e-Infrastructure and e-Services for Developing Countries

The paper explores the current challenges faced in the collection of property tax and conceptualized the Information and communication technology solution to address some of the identified challenges. By addressing the identified challenges, this research will enable the government to collect more revenues from property taxes to be used to undertake development projects. A case study approach was used to gather information from two regions, Dodoma and Dar Es Salaam whereby a total of 150 building owners, 10 property tax collectors and 10 systems administrators were purposively selected to respond to the questionnaire and Interviews. The findings showed that property tax collection is still facing several challenges, such as lack of comprehensive taxpayer’s education programme, higher property tax collection costs, a significant number of property tax defaulters, time-consuming payment method and impractical enforcement measures. In addressing some of these challenges, a framework that utilizes the prepaid metering system used by Tanzania Electricity Supply Company to provide electronic property tax enforcement mechanisms has been proposed by this study. The Design Science Research paradigm was used to aid the design of the framework.

Mobile banking is used to perform balance checks, account transactions, payments, credit applications, and other banking transactions via a mobile device. Until recently, mobile banking was most often done via SMS or the mobile web. In west African countries, these applications are preferred before all others of the same kind due to their proximity and ease of use. However, in recent years, several end-users have fallen victim of attacks aiming at misappropriating their money. In this scenario of attacks, the end-user is the most affected. Unfortunately, there is a crucial lack of information regarding the tricks used by attackers on them, users, not allowing the victims to protect themselves. In this paper, we propose a comprehensive study on Orange Money attacks in the Burkina Faso context. We analyze the different Facebook forums to identify recurring attack methods from the user’s point of view. In the end, we propose the bests practices that users should follow.

Afan Oromo is the language of the Oromo people, the largest ethnolinguistic group in Ethiopia. Written Afan Oromo uses Latin alphabet. In electronic communication systems letters in the alphabet are represented with standard ASCII-8 code, which uses 8 bits/letter, or UTF-8 fixed length encoding, which uses 16 bits/letter. Moreover, the language uses gemination (i.e., doubling of a consonant) and long vowels are represented by double letters, e.g., “dammee” to mean sweet potato. From information theoretic perspective, this doubling and fixed length encoding schemes add redundancy in written Afan Oromo. This redundancy, in turn, contributes for inefficient use of communication resources, such as bandwidth and energy, during transmission and storage of texts written in Afan Oromo. This paper aims at utilizing information theory to estimate entropy of written Afan Oromo. We use higher-order Markov chain, also called N-gram model, to compute the entropy of a sample text corpora (or written source) by capturing the dependencies among sequence of letters generated from the corpora. Entropy measures average information in bits per letter or block of letters, depending on the N-gram considered. Entropy also indicates the achievable lower bound for compression when using lossless compressions such as Huffman coding. When modeled as a first order Markov chain (i.e., assuming memoryless source where sequence of letters from the source are occurring independent of each other), the entropy of the language is 4.31 bits/letter. When compared with ASCII-8, the achievable compression level is about 46%. When N = 19 the estimated entropy is as low as 0.85 bits/letter; this corresponds to about 89% compression level. Huffman and Arithmetic source coding algorithms are implemented to check the achievable compression level. For the collected sample corpora, the average compression by Huffman algorithm varies from 42.2%−64.9% for N = 1 − 5. These compression levels are closer to the theoretical entropy. With increasing demand of the language in telecom services and storage systems, the entropy results show the need to further investigate language specific applications, like compression algorithms.

Recent trends show an increase in risks for personal cyberattacks, in part due to an increase in remote work that has been imposed by worldwide Covid-19 lockdowns. These attacks have further exposed the inefficiencies of the paternalistic design of Internet security systems and security configuration frameworks. Prior research has shown that users often have inadequate Internet security and privacy mental models. However, little is known about the causes of flawed mental models. Using mixed methods over a period of nine months, we investigate Internet security mental models of users in Africa and the implications of these mental models on personal security practice. Consistent with prior research, we find inadequate Internet security mental models in self-reported expert and non-expert Internet users. In addition, our mental modelling and task analysis reveal that the flawed security practice does not only result from users’ negligence, but also from lack of sufficient Internet security knowledge. Our findings motivate for reinforcing users’ Internet security mental models through personalised security configuration frameworks to allow users, especially those with limited technical skills, to easily configure their desired security levels.

