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Student retention is an important issue faced by Philippine higher education institutions. It is a key concern that needs to be addressed for the reason that the knowledge they gain can contribute to the economic and community development of the country aside from financial stability and employability. University databases contain substantial information that can be queried for knowledge discovery that will aid the retention of students. This work aims to analyze factors associated with student’s success among first-year students through feature selection. This is a critical step prior to modelling in data mining, as a way to reduce computational process and improve prediction performance. In this work, filter methods are applied on datasets queried from university database. To demonstrate the applicability of this method as a pre-processing step prior to data modelling, predictive model is built using the selected dominant features. The accuracy result jumps to 92.09%. Also, through feature selection technique, it was revealed that post-admission variables are the dominant predictors. Recognizing these factors, the university could improve their intervention programs to help students retain and succeed. This only shows that doing feature selection is an important step that should be done prior to designing any predictive model.

Violence against women and girls (VAWG) is not a recent phenomenon. What is new is the additional increasing threats that millions of women and girls face because of the rapid spread of ICTs and the expansion of social media. Cases of VAWG wherein ICT and social media are used as platforms by cybercriminals can be seen in the news media coverage. This study aims to understand and determine the trend and the state of cyber VAWG to raise awareness through mining online news websites. News articles were scraped from popular news websites between 2015 to 2020. The preprocessed articles (N=3,506) were analyzed by year using the Topic Keyword Model (TKM). It was observed that the cyber VAWG articles topic trends are increasing with most of the articles focusing on the topics \"Online sexual exploitation and sexual abuse of children\" and \"ICT-related violations of privacy\". Text mining methods may address the limitations of traditional qualitative approaches. Understanding the cyber VAWG issues by mining news articles is a novel approach that could help create programs and policies to address this societal concern. Additional studies should be conducted related to sentiment analysis of news data to verify and measure the influence of cyber VAWG-related topics.

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.

Violence against women and girls (VAWG) is not a recent phenomenon. What is new is the additional increasing threats that millions of women and girls face because of the rapid spread of ICTs and the expansion of social media. Cases of VAWG wherein ICT and social media are used as platforms by cybercriminals can be seen in the news media coverage. This study aims to understand and determine the trend and the state of cyber VAWG to raise awareness through mining online news websites. News articles were scraped from popular news websites between 2015 to 2020. The preprocessed articles (N=3,506) were analyzed by year using the Topic Keyword Model (TKM). It was observed that the cyber VAWG articles topic trends are increasing with most of the articles focusing on the topics “Online sexual exploitation and sexual abuse of children” and “ICT-related violations of privacy”. Text mining methods may address the limitations of traditional qualitative approaches. Understanding the cyber VAWG issues by mining news articles is a novel approach that could help create programs and policies to address this societal concern. Additional studies should be conducted related to sentiment analysis of news data to verify and measure the influence of cyber VAWG-related topics. La violencia contra las mujeres y las niñas (VCMN) no es un fenómeno nuevo. Lo nuevo son los crecientes peligros a que se enfrentan los millones de mujeres y niñas debido a la difusión de las TIC y redes sociales. Los casos de la VCMN donde se utilizan como plataformas las TIC y las redes sociales se encuentran fácilmente por medio de la cobertura mediática. Este estudio tiene como objetivo la comprensión y la definición de la tendencia y el estado de la ciber-VCMN para crear conciencia por medio del análisis de los sitios web de noticias online. Entre 2015 y 2020, se recopilaron artículos de los principales sitios de noticias. Se utilizó el Modelo de palabras claves temáticas para evaluar los artículos preprocesados (N=3.506) por año. Se señaló que la mayoría de los artículos sobre ciber-VCMN se centraban en temas de «Explotación sexual y abuso sexual de niños en línea» y «Violaciones de la privacidad relacionadas con las TIC». El análisis de los textos ayuda a trascender las limitaciones de las metodologías cualitativas tradicionales. Comprender las preocupaciones de la ciber-VCMN mediante la extracción de artículos de noticias podría ayudar a crear iniciativas y políticas para solucionar este problema. Proponemos que se lleve a cabo una investigación utilizando análisis de sentimiento de los datos de noticias para verificar y cuantificar el impacto de los problemas relacionados con la ciber-VCMN.

Violence against women and girls (VAWG) is not a recent phenomenon. What is new is the additional increasing threats that millions of women and girls face because of the rapid spread of ICTs and the expansion of social media. Cases of VAWG wherein ICT and social media are used as platforms by cybercriminals can be seen in the news media coverage. This study aims to understand and determine the trend and the state of cyber VAWG to raise awareness through mining online news websites. News articles were scraped from popular news websites between 2015 to 2020. The preprocessed articles (N=3,506) were analyzed by year using the Topic Keyword Model (TKM). It was observed that the cyber VAWG articles topic trends are increasing with most of the articles focusing on the topics “Online sexual exploitation and sexual abuse of children” and “ICT-related violations of privacy”. Text mining methods may address the limitations of traditional qualitative approaches. Understanding the cyber VAWG issues by mining news articles is a novel approach that could help create programs and policies to address this societal concern. Additional studies should be conducted related to sentiment analysis of news data to verify and measure the influence of cyber VAWG-related topics. La violencia contra las mujeres y las niñas (VCMN) no es un fenómeno nuevo. Lo nuevo son los crecientes peligros a que se enfrentan los millones de mujeres y niñas debido a la difusión de las TIC y redes sociales. Los casos de la VCMN donde se utilizan como plataformas las TIC y las redes sociales se encuentran fácilmente por medio de la cobertura mediática. Este estudio tiene como objetivo la comprensión y la definición de la tendencia y el estado de la ciber-VCMN para crear conciencia por medio del análisis de los sitios web de noticias online. Entre 2015 y 2020, se recopilaron artículos de los principales sitios de noticias. Se utilizó el Modelo de palabras claves temáticas para evaluar los artículos preprocesados (N=3.506) por año. Se señaló que la mayoría de los artículos sobre ciber-VCMN se centraban en temas de «Explotación sexual y abuso sexual de niños en línea» y «Violaciones de la privacidad relacionadas con las TIC». El análisis de los textos ayuda a trascender las limitaciones de las metodologías cualitativas tradicionales. Comprender las preocupaciones de la ciber-VCMN mediante la extracción de artículos de noticias podría ayudar a crear iniciativas y políticas para solucionar este problema. Proponemos que se lleve a cabo una investigación utilizando análisis de sentimiento de los datos de noticias para verificar y cuantificar el impacto de los problemas relacionados con la ciber-VCMN.