototyping and stakeholder feedback loops. The platform featured tools such as alumni directories, job boards, mentorship channels, and event alerts, deployed on mobile and web channels to maximise accessibility. Results indicated strong platform adoption (75% frequent usage), high satisfaction (over 70% satisfied), and alignment between pre-identified alumni needs and implemented features. The system demonstrates the potential for ICT solutions to bridge alumni-institution gaps, especially in resource constrained contexts. The study concludes with recommendations for sustainable engagement, scalability, and continuous system evaluation.

2025-01-01T00:00:00Z Mkilia, Emmanuel L. Kaleshu, Jones Sife, Alfred S. http://repository.mocu.ac.tz/xmlui/handle/123456789/2081 2026-03-06T09:16:19Z 2025-01-01T00:00:00Z

Customers' perceptions on banks' cybersecurity and their use of mobile banking services in Tanzania Mkilia, Emmanuel L.; Kaleshu, Jones; Sife, Alfred S. In this contemporary era, mobile banking services enable customers to organise and accomplish cashless financial transactions using mobile devices. However, the general state of banks' cybersecurity systems significantly impacts the usage of banking services offered through mobile networks among customers. The analysis was performed to assess how customers perceive the cybersecurity systems of banks and their association with mobile banking usage. By adopting a cross-sectional research design under the guidance of the Unified Theory of Acceptance and Use of Technology (UTAUT), the Partial least squares structural equation modelling (PLS-SEM) analysis reveals that banks' cybersecurity systems' performance expectancy had a significant positive impact on the use of mobile banking services. Further, banks' cybersecurity systems' effort expectancy, significant others' comments and facilitating conditions significantly and positively influence bank customers to use mobile banking. Aligning with these findings, banks and financial institutions should prioritise and strengthen cybersecurity systems and simplify mobile banking processes for enhanced mobile banking adoption and usage among bank customers.

2025-01-01T00:00:00Z Mulinge, Damaris M. Madila, Shadrack S. http://repository.mocu.ac.tz/xmlui/handle/123456789/2041 2025-12-02T06:19:11Z 2025-01-01T00:00:00Z

Development of a machine learning model for precipitation forecasting in Kenya Mulinge, Damaris M.; Madila, Shadrack S. Accurate precipitation forecasting is important for mitigating the impacts of climate variability in Kenya, where erratic rainfall events considerably affect agriculture, water control and disaster preparedness. Traditional methods such as ARIMA (Autoregressive Integrated Moving Average) and NWP (Numerical Weather Prediction) have shown to struggle with complex weather patterns due to linearity assumptions, high computational demands and limited spatial resolution. This research develops and evaluates an XGBoost-based machine learning model to enhance precipitation predictions both long-term and short-term. Utilizing a a 20-year weather dataset (2004 - 2024) with 7300 daily data records sourced from online Visual Crossing Weather Data, key features include temperature, humidity, wind speed, lagged precipitation (1-7), rolling means and seasonal encoding to capture bimodal rainfall patterns of the months of march-May, and October-December. Data processing involved min-max normalization of 0-1 range, feature selection, sin/cosine transformations for seasonal patterns and temperature-humidity interactions for connective modelling processes. The dataset used was split with 80% for training and 20% for testing and a temporal split ≤ 2020 for training and > 2020 for testing maintaining the chronological data order. The initial attempts exhibited poor performance with low R2 = 0.066 and a high RMSE=1.06 hence leading to XGBoost binary classification shift to predict the likelihood of rain/no-rain tomorrow. Bayesian optimization and GridSearchCV hyperparameter tuning was applied with default 0.5 threshold adjustment for improved rain class sensitivity using classification metrics and resulted 76.76% accuracy, 70.14% precision, 33.36% recall, 45.12% F1-Score and ROC-AUC 0.75. Post-tuning accuracy by reducing the threshold to 0.3 to capture missed rainfall events: 73% accuracy, no-rain precision and recall 81%, 53% rain precision, 54% recall, F1-Score 54%. Temperature-humidity interaction as the top predictor in feature importance. The results contribute to improved precipitation prediction accuracy hence supporting decision making in agriculture, water resource management and early disaster preparedness in Kenya’s climate vulnerable regions. Global Journal of Engineering and Technology Advances, 2025, 24(03), 043-050 Publication history: Received on 26 July 2025; revised on 29 August; accepted on 01 September 2025 Article DOI: https://doi.org/10.30574/gjeta.2025.24.3.0261

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