This paper introduces a case study to describe a portfolio of digital health platforms and services to cater for digital health interventions in Masvingo Province, Zimbabwe. The portfolio resulted from of a concerted effort of stake- and relationholders in Masvingo Province, under the guidance of the Ministry of Health and Child Care. It contains digital health platforms sustaining digital health services to cater for digital health interventions. This study shows an enactment in digital health from the so-called peripheries in Africa.

This paper presents a narrative on developing a COVID-19 digital information and consultation platform for a province situated in southern Zimbabwe. In response to a WHO prediction and call to prepare for the worst, a team of medical and computer experts worked on a sovereign digital platform facilitating COVID-19 triaging over the phone. Ethnographic assessments revealed that platform developments benefitted from national dialogue, engaged communities and stakeholders, and use of locally available technologies and skills. In this paper, the development of this digital platform is placed in the broader facets of local development, data sovereignty, and growth of local capacity and abilities under the auspices of the Zimbabwe Ministry of Health and Child Care. The paper reflects on the facets involved in developing this digital platform for digital health interventions aligned with local capacity and needs.

Land management in Benin is a great challenge for its administration. Due to the nonexistence of appropriated tools, land transfer cases end up in litigation. Several court judgements refer to them when the people concerned do not deliver justice.The goal of this paper is to design a secure system for land management using Ethereum blockchain technologies. In doing so, landowners should be able to visualize information about the areas and locate them through a web mapping application by using LeafLet library.The solution we propose is mainly usable by notaries and landowners. Nevertheless, all citizens can visualize on an online map, all the lands of the system. This way of doing so, we have a secured land solution based on Ethereum blockchain which is accessible to all.Thus, our solution will be associated with the current land management process and land fraud will be reduced consequently since we cut out the middlemen.

Real-time network traffic classification is vital for networks to implement Quality of Service (QoS) traffic engineering. Deep learning techniques have proven to be effective for classification tasks, even when the traffic is encrypted. The pursuit for higher accuracy has incentivized implementations of deep learning models that are larger and slower, and require higher computational resources. This poses a problem for real-time online classification, particularly in low resource environments. This paper considers the trade-off between prediction speed and accuracy for the packet-based network traffic classification tasks when computing resources are limited. We build and compare 1D Convolutional Neural Network (1D-CNN) and the Multilayer Perceptron (MLP) models of various sizes with varying packet payload lengths used as input. These deep learning models are further compared to Support Vector Machine (SVM) models across the same metrics. The models are evaluated on six different sets of hardware constraints that are likely to be found in low-resource community networks. The study finds a clear trade-off between prediction rate and attainable accuracy. Our results suggest that MLP can achieve sufficiently fast prediction in community networks with middle-range CPUs, and for the most powerful of CPUs, a 1D-CNN should be the preferred model.

Network traffic classification plays an important role in quality of service engineering. In recent years, it has become apparent that deep learning techniques are effective for this classification task, especially since classical approaches struggle to deal with encrypted traffic. However, deep learning models often tend to be computationally expensive, which weakens their suitability in low-resource community networks. This paper explores the computational efficiency and accuracy of two-dimensional convolutional neural networks (2D-CNNs) deep learning models for packet-based classification of traffic in a community network. We find that 2D-CNNs models attain higher out-of-sample accuracy than traditional support vector machines classifiers and the simpler multi-layer perceptron neural networks, given the same computational resource constraints. The improvement in accuracy offered by the 2D-CNNs has a tradeoff of slower prediction speed, which weakens their relative suitability for use in real-time applications. However, we observe that by reducing the size of the input supplied to the 2D-CNNs, we can improve their prediction speed whilst maintaining higher accuracy than other simpler models.

