An in-depth analysis of female students enrollment dynamics in Tanzanian mathematics Master’s programs using mixed-methods
| dc.contributor.author | Karunde, Safina | |
| dc.contributor.author | Mduma, Neema | |
| dc.contributor.author | Masanja, Verdiana | |
| dc.date.accessioned | 2025-10-08T05:51:16Z | |
| dc.date.issued | 2025-09-09 | |
| dc.description | SDGs-5 : Gender Equality | |
| dc.description.abstract | The under representation of female students in mathematics Master’s programs is a global challenge, including in Tanzania. Despite recognition, gaps persist in understanding historical enrollment patterns and contextual factors influencing female enrollment in Tanzanian mathematics Master’s programs. This research addresses these gaps through a mixed-methods approach. Quantitative analysis using an Auto regressive Integrated Moving Average (ARIMA) model for enrollment forecasting reveals nuanced dynamics in female enrollment, enhancing precision in predictions. Specifically, the ARIMA(0,1,1) model predicts the constant change of future values beyond the available data, while the prediction interval indicates enrollment fluctuations, gradually decreasing within the confidence intervals, and considering the simulated features. Qualitative document analysis provides contextual insights into socioeconomic and policy-related factors, such as cultural attitudes, societal norms, educational policies, availability of female role models, level of support, and gender biases. A Support Vector Machine (SVM) model comprehensively analyzes the impact of these (contextual) factors on the probability of female enrollment. Essentially, the prioritization of efforts should commence with addressing educational policies, promoting female role models, increasing support mechanisms, challenging cultural attitudes and societal norms, and addressing gender biases with nuanced strategies. Eventually, this study offers a cohesive under- standing of female enrollment dynamics, providing a foundation for targeted interventions and policies to foster gender diversity in Tanzanian mathematics Master’s programs. | |
| dc.identifier.citation | Karunde, A., Mduma, N., & Masanja, V. (2025). An in‑depth analysis of female students’ enrollment dynamics in Tanzanian mathematics master’s programs using mixed‑methods. Social Sciences & Humanities Open, 12, 101862. https://doi.org/10.1016/j.ssaho.2025.101862 | |
| dc.identifier.uri | https://doi.org/10.1016/j.ssaho.2025.101862 | |
| dc.identifier.uri | https://dspace.nm-aist.ac.tz/handle/123456789/3375 | |
| dc.language.iso | en | |
| dc.publisher | Elsevier Ltd | |
| dc.subject | Mixed-methods: | |
| dc.subject | Machine learning: | |
| dc.subject | Enrollment rate: | |
| dc.subject | Female students: | |
| dc.subject | Mathematics | |
| dc.title | An in-depth analysis of female students enrollment dynamics in Tanzanian mathematics Master’s programs using mixed-methods | |
| dc.type | Article |