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Abstract Despite huge commitments, education quality and achievement gaps have remained low in developing countries, posing threats to the actualisation of the SDG-4 goals. A recent quantitative study found that a scholarship scheme that engages communities adopts hybrid selection criteria, and seeks en-masse motivation towards high test scores might be effective at improving education quality. Case Study interviews of 15 beneficiaries of the IFOMSSA Senior Scholarship, a scheme established in 2017 for high schools in Southeast Nigeria. Respondents confirmed that the Scheme’s hybrid (needs and merit-based) selection criteria, along with its en-masse motivation strategies and community involvement, all contributed to improved test scores and exam enrolments. Non-scholarship recipients were motivated as much as eventual recipients, affirming “mass motivation”. Community groups (family, friends, teachers, community leaders and government staff) had positive influences, while media publicity inspired confidence and trust. Scholarship schemes that integrate the advocated principles might hold the key to sustainably optimising test scores in developing countries. This expands options for improving education quality and enrolments.

Engineering Mathematics requires that problem-solving should be implemented through ongoing assessments; hence the prediction of student performance using continuous assessments remains an important task for engineering educators, mainly to monitor and improve their teaching practice. This paper develops probabilistic models to predict weighted scores (WS, or the overall mark leading to a final grade) for face-to-face (on-campus) and web-based (online) Advanced Engineering Mathematics students at an Australian regional university over a 6-year period (2013–2018). We fitted parametric and non-parametric D-vine copula models utilizing multiple quizzes, assignments and examination score results to construct and validate the predicted WS in independently test datasets. The results are interpreted in terms of the probability of whether a student’s continuous performance (i.e., individually or jointly with other counterpart assessments) is likely to lead to a passing grade conditional upon joint performance in students’ quizzes and assignment scores. The results indicate that the newly developed D-vine model, benchmarked against a linear regression model, can generate accurate grade predictions, and particularly handle the problem of low or high scores (tail dependence) compared with a conventional model for both face-to-face, and web-based students. Accordingly, the findings advocate the practical utility of joint copula models that capture the dependence structure in engineering mathematics students’ marks achieved. This therefore, provide insights through learning analytic methods to support an engineering educator’s teaching decisions. The implications are on better supporting engineering mathematics students’ success and retention, developing evidence-based strategies consistent with engineering graduate requirements through improved teaching and learning, and identifying/addressing the risk of failure through early intervention. The proposed methods can guide an engineering educator’s practice by investigating joint influences of engineering problem-solving assessments on their student’s grades.

Agricultural climate index-based-insurance (IBI) compensates farmers for losses from adverse climatic conditions. Using a systemic review, we show that research related to agricultural climate index-based-insurance efficacy and application is lacking in many climate and food security vulnerable countries. We concluded that there are countries with high climate and food insecurity risk based on several climate and food security indicators that lack agricultural climate index-based-insurance research that could help farmers in these countries. Research to date has also largely focused on cereal crops and drought, which only represent a fraction of the crops and climate risks that agricultural climate index-based-insurance could be beneficial in managing. Our paper provides evidence-based recommendations for countries that should be focused on to redress the current disparities in agricultural climate index-based-insurance research.

Wheat is key global food crop that is heavily influenced by climatic variability. There has been extensive research on improving forecasts and management practices to minimise climate related yield losses, but less on how to handle yield losses caused by climate variability. We investigated whether index insurance could be used to manage climate related losses, specifically from winter rainfall drought for wheat crops in Australia. We utilised 31 years of yield data from 15 of Australia’s key wheat producing regions. The winter rainfall index was developed and tested using generalised additive regression models, allowing for non-linear effects. Models with the winter rainfall index explained significant variation in wheat yields in each of the regions assessed. Wheat yield models had cross-validated R2 values > 0.5 for two-thirds of the 15 regions modelled and best explained wheat yields in the Mallee, Western Australia (cross-validated R2 of 0.70). Calculated fair premiums ranged from $8.62 to $77.1 AUD/ha, while maximum liability was $59.25 to $212.12 AUD/ha. Throughout the eastern most wheat growing regions the winter rainfall index was consistently inefficient (i.e. not beneficial). In contrast, the winter rainfall index was financially efficient (i.e. beneficial) in the western wheat regions of eastern Australia and parts of Western Australia, with benefits of up to $97 AUD/ha and loss reductions of $9 AUD/ha. The spatial variability in insurance efficiency was explained by rainfall variance. As rainfall variance increased the efficiency of the winter rainfall index insurance for wheat decreased. Our findings have two important policy implications; (1) in areas where climate change is anticipated to increase rainfall variability risk-transfer options, such as index insurance, may become less viable and as such policies that support the development of index insurance without acknowledging or adjusting for variability in its benefit could lead to inefficient outcomes for both government and agricultural producers; and (2) where index rainfall insurance is not efficient then greater emphasise may need to be placed on developing alternate types of index insurance (e.g. using satellites) and/or on risk-management and climate adaptation strategies that minimise losses. Keywords: Climate variability, Drought, Wheat, Risk transfer, Climate adaptation