The situation of students in Sub-Saharan Africa: a case study of St Augustine University of Tanzania

Müller, Bernadette; Haller, Max

doi:10.1080/09620214.2012.709426

Cited by 2 publications

(6 citation statements)

References 16 publications

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“…It must be noted that parental education seems to matter differently for boys and girls. In Tanzania, Müller and Haller (2012) find that 31% of fathers and 17% of mothers of male students have college or university education, but these figures jump to 56% and 41% for girls.…”

Section: Inequality In Higher Education: Low‐ and Middle‐income Countriesmentioning

confidence: 99%

“…Kara, 2018;Schofer & Meyer, 2005), and sometimes feature in publications dealing with inequalities. Müller and Haller (2012) report a lack of quality resources and infrastructure in Tanzania, while Letseka and Pitsoe (2014) mention dysfunctionality in South African public schooling, where rural and township schools are of lower quality, thus hampering the development of the "epistemological access" necessary for HE. Despite significant expansion, most LICs offer "elite access," as opposed to the "mass" or "universal" access offered in HICs (Ilie & Rose, 2016).…”

Section: Macro-level Developments and Structural Inequalitymentioning

confidence: 99%

“…Despite significant expansion, most LICs offer "elite access," as opposed to the "mass" or "universal" access offered in HICs (Ilie & Rose, 2016). 3 Related to this is regional inequality, where communities are underserved due to limited HE infrastructure at the local level (but not necessarily at the national, Müller & Haller, 2012;Nawaz & Kundi, 2010;Ntshoe, 2003;Peet et al, 2015;Sánchez & Singh, 2018;Witenstein & Palmer, 2013;Bertolin & Leite, 2008). These disadvantages extend beyond the presence of HE institutions: for example, rural youth experience difficulties in accessing HE (Sánchez & Singh, 2018) due to lower parental education and wealth (Peet et al, 2015), and differentiated perceptions of and attitudes towards educational technologies used for online learning (Nawaz & Kundi, 2010).…”

Section: Macro-level Developments and Structural Inequalitymentioning

confidence: 99%

“…Many students need to work long hours alongside their studies, which limits their focus on pursuing HE (Letseka & Pitsoe, 2014). For instance, Müller and Haller (2012) find that in Tanzania (St. Augustine University, a mid-ranking private university), 90% of active students report financial problems, and 30% have problems with paying basic living expenses. As in other contexts, difficulty paying for tuition often leads to exclusion from programmes and examinations.…”

Section: Financial Resourcesmentioning

confidence: 99%

“…These stereotypes influence women beyond access, through factors such as perceptions in labour markets, which affect the rates of return for women, or the relevance of "hidden" curricula-the "hidden" referring to the (unintentional) transmission of gendered attitudes through learning and social behaviour. Müller and Haller (2012) cite research demonstrating that women are disadvantaged in terms of performance because they typically combine their studies with more domestic responsibilities than men. In terms of career outcomes, Nilsson (2019) finds that women experience longer school-to-work transitions and are paid less when finding a job.…”

Section: Sociocultural Resourcesmentioning

confidence: 99%

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Inequalities in higher education in low‐ and middle‐income countries: A scoping review of the literature

Reinders

Dekker

Falisse

2021

Development Policy Review

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Motivation Higher education is regarded as a key instrument to enhance socioeconomic mobility and reduce inequalities. Recent literature reviews have examined inequalities in the higher education systems of high‐income countries, but less is known about the situation in low‐ and middle‐income countries, where higher education is expanding fast. Purpose The article reviews the academic literature on higher education in low‐ and middle‐income countries using a research framework inspired by social justice and capability approaches. It considers the financial, sociocultural, human and political resource domains on which people draw, and how they relate to access, participation and outcomes in higher education. Methods A literature search for studies explicitly discussing in‐country inequalities in higher education revealed 22 publications. Substantial knowledge gaps remain, especially regarding the political (and decision‐making) side of inequalities; the ideologies and philosophies underpinning higher education systems; and the linkages between resource domains, both micro and macro. Findings The review highlights key elements for policy‐makers and researchers: (1) the financial lens alone is insufficient to understand and tackle inequalities, since these are also shaped by human and other non‐financial factors; (2) sociocultural constructs are central in explaining unequal outcomes; and (3) inequalities develop throughout one’s life and need to be considered during higher education, but also before and after. The scope of inequalities is wide, and the literature offers a few ideas for short‐term fixes, such as part‐time and online education. Policy implications Inclusive policy frameworks for higher education should include explicit goals related to (in)equality, which are best measured in terms of the extent to which certain actions or choices are feasible for all. Policies in these frameworks, we argue, should go beyond providing financial support, and also address sociocultural and human resource constraints and challenges in retention, performance and labour market outcomes. Finally, they should consider relevant contextual determinants of inequalities.

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Section: Inequality In Higher Education: Low‐ and Middle‐income Countriesmentioning

confidence: 99%

Section: Macro-level Developments and Structural Inequalitymentioning

confidence: 99%

Section: Macro-level Developments and Structural Inequalitymentioning

confidence: 99%

Section: Financial Resourcesmentioning

confidence: 99%

Section: Sociocultural Resourcesmentioning

confidence: 99%

See 3 more Smart Citations

Inequalities in higher education in low‐ and middle‐income countries: A scoping review of the literature

Reinders

Dekker

Falisse

2021

Development Policy Review

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Predicting patterns of persistence at a South African university: a decision tree approach

Paideya

Bengesai

2021

IJEM

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PurposeThe emerging field of educational data mining provides an opportunity to process large-scale data emerging from higher education institutions (HEIs) into reliable knowledge. The purpose of this paper is to examine factors influencing persistence amongst students enrolled in a Chemistry major at a South African university using enrolment data.Design/methodology/approachThe sample consisted of 511 students registered for a Chemistry major beginning in 2012, 2013 and 2014. Descriptive statistics in counts and percentages and classification (decision) tree methods were used in the analysis.FindingsGraduation from the Chemistry major is likely to occur after 4 years, which is regulation time plus 1 year, whilst departure mainly occurs in the first year of study. Classification tree modelling demonstrated that first year accumulated credits (FYAC), gender, financial aid status and school quintile were the factors associated with persistence. FYAC was the most critical factor.Research limitations/implicationsAlthough this study has many strengths, significantly the use of data mining methods to classify students, some limitations might affect how the results are interpreted. First, the analysis focused on a one-degree major in one institution, which leads to the suspicion that the observed results are discipline or institution-specific. Thus, the findings cannot be generalised to other contexts or disciplines. Second, with so many potential factors influencing student persistence, the analysis presented in this paper, which was limited to the covariates obtained in the institutional dataset the authors used, is by no means exhaustive. There is the possibility that some factors, which are not included in the present analysis, might have more predictive power.Originality/valueGlobally, university administrators are interested in predicting student outcomes and understanding the intricate balance between enrolment and throughput. Thus, whilst the findings from this study have an institutional focus, they resonate with other HEIs and present an alternative and highly visual way of identifying specific combinations of factors associated with persistence. The results from a classification tree model can also classify students at risk and inform the development of interventions that will support them.

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The situation of students in Sub-Saharan Africa: a case study of St Augustine University of Tanzania

Cited by 2 publications

References 16 publications

Inequalities in higher education in low‐ and middle‐income countries: A scoping review of the literature

Inequalities in higher education in low‐ and middle‐income countries: A scoping review of the literature

Predicting patterns of persistence at a South African university: a decision tree approach

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