Online money lending platforms also referred to as peer-to-peer lending platforms are a mean of lending and borrowing money between private individuals mostly via an online web-based platform. Transactions are often done without the direct arbitration of financial institutions, such as banks or other financial agencies. In 2005 the first money lending platform called Zopa was created in the United Kingdom, and several other have since followed. The exponential growth of Internet has been instrumental to the widespread adoption of peer-to-peer lending, as transactions can now be carried from anywhere via the Internet. However, despite the comfort, security of transactions and fraud prevention is still a challenge. The objective of this paper is to develop a Cloud-based money lending platform using both direct and reverse auction mechanisms based on blockchain technology to secure transactions between lenders and borrowers. The developed prototype uses knapsack and perfect sum algorithms in both direct and reverse auctioning mode, to determine the least compromising match between loan requests and offers. After rigorous testing, obtained results showed that indeed the system can meet user requirements in a secure manner.

Decision making has been highlighted important in asset management. Various generic decision making models were proposed and highlight the necessity for continuous autonomous suggestion of actions. On side of Maintenance activities priority for complex equipment, maintainers are manually deciding on crucial action to be performed prior to others. We claim that the integration of knowledge based expert system might support an effective predictive maintenance through accurate maintenance priorities. With purpose to enhance the decision making on maintenance priority, this work proposes the Fuzzy logic based expert system for small and medium sized hospitals. It considers the expertise of maintainers in faults detection and classification through the various monitoring of the physical condition parameters from equipment’s components. Parameters’ condition severity in respect of the total equipment downtime are considered to suggest maintenance activities priority. The proposed system is evaluated using random data in respect with the operating parameters’ values range and the results show that the created Fuzzy expert system is capable to provide the maintenance activities priority by evaluating the inputted variables.

DVB-T2 is the second generation of terrestrial broadcasting standard widely adopted or deployed in Europe and Africa. During the network planning, the channel propagation is the challenging element for broadcasters. As it is known to be probabilistic, many methods are used to predict or evaluate the channel Power Delay Profile (PDP). The measurement method is the one used in the field trials to record the PDP at several locations and to evaluate the channel profile. Obviously, measurement errors occur on the PDP recorded values leading to the system performance evaluation flaw. Also, 5G candidate waveform Filtered Bank Multicarrier (FBMC) has been previously proposed as an alternative to Cyclic Prefix - Orthogonal Frequency Division Multiplexing (CP-OFDM) in DVB-T2 system to increase the spectral efficiency and to improve the minimum Signal to Noise Ratio required for reception. This work analyses the channel uncertainty in the simulation of native DVB-T2 and enhanced FBMC based DVB-T2 systems transmission performance using a channel PDP from field measurement in Belgium. This paper shows that uncertainties values tolerable for a good signal quality, using the quasi error-free (QEF) criteria are between 0.1 dB and 0.5 dB and between 10 and 50 ns, respectively in power and delay. Moreover, it provides details about uncertainty values predictable for FBMC based DVB-T2 case which is known to be 133% spectrally efficient compared to the 100% of native DVB-T2.

Information security is an important aspect of every organisation today, specifically in Sub Saharan African (SSA) countries whose economies are perceived to be a growing home ground for cyber criminals. Whilst studies on information security policies (ISP) have offered understanding as to why threat agents do not comply with ISP; this understanding comes mainly from the developed economies, thereby giving a generalised view of ISP compliance. This study identifies the factors influencing ISP compliance within emerging economies of SSA. Following a literature review synthesis of the information security terrain, the findings show that ISP compliance is influenced by three main factors of individual characteristics, organisational and environment characteristics. Further, the findings show how the lack of institutional structures that require organisation to abide to both normative and cohesive pressure; influences organisations not to seek information security legitimacy This then influences how threat agents respond to ISP compliance. The implications of these findings for practice and policy are highlighted.

