COVID-19 and Sustainability in Higher Education

Munna, Afzal Sayed

doi:10.51629/ijeamal.v2i1.13

Cited by 4 publications

(2 citation statements)

References 11 publications

(12 reference statements)

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“…Indeed, one of the greater COVID-19 challenges for sustainability is the need for higher rates of unsustainable consumption to meet new needs (e.g., digital technology for learning or single-use plastics for minimizing viral spread). Munna [34] begins to further explore this in his reflective review. Indeed, sustainability is achieved through flexible models of work, stabilizing student demand, reprofiling resources, and supporting bridging financing.…”

Section: Goal 8: Decent Work and Economic Growth And Goal 9: Industry...mentioning

confidence: 99%

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Sustainability in Higher Education during the COVID-19 Pandemic: A Systematic Review

2022

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The COVID-19 pandemic has created cause for rapid innovation in, reimagining of, and pivoting of higher education institutions. Prior to 2020, the global higher education sector began to radically focus their efforts on creating sustainable institutions, and incorporated the United Nations’ Sustainable Development Goals (SDGs). The novel coronavirus pandemic may have changed that. This systematic review examines eight manuscripts, identified through a systematic search strategy on sustainability during the COVID-19 pandemic across 2020–2021. Interestingly, the low volume of manuscripts identified highlights potential learning and teaching risks, as priorities may have shifted during rapid digitalization and emergency remote teaching practices. These manuscripts focused on Goal 4, inclusive and equitable quality education (50%); Goal 8, decent work and economic growth; Goal 9, industry, innovation, and infrastructure (37.5%); and goal enabling through integrating and embedding sustainability into the curriculum (12.5%). The implications of this systematic review highlight a need to rebuild efforts to focus on the Sustainable Development Goals, particularly considering the evolving higher education landscape during COVID-19. While there were still considerable volumes of manuscripts on higher education and sustainability during 2020–2021, the lack of contextualization to current higher education conditions should be of concern for sustainability scholars. This systematic review creates a critical foundation for accelerating our understanding of achieving SDGs in higher education during and beyond the pandemic.

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Section: Goal 8: Decent Work and Economic Growth And Goal 9: Industry...mentioning

confidence: 99%

“…Only one of the manuscripts from the sample spoke explicitly and primarily to this, although others briefly mentioned consumption or production. For example, Munna [34] highlighted the need to stabilize European Union student demand in his review.…”

Section: Goal 12: Responsible Consumption and Productionmentioning

confidence: 99%

Sustainability in Higher Education during the COVID-19 Pandemic: A Systematic Review

2022

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Modeling of motivation of key executives of government agencies of regions using a multi-objective genetic algorithm

Yashin

Koshelev

Sukhanov³

2022

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Subject. This article explores the motivation of top managers of government entities to bring into line the interests of the population, the State and its key executives. Objectives. The article aims to create a model of motivation of key executives of government institutions of the regions, which will make it possible to make the intangible motivation of top managers contingent on the achieved strategic potential of the region and their financial incentives. Methods. For the study, we used a multi-objective genetic algorithm and the Pareto Frontier solutions set. Results. The article proposes a procedure for reaching a conclusion about the actual bonus award (incentivization) of key executives of government agencies of the regions. Conclusions and Relevance. The results obtained can be useful to government agencies to develop a rational system of financial and non- financial incentives of their senior leadership.

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Neural network modeling of motivation for government agencies’ top management of regions as a classification problem

YASHIN,

KOSHELEV,

KUPTSOV

2023

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Subject. The study deals with modeling the motivation of top managers of government agencies in regions to align the interests of people and the State. Objectives. The purpose of the study is to create a neural network model of motivation for top management of regional government institutions for a classification problem. Methods. Using neural networks, we simulate criteria for non-financial and financial motivation of the said top management, and criteria for strategic potential of regions. Financial motivation is defined as the salary of a senior civil servant, and non-financial motivation as his or her career growth. At the same time, the target function is a coefficient of natural population growth in regions, its positive value is assessed positively, and negative value negatively. As a result, the problem of binary classification in the trained neural network is solved. Results. Comparing the accuracy of the model in the considered example with accuracy that was obtained earlier, using logistic regression, we note that in the previous model, the total error in verification by the functions of non-financial and financial motivation and strategic potential was 39%. In our case, this error was only 12%. This suggests that neural networks enable to achieve much more accurate forecasting. Conclusions. The findings could be useful for regional government agencies to develop a constructive system of non-financial and financial motivation for their top managers.

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COVID-19 and Sustainability in Higher Education

Cited by 4 publications

References 11 publications

Sustainability in Higher Education during the COVID-19 Pandemic: A Systematic Review

Sustainability in Higher Education during the COVID-19 Pandemic: A Systematic Review

Modeling of motivation of key executives of government agencies of regions using a multi-objective genetic algorithm

Neural network modeling of motivation for government agencies’ top management of regions as a classification problem

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