Pan African Journal of Media, Data, and Information Literacy | 06 August 2006
E-Learning Platforms for Gender-based Online Privacy Protection in Lagos, Nigeria: A Methodological Framework
I, h, e, d, i, o, h, a, A, d, e, d, e, j, i, ,, O, b, i, K, i, n, g, s, l, e, y, ,, O, s, i, b, a, n, j, o, A, w, o, s, i, k, a, ,, E, d, e, G, e, o, r, g, e
Abstract
E-Learning platforms are increasingly used in universities to facilitate learning. However, there is a growing concern about gender-based online privacy issues among university students, particularly in Lagos, Nigeria, where computer science education is prevalent. A mixed-methods approach was employed, combining quantitative surveys with qualitative interviews. A convenience sample of 150 university students from four public universities in Lagos participated in a survey assessing their knowledge, attitudes, and usage patterns regarding online privacy on E-Learning platforms. Interviews were conducted to explore perceptions of privacy features and user engagement. The survey revealed that 68% of participants had experienced privacy concerns related to gender-based issues such as cyberstalking or harassment while using E-Learning platforms. The most commonly used privacy feature was the ability to report suspicious activity, with a reported usage rate of 75%. Interviews highlighted the importance of user-friendly interfaces and clear instructions for privacy settings. The methodological framework successfully identified key areas for enhancing gender-based online privacy protection on E-Learning platforms. Recommendations were provided based on participant feedback and interviews to improve platform design and user experience. Implementing a comprehensive privacy policy, providing regular training sessions, and encouraging open communication channels regarding privacy issues are recommended for universities using E-Learning platforms in Lagos. E-learning, gender-based online privacy, university students, Lagos, Nigeria Model estimation used $\hat{\theta}=argmin<em>{\theta}\sum</em>i\ell(y<em>i,f</em>\theta(x<em>i))+\lambda\lVert\theta\rVert</em>2^2$, with performance evaluated using out-of-sample error.