The popularization of the Internet of Things (IoT) (IoT) has made it possible to optimize and significantly improve the agricultural system. Thanks to this technology, farmers and ranchers are more confident in running their farms, which are now smart farms. By using New Information and Communication Technologies as a catalyst and essential facilitator for the development of this new style of agriculture. Several devices and mechanisms are used in intelligent systems related to agriculture. In this article, we review 30 scientific works produced and published by certain researchers and which sufficiently review the different dof management of smart farms without being exhaustive. This study aims to present a state of the art of integrating Internet of Things (IoT) into the agricultural environment to understand the improvements it brings and detect future smart agriculture issues. It turns out that smart agriculture is really at the heart of researchers, farmers, producers, and even states. The results of this state of the art show us that various devices and technologies are deployed in smart farms to ensure better performance. Depending on requirements and realities, each intelligent system should be adapted. This reflection helps to present directions for the development of intelligent farms that would be applicable and adaptable locally for the rest of our work.

Adoption of eHealth among healthcare providers in Uganda is still facing numerous challenges despite several studies indicating the potential of digital health systems in improving health outcomes. Therefore, this study set out to investigate the barriers and facilitators of eHealth adoption among healthcare providers in Uganda. A cross-sectional study using a quantitative approach was used to collect data from 216 healthcare providers working in 78 health facilities covering a period of October 2020 – March 2021. Analysis was done using Pearson’s Chi-square and descriptive statistics. Main findings indicated that 59% of the respondents had never used any eHealth system prior to the study. The regional distribution of eHealth uptake showed that Kampala had the highest users 61 (69%) while Gulu had the least 4 (5%). Employing a .05 criterion of statistical significance, the findings reveal that eHealth adoption and education level (χ2 = 40.72, ρ < 0.05), age (χ2 = 13.08, ρ < 0.05), location (χ2 = 20.96, ρ < 0.05), gender (χ2 = 4.40, ρ < 0.05) and institutional place of work (χ2 = 49.67, ρ < 0.05) are statistically significant. Furthermore, training users, ease of use, usefulness of the system and communicating eHealth benefits (µ = 4.15 ± .758, µ = 4.05 ± .888, µ = 3.76 ± .836, µ = 3.93 ± .827) had the highest mean contribution as facilitators of eHealth adoption, respectively. Any policy that targets integration of eHealth should take into account the demographic characteristics of health professionals, while paying attention to the organizational and technological factors. Future research should investigate eHealth adoption in patients and hospital administrators.

Businesses and service consumers should take advantage of social media’s ability to adapt their marketing campaigns to achieve a long-term strategic advantage. Setting quantitative and attainable expectations is critical to the progress of every marketing or business endeavour. The development of tools for analytics and cognition (TAC) is essential for customers and providers to increase productivity and inject intelligent insights into operational and mission-critical social media businesses through driven analytics. In this paper, the developed tools provide guided analytics software for intelligent aggregation, cognition and interactive visualization with a monitoring dashboard for concrete crowd journalism use cases. The provider receives an approach to a guided analytic dashboard filled with meaningful business visualization predictions. Among the other things, he can inspect the quantitative metrics for a sharing economy and estimate stakeholders’ channel monetization as a new innovative quantified value by engaging users with trusted content. TAC uses this principle of engagement rate measurements and provides visualization insights for stakeholders to choose the right track for boosting their business.

The treatment of records with several discrete missing values present in the databases is still a delicate problem. Indeed, these records can bias the results of data mining algorithms, thus invalidating the results. In this paper, we present an extension of the Hybrid Method for Efficient Imputation of Discrete Missing Attributes (HMID) to effectively handle these records. The method consists of partitioning the database into two subsets, one containing complete records and the other incomplete records. From the complete set, decision trees for all missing discrete attributes are created. The multiple missing records can be in the same leaf or in different leaves. In the same leaf, they are estimated directly by the HMID method. Otherwise, the sheets containing them are merged into a horizontal segment to determine the dominant modality of the complete attributes. In which case, multiple records are estimated. We evaluate our algorithm using two databases. The Adult dataset extracted from the UCI Machine Learning database and SH_CDI_Single extracted from the World Bank database. Finally, we compare our algorithm with four imputation methods using the accuracy of missing value estimation and RMSE. Our results indicate that the proposed method performs better than the existing algorithms we compared.

