Contextualizing Types of Filipino Collective Support during the #COVID19 Lockdown

The lockdown as a countermeasure at the onset of the COVID-19 pandemic gained diverse responses globally. Many turned to Social Media platforms such as Twitter to express their sentiments on health crisis-related concerns. This study magnified the collective support-related Twitter content posted by users within the Philippines at the beginning of the pandemic. Collective Support expressions were collected using the Twitter Python Library and examined using content analysis. The primary goal is to elicit insights to understand the Filipinos' social/collective behaviors and how they were manifested at the onset of the COVID-19 lockdown. Hofstede's and Triandis' Theory of Collectivism primarily guided the direction of the study towards the affirmation of the Philippines as a collectivistic nation as demonstrated in the Collective Support Tweets classified under the following identified themes: (1) Language of Appreciation, Tribute, Support, covering the most significant percentage with 38.96% of the collective support tweets;(2) Friendly Reminders with 28.91%;(3) Acts of Community Service comprising 20.31%;and (4) Encouraging Words forming 11.82%. Given the Filipino's traditional familial and community-oriented culture, their collectivistic behavior shall naturally be conveyed irrespective of location, technology, and other relevant settings. However, considering the Twitter dataset under study, the technology shaped cultural implications based on the shared Twitter content in the Philippines. Further, it has affirmed the Philippines' collectivistic culture in accordance with the indicators under Hofstede's and Triandis' Theory of Collectivism.

Prescriptive Faculty Performance Analysis: A Case at the Onset of Covid-19 Pandemic

The challenges of internationalization, the diversion to outcome-based education, and the emergence of the COVID-19 pandemic triggered a growing demand for quality educators. Hence, educational institutions shall ensure continuous evaluation of faculty performance and use its data as a tool to capacitate learning providers and enhance instruction in the classroom. Using the identified performance indicators, this study aims to elicit insights from the dataset extracted from the Faculty Performance Evaluation System (FPES) of the Camarines Sur Polytechnic Colleges (CSPC) to understand how the students perceived their respective instructors' performance levels prior to and at the onset of the COVID-19 pandemic. Generated patterns were uncovered using descriptive analysis based on the students' ratings. Meanwhile, the students' comments, suggestions, and recommendations were analyzed using Sentiment Analysis through TextBlob. The same dataset was further examined to recommend a prescribed action using a supervised learning method (Decision Tree Algorithm). With 98% model accuracy, faculty performance testing dataset were provided with prescribed actions with the following rules: Outstanding & Very Satisfactory Ratings = Re-Hire/No Action Needed;Satisfactory = Mentorship;Unsatisfactory & Poor = Re-Training & Re-Evaluation. The study discovered a decline in the faculty performance evaluation results at the onset of the COVID-19 pandemic. However, the students' sentiments were considerate to the faculty's endeavor as most of its polarity scores fell under "positive." Recommendations to strengthen and boost faculty performance were incorporated based on the findings of the prescriptive analysis.