Educational Inequalities Research Articles

Gender inequality in adult education: a comparative study of four adult learning systems

Gender inequality in adult education: a comparative study of four adult learning systems

Unveiling And Addressing the Causes of Educational Inequalities in China

Unveiling And Addressing the Causes of Educational Inequalities in China

The Development of African American Education and The Causes and Effects of Racial Educational Inequality

The Development of African American Education and The Causes and Effects of Racial Educational Inequality

Reasons of educational inequality in UK

Reasons of educational inequality in UK

Digital Infrastructure – The Need to Overcome Digital Divide and Inequality in Higher Education

Digital Infrastructure – The Need to Overcome Digital Divide and Inequality in Higher Education

Bridging the gap for underserved populations: Personalized AI solutions for college access and learning support

AbstractThe evolution of artificial intelligence (AI) technology offers promising solutions to underserved populations in the United States. AI tools can reduce college access and academic barriers for low‐income students, first‐generation college students, racial and ethnic minorities, individuals with disabilities, and students living in rural areas. While AI tools offer support to college students from all demographics, they offer uniquely individualized assistance to underserved students to overcome the complex postsecondary schooling challenges they face. Examples of the nuanced, often complex, obstacles faced by these vulnerable students are discussed along with brief descriptions of AI tools that can help. AI tools leverage the power of high‐speed computing, expansive access to information, machine learning capabilities, and natural language processing. Utilizing the power of AI tools, vulnerable learners can increase their access to financial, academic, and social resources needed to attend and be successful in postsecondary education. AI tools also help close opportunity gaps as they provide means by which college students gain increased independence and control over their educational and career journeys. Note that the AI tools referenced in this article are included as examples only. We encourage ongoing discussion of AI‐powered tools and solutions to help underserved students gain more access to higher education and get individualized support for vulnerable learners.Practical Takeaways Highlighting the potential of AI in addressing educational inequalities. Using AI in higher education settings for educators to better understand and respond to student needs. Implementing AI usage in higher education settings for underserved populations to reduce dropout rates. Leveraging technology to offer tailored guidance, bridging gaps, and fostering community among the underserved populations in higher education.

Looking to the Past to Understand the Present: Legacies of Inequality, Status Changes, and Collective Memory in Burundi’s Post-War Schooling

How do efforts to reduce ethnic inequality in schooling interact with narratives of inequality and past grievances in schools after ethnicity-based conflict? In this paper, I analyse interviews with 114 youth in Burundi to explore the intersection of these narratives given a power-sharing agreement and changes in group status in post-war Burundi. I show that despite government efforts, narratives of exclusion and inequality in the past persist, and have become more complex. This study has implications for our understanding of the role of redressing education inequalities in short- and long-term peacebuilding processes.

Trends in educational inequalities in smoking-attributable mortality and their impact on changes in general mortality inequalities: evidence from England and Wales, Finland, and Italy (Turin)

BackgroundSocioeconomic mortality inequalities are persistent in Europe but have been changing over time. Smoking is a known contributor to inequality levels, but knowledge about its impact on time trends in...

Educational inequalities in blood pressure across the adult life course: Evidence from a 20-year follow-up study.

High blood pressure (BP) is a key contributor to the burden of disease. This study aimed to assess: a) educational differences across the entire BP distribution, and b) educational differences in the trajectories of BP across the adult life course. Longitudinal data from the Stockholm Diabetes Prevention Program was analysed using quantile regression and linear mixed effects models. Models were adjusted for age, sex, lifestyle, and BP medication. Lower educational level was associated with higher systolic BP (SBP) at all follow-up periods. Age and sex adjusted mean SBP was 2.49 (95% confidence interval (CI) 1.10, 3.87), 3.95 (95% CI 2.45, 5.45) and 2.61 (95% CI 1.09, 4.13) mmHg higher for people with pre-secondary education compared with post-secondary at baseline, 10 years and 20 years follow-up, respectively. Quantile regressions revealed that the inequalities could be observed across the entire BP continuum. Longitudinally analysed, people with pre-secondary education had 3.01 (95% CI 1.91-4.11) mmHg higher SBP than those with post-secondary education, age and sex adjusted. No significant convergence or divergence of the educational gaps in SBP was observed. Educational differences remained even after adjusting for lifestyle and BP medication. These results imply that public health interventions should aim to bring about distributional shifts in blood pressure, rather than exclusively focusing on hypertensive people, if they are to effectively minimize the educational disparities in blood pressure and its consequences.