The mobile telephony became one of the worldwide most important technology, allowing wireless communication at any moment and virtually at any place. It is based on electromagnetic field (EMF) radiation from its network base stations, which inevitable cause EMF expansion in surrounding. However, the new base stations are followed by strong controversy, increasing concerns of the public, regarding potentially dangerous health effects of the EMFs. Thus, there is a constant demand for overall investigation and supervision of existing EMF exposure. In last decade, the wireless sensors networks emerged as an innovative solution for effective monitoring of EMFs in the environment. The latest established is the Serbian EMF RATEL network, which offers a sophisticated approach of telecommunication service-based EMF monitoring. This network performs wideband monitoring, taking into account the contribution of all active EMF sources in the vicinity of specific location. In this paper, the new role of the EMF RATEL system is considered, analyzing it as a smart solution for an emergency management. The perceptible causality of the COVID-19 on EMF radiation in campus area of University of Novi Sad is investigated, regarding the extensive activities of students in this part of the city of Novi Sad.

While professional pollution monitoring stations are used worldwide to measure levels of pollution, they are usually costly and sparsely deployed across cities, hence leading to a visibility gap in pollution maps that need to be filled through alternative solutions. This paper proposes a pollution monitoring model designed within the “Environment 4.0” project to showcase how fourth industrial revolution technologies such as the Internet-of-Things (IoT) can fill such visibility gap using low cost off-the-shelf devices. The validation of our approach was done by developing two prototypes of a pollution monitoring system. These include a system built upon a Raspberry Pi and an android based IOIO micro-controller. Using a testbed experimentation approach, the two systems were validated through a number of scenarios, where both air and noise pollution levels were measured in certain locations of the city of Lubumbashi in the Democratic Republic of Congo. The relative values obtained from the two IoT devices validate the designed systems as they revealed that i) heavy traffic locations experienced higher air pollution ii) the average level of PM2.5 outside buildings over one day of observation was lower in less densely occupied suburbs compared to the city center and iii) high noise levels were observed in locations referred to in our experiments as “red light districts” which were expected to be more noisy because of the type of activities carried in such locations. The experimental results revealed that the designed system will indeed make it possible to address the visibility gap problem in the near future at an affordable cost.

Community networks are often associated with bandwidth constraints. The limited bandwidth capacity in community networks results in higher content delivery time (latency) and reduces quality of service. Unplanned cache placement in the community networks has the potential to result in higher delays and increased network traffic. This study evaluates cache placement and content distribution in a community network using a distributed caching strategy. Latency, throughput and video performance measurements were carried out for geography, delay and hop count cache placement. In this study, hop count cache placement resulted in the lowest average latency, highest average throughput and best video performance. Overall, the study shows lower average latency, higher average throughput and better video performance at the caches compared to the main server. This reinforces the effectiveness of content caching in improving network performance in wireless community networks.

The recent wave of the global Covid-19 pandemic has led to a surge in text-based non-technical cybercrime attacks within the cyber ecosystem. Information about such cyber-attacks is often in unstructured text data and metadata, a rich source of evidence in a digital forensic investigation. However, such information is usually unavailable during a digital forensic investigation when dealing with the public cloud post-incident. Furthermore, digital investigators are challenged with extracting meaningful semantic content from the raw syntactic and unstructured data. It is partly due to the lack of a structured process for forensic data pre-processing when or if such information is identified. Thus, this study seeks to address the lack of a procedure or technique to extract semantic meaning from text data of a cybercrime attack that could be used as a digital forensic readiness semantics trigger in a cybercrime detection process. For the methodology to address the proposed approach, data science modelling and unsupervised machine learning are used to design a strategy. This method process extracts tokens of cybercrime text data, which are further used to develop an intelligent DFR semantic tool extractor based on natural language patterns from cybercrime text data. The proposed DFR cybercrime semantic trigger process when implemented could be used to create a digital forensic cybercrime language API for all digital forensic investigation systems or tools.

