Farm records hold the static, temporal, and longitudinal details of the farms. For small-scale farming, the ability to accurately capture these records plays a critical role in formalizing and digitizing the agriculture industry. Reliable exchange of these record through a trusted platform could unlock critical and valuable insights to different stakeholders across the value chain in agriculture eco-system. Lately, there has been increasing attention on digitization of small scale farming with the objective of providing farm-level transparency, accountability, visibility, access to farm loans, etc. using these farm records. However, most solutions proposed so far have the shortcoming of providing detailed, reliable and trusted small-scale farm digitization information in real time. To address these challenges, we present a system, called Agribusiness Digital Wallet (ADW), which leverages blockchain to formalize the interactions and enable seamless data flow in small-scale farming ecosystem. Utilizing instrumentation of farm tractors, we demonstrate the ability to utilize farm activities to create trusted electronic field records (EFR) with automated valuable insights. Using ADW, we processed several thousands of small-scale farm-level activity events for which we also performed automated farm boundary detection of a number of farms in different geographies.

Clinical records capture the temporal, participatory, and interventional details of the care provision process. The exchange of these records plays a critical role in care continuity. Recently, there has been increasing attention on health data privacy and confidentiality which translates to questions on ownership and accessibility of clinical records. Traditional approaches to remedy this stand the risk of reducing the accessibility of these records, making care continuity across facilities more difficult. This poses a need for mechanisms that would enable the secure exchange of health data without adversely affecting the access to clinical records. This paper presents the Digital Health Wallet (DHW); a blockchain-enabled system that allows seamless clinical workflow orchestration and patient-mediated data exchange through consent management in a privacy-preserving manner. We conducted a preliminary test to benchmark the performance of DHW in resource-constrained healthcare facilities in developing countries.

We have been involved in the development of several blockchain-based solutions that largely utilize workflows. Workflows are used to guide users from independent organizations to process and manage transactions, data and documents in a trusted, immutable, and transparent manner for all relevant entities on a given blockchain network. This work discusses our approach to automate the process of creating, updating, and using workflows for blockchain-based solutions. In particular, we present a workflow definition schema using existing templates. We also show how the workflow definition is used to automate the generation of graphical user interfaces and the possibility of generating associated blockchain smart contracts in the future.

In this paper, we address the problem of improving data collection of the education system by presenting School Census Hub (SCH). The SCH concept emerged from field studies with stakeholders in Kenya. The goal of these studies were to help unlocking three key high-level requirements for the design of SCH. i) Budget allocation, allocating budget should be based on a verifiable number of active students and teachers, ii) Spending, spending on assets should be transparent and verifiable, iii) and, Improving learning environment, unlocking the limited insight into statistical relationship between school effectiveness and demographic variables. We present the overall architecture and design of SCH based on the findings from the field studies. The first version supporting a core set of capabilities for school data collection has been implemented. To evaluate the system, we conducted a large scale pilot in 97 schools. We report on a usability study of SCH that demonstrates user awareness and support for data acquisition and reporting in education management information system in Sub-Sharan Africa.

Several initiatives have been proposed to collect, report, and analyze data about school systems for supporting decision-making. These initiatives rely mostly on self-reported and summarized data collected irregularly and rarely. They also lack a single independent and systematic process to validate the collected data during its entire lifecycle. Furthermore, schools in developing countries still do not maintain complete and up-to-date school records. Due to these and other factors addressing the education challenges in those countries remains a high priority for local and international governments, donor and non-governmental agencies across the world. In this paper, we discuss our initial design, implementation, and evaluation of a blockchain-enabled School Information Hub (SIH) using Kenya's school system as a case study.

In this paper, we study the engagement and performance of students in a classroom using a system the Cognitive Learning Companion (CLC). CLC is designed to keep track of the relationship between the student, content interaction and learning progression. It also provides evidence-based engagement-oriented actionable insights to teachers by assessing information from a sensor-rich instrumented learning environment in order to infer a learner's cognitive and affective states. Data captured from the instrumented environment is aggregated and analyzed to create interlinked insights helping teachers identify how students engage with learning content and view their performance records on selected assignments. We conducted a 1 month pilot with 27 learners in a primary school in Nairobi, Kenya during their maths and science instructional periods. We present our primary analysis of content-level interactions and engagement at the individual student and classroom level.

We present the motivation, design, and preliminary study of a mobile-enabled, blended learning technology called the Cognitive Learning Companion (CLC). The CLC concept emerged from field studies with teachers and students in Africa. These studies led to two key high-level requirements that shaped the design philosophy of CLC: 1) seamless support for different modes of learning and teaching in a blended scenario (where a student learns in part through face-to-face interactions with a teacher in a classroom, and in part through a combination of teacher and system supervision/direction outside of class) and 2) support for tracking student engagement and sentiment during this blended learning journey, and the interplay of these affective processes with concept and skill-building processes as part of learning. In this paper, we discuss findings from the field studies and outline our approach to address the requirements. We present the overall architecture and design of CLC. The first version supporting a core set of capabilities for blended learning has been implemented as mobile applications for teachers and students. We conducted a limited pilot to test the technology in an actual classroom setting. We also report on a usability study of CLC that demonstrates user awareness and support for data-driven cognitive decision-making in education.

We report on the motivation and qualitative studies that examine the design of a sentiment and context collection tool in a mobile-enabled blended learning technology. The tool concept emerged from field studies with teachers and students from two primary schools in Kenya. In this paper, we discuss the background and motivation of learners sentiment and context. Next, we present the overall design of the proposed module and its prototype implementation in a blended learning environment. Detailed discussions on the algorithms underlying the tool are beyond the scope of this paper.

Several mobile-enabled solutions for education transformation have been deployed in Africa. Drawbacks of these systems include, a predominant focus on disseminating bulk learning content, student outcomes are measured solely on quantitative performance metrics, a lack of instrumentation to capture fine-grained user interaction data, and finally none of them seem to focus on capturing the contextual factors affecting learners. Hence, there are few chances to fully empower educators to create effective interventions for their students. In this paper, we present an adaptive event framework library that can be embedded within a blended learning environment. It enables the capturing of fine-grained learners activity stream data, including learners sentiment and context information. We conducted a limited controlled experiment to evaluate the effectiveness of the event framework. The real time visualizations provide useful insights to teachers in understanding their classroom and/or individual student engagement and progress. Finally, we outline challenges and preliminary solution how such system can be deployed in resource constrained environments.