The digital gender divide is a major challenge that needs to be addressed in developing countries. Thus, the focus of this study is to address the digital il-literacy of girls and women that also fuels the digital gender divide. The goal is to produce an e-learning module that focused on the skills to be measured in assessing ICT skill in Sustainable Development Goals (SDG) 4. This can be used during training as a tool to capacitate participants like marginalized women and girls. The development of this e-module follows the research and development using the 4D model process that begins in define phase, followed by the design of e-learning content and development activities, and lastly disseminate. The impact of the e-learning module was evaluated during ICT literacy training for marginalized women and girls. This study found that utilizing e-learning modules in the development of skills among participants was significant. This study was a humble step towards gaining technological skills of the marginalized girls and women in the Philippine community to-wards ICT4D.

Dengue is a viral mosquito-borne infection that is endemic and has become a major public health concern in the Philippines. Cases of dengue in the country have been recorded to be increasing, however, it is reported that the country lacks predictive system that could aid in the formulation of an effective approach to combat the rise of dengue cases. Various studies have reported that climatic factors can influence the transmission rate of dengue. Thus, this study aimed to predict the probability of dengue incidence in Iligan City per barangay based on the relationship of climatic factors and dengue cases using different predictive models with data from 2008 to 2017. Multiple Linear Regression, Poisson Regression, and Random Forest are integrated in a mini-system to automate the display of the prediction result. Results indicate that Random Forest works better with 73.0% accuracy result and 33.58% error percentage, with time period and mean temperature as predictive variables.

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