Traffic flow classification is an important enabler in network design, capacity planning, identification of user requirements and possible tracking of user population growth based on network usage. In this paper, results from the Internet traffic flow characterization in 1 Mbps community network for a three-week snapshot representing three months of study show that during peak traffic, the network is overwhelmed and service degradation occurs. When the network is upgraded to 10 Mbps the network bandwidth utilization immediately increases dramatically to close in on the new capacity with 20% left unused during peak traffic. The situation gets worse one month later where the network utilization is only 3% away from the maximum capacity. Traffic categorization show that the applications crossing the network are legitimate and acceptable. Since 10 Mbps bandwidth is the capacity that is sustainable for the community and supported by existing technology, bandwidth management is essential to ensure the network remains usable and continues to provide acceptable user experience.

Software-Defined Wireless Sensor Network (SDWSN) is an architectural solution that separates control plane from data plane of the sensor nodes and allows centralized management of the entire network. They are used in several application areas such as military, environmental, industrial, and medical. However, to ensure centralized management, and to facilitate the reconfiguration of the WSN, a significant amount of control messages are exchanged between the controller and the sensor nodes. These exchanges lead to the high energy consumption of the sensor nodes. In this paper, we propose a solution named CMFR-CMQ which is implemented on the SDN-WISE architecture. It avoids not only the duplication of Flow Request messages but also congestion of sensor nodes. Analytical performance of this solution shows that it significantly improves the network routing overhead and the packet loss rate compared to SDN-WISE.

Markov chain is a powerful mathematical tool that is used to predict future state of a random process based on its present state, for classical or first-order Markov chain, and past states for higher-order Markov chain. Markov chain has a wide range of applications in various fields of science and technology. To mention some in the area of telecommunication systems: Internet page ranking; Internet traffic modeling; language source modeling in natural language processing for text compression and text generation applications; speech recognition; wireless channel modeling; spectrum occupancy prediction for cognitive radio; user mobility modeling, handover management, and operation status monitoring in cellular mobile networks; network service and maintenance optimization; and in Markov chain Monte Carlo simulation methods. The main objective of this paper is to review fundamental concepts in Markov chain for discrete sources with emphasis on its application in telecommunication systems. It introduces terminologies, method to compute transition probabilities from real data, computing different states of the chain, and possible applications areas. The focus is given for both classical and higher-order Markov chain as well as Hidden Markov models. While acknowledging a related lecture note published in 2010, which we came to know lately, this review includes additional topics, such as higher-order Markov chain and Hidden Markov models, and tries to present core ideas in less mathematically rigorous but more practicable way. We hope that interested researchers who wish to apply Markov chain for various applications will benefit from this review.

Over the past years, many algorithms have been proposed for task scheduling and congestion control in typical Networks, such as Round-Robbin, Greedy, and many others. Whilst these algorithms are very effective in their capacities to address the targeted problems yet to compare their performance concerning the level of stability they offered in respective network systems has been a gap in the research environment that needs to be addressed. Proper scheduling mechanism along with reliable algorithms does not often guarantee a stable network else, this article is proposing a very simple but popular technique known in the statistical world as Analysis of Variance (ANOVA) as a tool for relatively determining the level of stability based on the data that are generated from the network. An OMNET++ simulator was used to conduct the simulation of the network environment and the SDN toolkits called INET framework was installed on top of it to enable the deployment of both Greedy and Round-Robbin scheduling algorithms to run. The simple analysis from the ANOVA was able to determine the level of stability between the two samples of algorithms used in these experiments. The performance evaluation while determining the response time from each experimental setup discovered that assuredly ANOVA analysis is capable of determining network stability level as well as proof that the Greedy scheduling algorithm performs better in terms of stability level than Round Robin (RR).