Modifiable risk factors mediating the impact of educational inequality on heart failure: A Mendelian randomization study

BackgroundHeart failure (HF) is a rapidly growing global disease burden with high mortality rates. We aimed to utilize mendelian randomization (MR) analyses to investigate the association between educational attainment (EA) and HF, and to evaluate the contribution of modifiable risk factors as mediators. MethodsWe applied a two-sample MR approach based on the largest genome-wide association studies (GWAS) to investigate the causal relationship between EA and HF. Data collection was conducted in July 2023. We then conducted mediation analyses to explore whether body mass index (BMI), blood pressure, and type 2 diabetes mellitus (T2DM) mediate the effect of EA on HF, and utilized multivariable MR to estimate the proportion of mediation attributed to these factors. ResultsGenetically predicted 3.4 years of additional education was associated with a decrease in the risk of HF (OR 0.76 for each 3.4 years of schooling; 95% CI 0.72, 0.81). BMI, T2DM, systolic blood pressure, and diastolic blood pressure mediated 40.82% (95% CI: 28.86%, 52.77%), 18.00% (95% CI: 12.10%, 23.90%), 11.60% (95% CI: 7.63%, 15.56%), and 7.80% (95% CI: 4.63%, 10.96%) of the EA-HF association, respectively. All risk factors combined were estimated to mediate 63.81% (95% CI: 45.91%, 81.71%) of the effect of EA on HF. ConclusionHigher EA has a protective effect against the risk of HF, and potential mechanisms may include regulation of BMI, blood pressure, and blood glucose. Further research is needed to understand whether interventions targeting these factors could influence the association between EA and HF risk.

Energy poverty measurement and determinants in rural China: Does education inequality matter?

Due to the pressing need to attain sustainable development goal 7, global attention on addressing energy poverty has increased. Improving rural education has been recognized as a strategy to mitigate energy poverty in rural China. However, the presence of education inequality remains a substantial challenge to energy poverty alleviation and underexamined. Our research aims to evaluate rural energy poverty levels systematically and investigate the impact of education inequality on rural energy poverty. This study begins by employing a novel weighting approach to develop a comprehensive indicator system, which accurately measures the extent of energy poverty in rural China. Subsequently, a panel fixed-effects model is utilized to analyze the causal impact of education inequality on rural energy poverty. Furthermore, the study explores the heterogeneity and potential mechanisms of education inequality on rural energy poverty. Findings reveal a decline in rural energy poverty from 2005 to 2020, and the nature of energy poverty has substantially transformed, with a greater prominence of using unclean and unaffordable energy sources. Education inequality increases rural energy poverty and indirectly affects it by reducing the income of rural residents. The influence of education inequality on rural energy poverty exhibits heterogeneity in both time and region. Policy recommendations are proposed for mitigating energy poverty and promoting education equality in rural areas.

Некоторые аспекты цифрового неравенства в сфере образования

the paper examines the problems of digital inequality in the sphere of inequality, focusing on the analysis of its essence and main characteristics. The article examines such an indicator of digital inequality as unequal access of educational institutions to IT resources, which results in digital barriers. It is stressed that digital inequality in education leads to the deepening of social inequality, strengthening the processes of marginalisation and social deprivation, as well as to the production of social risks.

Ethical Considerations and Fundamental Principles of Large Language Models in Medical Education: Viewpoint.

This viewpoint article first explores the ethical challenges associated with the future application of large language models (LLMs) in the context of medical education. These challenges include not only ethical concerns related to the development of LLMs, such as artificial intelligence (AI) hallucinations, information bias, privacy and data risks, and deficiencies in terms of transparency and interpretability but also issues concerning the application of LLMs, including deficiencies in emotional intelligence, educational inequities, problems with academic integrity, and questions of responsibility and copyright ownership. This paper then analyzes existing AI-related legal and ethical frameworks and highlights their limitations with regard to the application of LLMs in the context of medical education. To ensure that LLMs are integrated in a responsible and safe manner, the authors recommend the development of a unified ethical framework that is specifically tailored for LLMs in this field. This framework should be based on 8 fundamental principles: quality control and supervision mechanisms; privacy and data protection; transparency and interpretability; fairness and equal treatment; academic integrity and moral norms; accountability and traceability; protection and respect for intellectual property; and the promotion of educational research and innovation. The authors further discuss specific measures that can be taken to implement these principles, thereby laying a solid foundation for the development of a comprehensive and actionable ethical framework. Such a unified ethical framework based on these 8 fundamental principles can provide clear guidance and support for the application of LLMs in the context of medical education. This approach can help establish a balance between technological advancement and ethical safeguards, thereby ensuring that medical education can progress without compromising the principles of fairness, justice, or patient safety and establishing a more equitable, safer, and more efficient environment for medical education.

Poverty Mafia? Contributions of Government-Nonprofit Collaborations to Reduce Educational Inequality

Poverty Mafia? Contributions of Government-Nonprofit Collaborations to Reduce Educational Inequality

The Influence of Education Level and Inequality on Poverty in North Sumatera

This study aims to determine the influence of education level and inequality on poverty in North Sumatra. This research uses annual time series data with a quantitative approach. The results show that partially there is no influence between education level and inequality on poverty in North Sumatra. Simultaneously, there is also no influence between education level and inequality on poverty in North Sumatra. The magnitude of the influence of education level and inequality on poverty in North Sumatra is evident from the coefficient of determination (r square) which is 46%, indicating that education level and poverty significantly affect poverty in North Sumatra.

