This study determined the issues and challenges encountered by the parents who facilitated learning at home. This research was a Qualitative-Phenomenological study that utilized the Narrative Form using the responses of the parent-participants in the Key Informant Interview conducted by the researcher. Creswell Method applied in qualitative analysis of these reactions to explore the lived experience of parents who served as Learning Facilitators in Modular Distance Learning. From the responses of the participants in the Key Informant Interview conducted, the researcher was able to extract the following themes that describe their overall ability in painting the portrait of their children: FB: Keeping You Informed, Education Must Continue, Education Cannot Wait, MDL Finds a Way, Painting a Portrait as Consecrated Responsibility, Race Against Time, and Time Works Wonders.           The researcher was able to draw out issues with implications of the findings on the lived experiences of the parents in painting the portrait of their children as not just a simple task. Their ability to paint the picture and helping their children with their studies were perceived as challenging but added colors in making their future even more meaningful.   Keywords: Paint a Portrait, Modular Distance Learning, Learning Facilitator

<span lang="EN-US">The Philippines is one of the countries in the world who suffers in different disasters, particularly natural disasters. Every year, there are more than twenty incidents recorded in the country related to different disasters which involve numerous lives of its citizens. It is found that most Filipinos have lack of knowledge in terms of disaster preparation specially, teenagers. This paper intended to develop a mobile-based game that aims to spread awareness on what to do during disasters. Upon development, forty-five (45) respondents were chosen to test the reliability of the application which composed of elementary students, household owners, police officers, fire fighters and IT experts. Further, ISO 25010 was adapted and modified in assessing the project. The results showed that the application is strongly acceptable and gives appropriate output in terms of disaster preparation garnering a total mean of 3.83<br /></span>

This descriptive research aimed to determine the level of perception of faculty of Laguna State Polytechnic University on the 21st century characteristics of educator.  Demographic profile of the respondent includes age, sex, status, educational attainment, academic rank, and years of service. The study identified six characteristics of 21st century educators such as adaptor, lifelong learner, technology savvy, collaborator, visionary, and leader. It was found out that the highest computed mean and standard deviation of 5.59 (0.13) was obtained from collaborator as one of the characteristics of educator and was interpreted as strongly agree. The faculty have developed and enhanced their knowledge and skills in teaching in order to adopt with the demands of 21st century learners. It is suggested that faculty members should continue to enhance their 21st century skills such as developing and applying new pedagogies in teaching and learning; and implementing design thinking and system thinking for educators.

The project aims to develop an intelligent system for simulating pisciculture in Taal Lake in the Philippines through geographical information system and deep learning algorithm. Records of 2018-2020 from the database of Bureau of fisheries and aquatic resources IV-A-protected area management board (BFAR IVA-PAMB) was collected for model development. Deep learning algorithm model was developed and integrated to the system for time series analysis and simulation. Different technologies including tensorflow.js were used to successfully developed the intelligent system. It is found on this paper that recurrent neural network (RNN) is a good deep learning algorithm for predicting pisciculture in Taal lake. Further, it is also shown in the initial visualization of the system that barangay Sampaloc in Taal has highest rate of fish production in Taal while Tilapia nilotica sp. is the major product of the latter.

A fire incident is a devastating event that can be avoided with enough knowledge on how and when it may occur. For the past years, fire incidents have become a big problem for the Philippines, since it affects the socio-economic growth of the country. Machine learning algorithm is a well-known technique to predict and analyze data. It can also be used to recognize pattern and develop models for artificial intelligence. Pattern recognition through machine learning algorithm is already established and have proven itself accurate in different fields such as education, crime, health and many others including fire incidents. This paper aims to develop a model for recognizing patterns of fire incidents in the province of Laguna, Philippines implementing a machine learning algorithm. With the foregoing project, it is found out that a recurrent neural network shows an astonishing result in terms of pattern recognition. Further, it is also found that Calamba City is the most vulnerable area in case of fire occurrence in the Province of Laguna.