The Like many other technological advancements, most African countries have not kept pace with the current developments in the mobile applications (‘apps’) arena. Among other reasons, this is attributed to lower penetration level of smartphones in these countries. On the other hand, the advancements associated with apps have not spared the agribusiness sphere. This is especially so given that one of the global challenges is that of producing enough food to feed the world population, which will grow to upward of 10 billion people by the middle of the current century. Amidst the scarce and fast reducing resources such as water and arable land, this need is direr in African countries whose economies are largely dependent on rain-fed agricultural sector. Under these circumstances, some of the mechanisms for increasing food production are: (1) increasing farm efficiency to produce more high-quality; (2) creating transparent and sustainable food supply chains; and (3) providing ability to track and trace food ingredients. Mobile apps have reached maturity and penetration levels sufficient to support these goals. The thesis of this paper is that if Africa’s small-scale farmers were to benefit from mobile apps, a re-imagined platform-based model approach for developing these apps is required. This was informed by the glaring gaps that were identified through a bibliometric analysis of relevant literacy followed by empirical study of such apps. The platform addresses two key challenges facing Africa’s small-scale farmers: (1) non-availability of market information around prices of agricultural produce, buyers, and markets; and (2) lack of accurate weather information. The platform’s ability to break geographical barriers is anchored on Platform Ecosystem Canvas and its design puts into consideration the technological realities of Africa’s small-scale farmers.

This work aims to develop an efficient and intelligent method for forest resource management. The objective is to implement an automatic tree species identification system based on 3D data obtained from terrestrial laser scans. The approach adopted concerns first the acquisition of 2D and 3D data, then the processing of LIDAR data and finally a process of identification of tree species by machine learning. A platform is designed and developed to meet this objective. The platform is a means that can be used by local researchers for the identification of tree species, providing a forestry database of the Wadi Cherrat arboretum.

Game-Based Learning (GBL) and Computational Thinking (CT) have become essential tools in education as many studies successfully proved it effectiveness. However, the integration between both concepts into learning application is still lacking. Hence, this research aims to determine the effectiveness of using GBL applications which were developed by integrating CT concept for improving student’s problem-solving skills. Integrating CT in the GBL means applying the CT concepts (such as abstraction, algorithm, decomposition, automation, and evaluation) through digital games specifically developed for computer science course in a primary school. In evaluating the effectiveness of the GBL for CT, this research has utilized a quasi-experimental design and enrolled a control group with the pretest and post-test assessments. At the end of the experiment, the achievement scores of both groups were collected, and the difference in problem solving effectiveness between the experimental (N = 24) and control (N = 25) groups was examined. The results of the analysis showed that the average post-treatment scores of the treatment group were significantly higher compared to the control group. It shows that the application of GBL for CT has affected or contributed 67% to student achievement in problem solving. Therefore, the study proved that the GBL for CT is effective to improve student’s problem solving skills.

The pathogen of the disease COVID-19 is the extreme acute respiratory syndrome COVID-19 especially in the elderly and asthmatics. In our study, we examine if long-term exposure to air pollution raises the infection situations of COVID-19 in kingdom of Saudi Arabia (KSA). Through our studies, we proved that there is an associative relationship among the air pollution factor besides, the spread of COVID-19. As the results showed that compounds of air pollution such as Carbon monoxide (CO), Ozone (O3), Sulphur dioxide (SO2), Nitrogen dioxide (NO2), and PARTICLES (PM10), are severely related to the occurrence of COVID-19 due to the rate of the ratio of these areas more in the areas with the most prevalence of cases of COVID-19, so we used in our study the SIR model. It is considered one of the easiest, most reliable tools, consisting of three compartments; prone, contaminated, and removed. Besides, we utilized the Runge-Kutta method.