Nurturing Scientific Literacy under Sustainable Development Goals in the Teaching of Science

To cope with the global challenges, United Nations came up with 17 sustainable development goals (SDG for short) to eradicate the extreme poverty, education inequality, climate change and other issues concerning to mankind living condition. SDG 4 aims to provide the access to quality education for everyone around the world. Since education plays a crucial role in realizing SDGs, science education is conducive to equip students with scientific literacy to explore the sustainability considering its nature of discipline. This research was conducted in a Chinese senior high school among science teachers, including biology, physics, chemistry, and management staff for a more comprehensive data needed. Based on findings, science education was poorly implemented on scientific literacy-oriented since all disciplines in class are for exam purpose not for overall development of students. Therefore, this research is trying to contribute for well-performed science education teaching mechanism in China in context of sustainability in a long run.

Inequality of educational opportunity among primary school children in Cameroon

Despite several education policies related to the Sustainable Development Goals, educational inequalities persist at the primary school level in Cameroon. However, little is known on students’ circumstances and efforts in an attempt to reduce these inequalities. The aim of this paper therefore is to analyse inequality of opportunity in Cameroon’s primary education sector. Using the 2014 and 2019 PASEC databases, we decompose students’ reading and mathematics test scores based on Shapley and non-parametric approaches to assess the role of household, school and environmental circumstances. Results show that inequality of opportunity contributes between 38.1 % and 46.3 % of total inequality for between 2014 and 2019 in test scores. Interestingly, school factors like school type, location, resources, and class size, have a bigger impact than students’ family background. Policies to improve school conditions should focus on rural and under-resourced areas in terms of educational materials, while improving school inspections and reducing class sizes may be more effective in reducing educational inequalities.

THE IMPACT OF SOCIO-ECONOMIC FACTORS AND INEQUALITIES ON LABOUR MARKET OPPORTUNITIES

In both developing and developed countries of the world, including Hungary, there are significant inequalities in both health status and the spatial distribution of the health system. Regional inequalities are a major challenge, as areas and regions that are disadvantaged are less able to develop and the people living there have fewer opportunities for economic advancement and better living conditions. Widely discussed in the literature that socio-economic status has a significant impact on the health status of a society. Economic and territorial disparities can be found within a municipality, a county, a region, a country, or even across Europe. In our study, we examine territorial and social inequalities in Hungary, with a special focus on economic, educational and health inequalities. We wanted to answer the question of how these factors might affect the lifestyle and labour market opportunities of the population. In this study, we use data from various sources and databases to illustrate the socio-economic differences and their effects in different regions. Research shows that the demographic situation, together with the ageing of societies, has a significant impact on countries' social security systems, pension systems and health care. Projections in the more advanced EU Member States show an increase in the proportion of people aged eighty-five and over by 2020. Higher educational attainment enhances an individual's social skills, abilities and capabilities and promotes a healthier lifestyle by managing risks more consciously. Various surveys show that only 50% of high school graduates rate their health as good or particularly good, while for those with tertiary education the figure is close to 75%. There is a direct correlation between income and health status. In poor countries in particular, there is a clear link between income levels and mortality rates. Higher income is often associated with better health. In most EU countries, poor people are much more likely to have poor or extremely poor health.

Rural Teachers' Digital Competence: Impediments and Developmental Responses

Digital transformation of education is an inevitable trend in the development of education in the digital era. Teachers' digital competence is the comprehensive ability of teachers to use digital technology to empower the teaching and learning process, which plays an important role in promoting the digital transformation of education, advancing educational equity, and cultivating students' digital competence. Under the dual system of urban and rural areas, there is a gap in digital competence between urban and rural teachers due to factors such as ambiguous cognition of rural teachers' digital technology, insufficient support, and lack of assessment and training systems, which may further widen educational inequality between urban and rural areas. The development of rural teachers' digital competence can be promoted by guiding them to change their way of thinking, increasing support, and improving assessment and training systems.

Market or State? Post-Reform Distribution of Sociology Faculty at Chinese Universities

Neoliberal policies since 1978 have caused severe regional inequality in China. Cities in regions with high levels of marketisation attract top academic experts due to their proximity to economic incentives and prestigious universities. However, little is known about how economic reform has shaped the distribution of experts in the social science and humanities disciplines as they are less in demand on the market and often rely on support from the state. Using sociology as a unique case, this study investigates market and state influences on the regional distribution of sociology experts in Chinese universities based on city-level measurements. Sociology has a unique historical and paradigmatic connection with the state of China, which complicates any straightforward relationship between neoliberal policies and higher education inequality in sociology departments. Through a manual collection of 1,041 faculty profiles from 66 university websites, it was determined following the fractional logistic regression method that both the market and the state facilitate distribution. The city’s marketisation increases the internationalisation of faculty members in sociology departments, while the city’s position in the state’s administrative hierarchy maintains the sociology departments, regardless of the city’s marketisation. In general, the state still plays an important role in shaping academic expert distribution after 40 years of market reform in China.