This paper presents the role of ICTs in transforming Africa’s Education Systems (AES) in science, technology, engineering, and mathematics (STEM) subjects/courses. The paper highlights on a positive shift across Africa in using ICT to improve the quality of teaching and learning through activities such as intensive ICT skills training to teachers, increase in ICT equipments and applications in schools, and emergence of living labs (LLs) and innovation spaces/centres (InnoSpace). We first provide some of the challenges of integrating ICTs in education followed by a description of key past and current ICT initiatives supporting the adoption of ICTs in schools using a number of case studies in sub-Saharan Africa. We further present various ICT-based models for education, as a transformational approach towards integrating ICTs in AES. Moreover, we provide various ICT platforms deployed for education service delivery in disadvantaged African society (e.g., rural areas) including LLs and InnoSpace across the continent. Finally, we highlight our main findings and observations in terms of opportunities and future ICT for education research directions in Africa. Our aim is to provide some guidelines and ensure that Africa uniformly meet the 2030 United Nations Sustainable Development Goal number 4, which is to ensure inclusive and quality education for all and promote lifelong learning, particularly using ICTs.

Women farmers constitute the vast majority of agricultural producers in Tanzania; however, it is not clear where they obtain information on modern farming innovations. Knowing these sources will enable the government and international aid organizations to more effectively channel information for increased productivity, leading to national food security, increased incomes for farm families and promoting gender parity in Tanzania. This study, which was carried out among 300 women farmers in Hai and Kilosa districts of Kilimanjaro and Morogoro regions respectively, aimed at identifying their sources of agricultural information. The theory that guided this study was Rogers’ Diffusion of Innovations, especially Rogers and Shoemaker’s Source-Message-Channel-Receiver-Effect (SMCRE) model. The study, which yielded a high response rate of 96%, found that radio and agricultural extension workers were the primary sources of agricultural information for women farmers. Therefore, the researchers recommend that the Ministry of Agriculture, Food Security and Cooperatives (MAFC) should focus on community radio as the primary source of information dissemination to farmers and supply rural households with solar-powered radios free of charge or at subsidized cost. The finding that extension workers are women farmers’ second main source of information on farming innovations also defeats the general contention that male extension workers are inaccessible to female farmers. A peculiar finding was that even though women farmers have access to mobile phones they do not use them to obtain agricultural information, perhaps, because the MAFC does not have a system for farmers to call in for information.

In Tanzania, agriculture sector is known for employing more than 70% of the total population. Agriculture sector faces many challenges including climate change. Climate change causes low productivity in agriculture; low productivity is caused due to poor implementation of agricultural policies and strategies. This poor implementation of policies has also caused many farmers to be not competent in climate change adaptation. Over the years, provisions of agricultural advice and extension were provided by various approaches, including training and visit extension, participatory approaches, and farmers’ field schools. However, provision of agricultural advisory and extension service is inefficient. Also, in most cases the usage of most agricultural innovations and technologies developed is limited. A literature review indicates that the main reasons given by Tanzanian farmers for not using improved technology are not lack of knowledge or skill, but rather that the technologies do not contribute towards improvements (e.g., the technologies are not profitable or they imply to high risk). Thus, agricultural extension service needs to be geared towards teaching farmers how to develop innovative and cost effective technologies that are contextualized. Limited numbers of agricultural extension staff and less interactivity of Information and Communication Technologies (ICTs), such as radio and television, have been mentioned to be among the factors limiting the provision of agricultural advisory and extension services to the majority of farmers in Tanzania. The advancements in ICTs have brought new opportunities for enhancing access to agricultural advisory and extension service for climate change adaptation. In Tanzania, farmers and other actors access agricultural information from various sources such as agricultural extension workers and use of various databases from Internet Services Providers. Also there are different web – and mobile – based farmers’ advisory information systems to support conventional agricultural extension service. These systems are producing bulk amounts of data which makes it difficult for different stakeholders to make an informed decision after data analysis. This calls for the need to develop a tool for data visualization in order to understand hidden patterns from massive data. In this study, a semi-automated text classification was developed to determine the frequently asked keywords from a web and mobile based farmers’ advisory system called UshauriKilimo after being in use for more than 2 years by more than 700 farmers.

Besides OpenStreetMap (OSM), there are other local sources, such as open government data (OGD), that have the potential to enrich the modeling process with decision criteria that uniquely reflect some local patterns. However, both data are affected by uncertainty issues, which limits their usability. This work addresses the imprecisions on suitability layers generated from such data. The proposed method is founded on fuzzy logic theories. The model integrates OGD, OSM data and remote sensing products and generate reliable landfill suitability results. A comparison analysis demonstrates that the proposed method generates more accurate, representative and reliable suitability results than traditional methods. Furthermore, the method has facilitated the introduction of open government data for suitability studies, whose fusion improved estimations of population distribution and land-use mapping than solely relying on free remotely sensed images. The proposed method is applicable for preparing decision maps from open datasets that have undergone similar generalization procedures as the source of their uncertainty. The study provides evidence for the applicability of OGD and other related open data initiatives (ODIs) for land-use suitability studies, especially in developing countries.