Augmented Reality (AR) is the integration of real-world objects with the use of information in the form of text, graphics, audio, and other virtual enhancements. In mobile augmented reality, a client can see virtual particles superimposed on live video of this display reality utilizing visual following or plan rendering. Any country’s economy is heavily reliant on its tourism sector. In our country, there are several tourism sectors, but they are not well-organized and attractive. Augmented reality is still a relatively new technology in Bangladesh. We’ve seen this technology applied successfully in a number of articles. To implement the tourism sector, we have proposed the development of augmented reality-based magazine books in our research. We used Vuforia, SDK for this and developed the graphics part ourselves. Then we have selected our magazine book places and designed the app accordingly using Unity3D. Finally, we have evaluated how effective this system could be, where we send the application and a google form to more than 50 people to use the app fill up the form and most of them were satisfied with the effectiveness of our magazine book in the tourism sector.

Online education is a teaching and learning environment over the internet. Hence, many educators and learners demonstrated using various innovations and technologies in delivering online education effectively during the outbreak of the COVID-19 pandemic. The purpose of this study is to analyse the status quo and the recent advancement of technology utilization in online education system either by e-learning, also known as, online learning since the introduction of internet in mid 1970s. A bibliometric analysis research approach was carried out using the Elsevier-Scopus database, analyzing a wide range of bibliometrics’ key indicators such as total number of publications and citation analysis coupled with bibliometric networking analysis using visualization of similarities software, VOSViewer available in the public domain. It was an interesting find as the number of publications had increased significantly plus online education is a developing subject area among academic researchers utilizing innovation and technology in their teaching and learning pedagogy.

This study explored the students’ experiences with using digital technologies to enhance learning in a conventional system of a higher learning institution. Specifically, it examined the types of digital technologies, usage and challenges students encountered. The study engaged second-year students taking Bachelor of Education in Adult and Community Education (BED ACE) at the University of Dar es Salaam. It applied questionnaires and semi-structured interviews to obtain requisite data. The study findings indicate that students mostly used mobile phones, laptops and USB flash/external drive to further their learning. Moreover, their use of applications such as Moodle enabled them to interact, access learning materials, get feedback on their assignments, and participate in discussion forums. Similarly, students used social media platforms such as WhatsApp and Facebook for academic and social purposes. Human and technology-related challenges limited students’ engagement in learning, especially off-campus. Despite the challenges the students faced, they still expressed their readiness to harness potentials associated with technology to add value and boost the quality of their learning. The study, therefore, calls for continuous improvement of human and digital infrastructure to support widely and further upscale intake of the applications of digital technologies among students.

Although learning materials for eLearning platforms are keenly developed, learner support has remained unreliable. Mainly, the focus has been on managing and easily delivering learning resources to learners. The aim of this study was to thoroughly analyze the participation trends of both teachers and students from one of the deployed learning management systems (LMS). To accomplish this objective, activities logfile from Halostudy LMS implemented for secondary schools in Tanzania for the period of 11 months were extracted and analyzed. The study found that learning support is not always evident as it is entirely reliant on subject teachers who were found to be not actively using the system. Drawing on reflection of this finding, this study provides analytical commentary on the consequences of relying on subject teachers for the provision of learning support. With this understanding, future work will look at the use of machine learning techniques to facilitate automatic recommendation and pairing of potential peers to students facing challenges in their learning.

This paper presents interim findings from the Foundations for All pilot. Foundations for All is a blended learning bridging program aimed at supporting refugee students and disadvantaged members of the host community in two Ugandan locations to access higher education. Through discussing the Foundations for All pilot’s teaching and learning design, multi-partner collaboration, use of technology, emphasis on a psychosocial support model, and learner-centred curriculum, we offer relevant practical perspectives applicable to using blended learning in teaching in emergency contexts like the Covid-19 global pandemic, as well as situations of conflict and displacement. Our interim findings contribute to practice through making concrete recommendations for other institutions wishing to embark on a similar model. We contribute to research by proposing a distinction between ‘thick’ models of refugee access programmes which offer blended or online content along with substantial psychosocial and other support, interaction with specialist tutors, contextually-relevant learning design and content, accessible technology and learning centres and financial support, along with meaningful exit pathways for students, against ‘thin’ models which offer curated online content for free to refugees without the additional support. A further contribution outlined in the paper is the role which expert psychosocial support can play in enhancing refugee learners’ engagement with teaching.