Development of a Digi-Face Cross-Platform Mobile App for Online Teaching, Learning, and Research: A Case Study of African Higher Education Communities

Dr. Jonathan Ngugi, Pawala Janyan — Sun, 02 Feb 2025 00:00:00 +0000

This project report examines the design, development, and implementation of a cross-platform mobile application designed to enhance online teaching, learning, and research within African higher education communities. The project builds on the existing Digi-Face platform, which is a foundational tool for online education, particularly for Centers of Excellence in East Africa. Although the Digi-Face website connects these centers effectively, it lacks a dedicated mobile application, creating barriers to seamless access to online and offline educational resources. This gap limits the platform’s ability to offer an integrated, user-friendly interface that fosters collaboration and enhances engagement.

The primary objective of this study is to address this gap by developing a mobile application that provides an intuitive, accessible, and cross-platform solution for mobile devices. By doing so, the app aims to improve the user experience for students and instructors within the African Centers of Excellence in East Africa. The study employs a mixed-methods approach, combining qualitative and quantitative research techniques to guide the development process. Industry-standard tools and frameworks for cross-platform mobile app development were utilized, ensuring compatibility across a range of devices and operating systems, including Android and iOS.

The implementation results demonstrate significant improvements in the accessibility and usability of the Digi-Face platform via the mobile app. The app facilitates easy access to educational materials, collaborative features, and research resources. A comparative analysis with existing solutions reveals notable enhancements in user engagement, resource utilization, and overall learning experiences. However, the app's effectiveness is not without challenges. Issues such as device capability disparities, network infrastructure limitations in regions with poor internet access, and varying user familiarity with mobile technology may impact the app's functionality in certain contexts. Additionally, the mobile app requires ongoing updates and maintenance to adapt to evolving technological advancements and user needs. In conclusion, the development of the Digi-Face cross-platform mobile application represents a significant advancement in promoting online education within African higher education communities.

Benchmarking Machine Learning Models for Landslide Susceptibility: A Study in the Ngororero Sector

Jean de Dieu Hagenimana, Djuma Sumbiri — Sat, 08 Mar 2025 00:00:00 +0000

This study evaluates the performance of six machine learning algorithms: Decision Trees (DT), Neural Networks (NN), Support Vector Machines (SVM), Random Forests (RF), Gradient Boosting Machines (GBM), and k-nearest Neighbors (k-NN) for landslide prediction in the Ngororero sector, Rwanda. Using Sentinel-2 satellite imagery, meteorological data, and topographical datasets from 2015, 2019, and 2023, the study incorporates critical features such as slope, rainfall, soil type, and vegetation cover. The findings indicate significant temporal and algorithmic variations in prediction performance. K-Nearest Neighbors and Random Forest consistently achieved high accuracies, with kNN showing a value of 84% in the 2019 dataset and more than 80% in other datasets. Random Forest demonstrated robust performance with a 78.98% accuracy in 2015. The research concludes that k-nearest Neighbors and Random Forest are optimal for predicting landslides in the Ngororero sector.

Journal of Information and Technology

Development of a Digi-Face Cross-Platform Mobile App for Online Teaching, Learning, and Research: A Case Study of African Higher Education Communities

Benchmarking Machine Learning Models for Landslide Susceptibility: A Study in the Ngororero Sector