The University of Antique (UA), formerly known as Polytechnic State College of Antique, is a state university in the Philippines. It is mandated to provide higher technological, professional and vocational instruction and training in science, agricultural and industrial fields, as well as short-term technical or vocational courses. It is also mandated to promote research, advanced studies and progressive leadership in its areas of specialization. Its main campus is in Sibalom, Antique.UA was originally the Antique School of Arts & Trades (ASAT) which was established by virtue of Republic Act 857 sponsored by the late Congressman Tobias A. Fornier in 1954 with Mr. Fermin Taruc as its first Superintendent. Mr. Taruc was sent to Sibalom, Antique by the Bureau of Vocational Education to set up and construct the first buildings of ASAT..
Accurate short-term load forecasting (STLF) is essential for optimizing power dispatch and preventing equipment failures in localized electrical grids. This study developed and validated a reliable predictive model for the Antique substation using the Group Method of Data Handling (GMDH) machine learning algorithm. The model was trained and tested on 692 hours of historical electrical load data, split into 592 hours for training and 100 hours for out-of-sample testing. The self-organizing network used a linear transfer function and reached an optimal polynomial complexity of 72 with 48 selected variables. After thorough validation, the algorithm forecasted grid demand for a 100-hour unseen horizon (September 2 to September 6). Performance metrics show exceptional predictions and complete absence of data over fitting. The training phase yielded a Mean Absolute Percentage Error (MAPE) of 3.28%. Accuracy successfully improved during the blind testing phase, resulting in a MAPE of just 2.70%, a Root Mean Square Error (RMSE) of 81.46% kW, and a Mean Absolute Error (MAE) of 66.84 kW. For the future forecasting horizon, the model predicted a peak power demand 4,117 kW while establishing strict statistical confidence band of ±142,35 kW. Falling below the standard threshold for STLF, the GMDG algorithm proved to be a highly dependable tool. Integrating this predictive model into daily substation operations will allow engineers to proactively anticipate peak surges, schedule maintenance safety, preventing catastrophic overloads.
Mathematical proof is fundamental to mathematical reasoning and conceptual understanding, yet teaching proof remains challenging because learners must coordinate mathematical knowledge, logical reasoning, justification, and communication. This study investigated the challenges mathematics teachers encounter in addressing proof and the strategies they employ to respond to students’ misconceptions and learning difficulties. A mixed-method research design was used to integrate quantitative assessment of teachers’ perceived challenges with qualitative analysis of instructional strategies. The participants were 30 mathematics teachers in Region VI who were enrolled in graduate studies in mathematics or mathematics education. Data were collected through a researcher-developed questionnaire consisting of respondent profiles, 15 Likert-scale items on challenges in proof instruction, and open-ended questions concerning instructional strategies. Descriptive statistics, the Kruskal–Wallis H test, and the Mann–Whitney U test were used to analyze quantitative data, while thematic analysis was used for qualitative responses. The findings indicate a very high level of challenges across cognitive, affective, instructional, curricular, and structural dimensions, with the tendency of students to treat examples as proofs receiving the highest rating. Teachers report using multiple proof techniques, scaffolded instruction, guided questioning, collaborative learning, and engagement-oriented strategies. No significant differences are found across teacher profile variables. The findings support the need for progressive proof instruction, reasoning-focused professional development, improved instructional resources, and stronger curriculum alignment.
This descriptive study aimed at assessing the career aspirations among teacher education and maritime students at the University of Antique, Philippines. The computer-processed statistics were frequency, percentage, means, and standard deviation for descriptive analysis, the t-test, and One-Way ANOVA for inferential analysis. Alpha level was set at .05. The study found that students' career aspirations were generally moderate. The students had shared the five highest career aspirations: achieving in their career is very important to them, being one of the best in their filed is important to them, becoming a leader in their job is very important to them, attaining leadership status in their career is important to them, and they want their work to have a lasting impact on their field. On the other hand, they also shared the five lowest career aspirations: planning to rise to the top leadership position of their organization or business, managing other employees when they are established in their career, becoming a leader in their career field, knowing that they will be recognized for their accomplishments in their field, and hoping to move up to a leadership position in their organization or business. A significant difference existed in the career aspirations among teacher education and maritime students when classified by age. However, no significant difference existed in the career aspirations among teacher education and maritime students when classified by college, gender, place of birth, and location. Therefore, one important implication that should be considered is bridging the gap between different age groups of students in their career aspirations through workshops and interventions targeted at students in general. They are to be exposed to a wide range of resources that relate to careers.
Objective: To assess the use of creative strategies and the level of pedagogical enhancement among Filipino science teachers on the Island of Panay and to explore their relationship in promoting science literacy. Method: The research design is a descriptive survey using a researcher-made questionnaire validated by five experts. The questionnaire was divided into two sections: Section 1 focused on creative strategies, and Section 2 on pedagogical enhancement. The researchers used the mean, standard deviation, and Spearman's rho. Findings: The respondents’ profiles showed that 40 elementary teachers, 32 junior high school (JHS) teachers, and 28 senior high school teachers participated in this study. The creative strategies mean score ranged from 1.72 to 2.31 and was interpreted as sometimes. These creative strategies are role-playing, concept mapping, world cloud guessing, and classroom opinion. While pedagogical enhancement was rated very often, with mean scores from 2.23 to 2.89. When grouped by educational qualification, years of service, sex, and school type, creative was rated sometimes, with mean scores ranging from 1.95 to 2.11, while pedagogical enhancement was rated very often, with mean scores ranging from 2.47 to 2.58. There was no significant relationship between creative strategies and pedagogical enhancement (r = 0.123, p = 0.223) in terms of teachers’ awareness. This implies that both variables operate independently in classroom practice. Findings suggest a gap between teachers’ awareness and actual implementation of creative strategies despite strong pedagogical practices. Novelty: Scientific literacy largely depends on teachers’ ability to deliver instruction effectively. Educators serve as key agents of learning, responsible for ensuring that knowledge is conveyed in meaningful and efficient ways using creative strategies and innovative approaches. In this context, the study underscores the importance of providing targeted professional development, sustained support, and specialized training to enhance the integration of creativity in science instruction. Keywords: Basic Education, Creative Strategies, Pedagogical Enhancement, Science Literacy, Science Teacher
Rice production in the Philippines faces significant challenges in nitrogen management, traditionally reliant on subjective visual assessment using the Rice Leaf Color Chart (RLCC). This study developed an end-to-end mobile artificial intelligence system for automated, non-destructive nitrogen deficiency assessment. We collected and labeled 2,668 rice leaf images according to RLCC standards (Levels 2–5) and trained a Convolutional Neural Network (CNN) that achieved 98.75