The K to 12 program has been implemented in the Philippines by the Department of Education which implicated an additional two years in the students’ basic education. These ancillary years allow senior high school students to take courses under the core curriculum and the track of choice. Each student must select one track to pursue that can equip him/her with skills to prepare for the future. Prediction of choice of career track in senior high school is advantageous for educational institutions since it gives insights that can help them develop vital programs beneficial for students’ learning in school. In this study, we applied artificial neural network (ANN) to predict the career strand based on the students’ grades in five major subjects. Different ANN models have been considered and compared. In training and testing the models, a sample of 293 student data information was used. The highest accuracy recorded among all the models was 74.1 %.
Complex networks have been used to characterize real world systems. The network structure may signify important relationships which may not be evident in other methods of analysis. In this study, we characterize the floral diversity in three study sites in the agroforestry zone of Mount Makiling Forest Reserve using network analysis. Plant species found in each study site are considered as nodes (N). Edges (E) are established to connect species with the same alternate role and habit. The dataset includes N = 157 and E = 4279 for Bagong Silang site, N = 145 and E = 3740 for the Karay site, and N = 122 and E = 2429 for the Magnetic Hill site. Network parameters such as degree, path length, clustering coefficient, modularity and number of connected components were calculated. Obtained values were compared to published diversity index. Results show that lower clustering coefficient and higher average path length signify higher diversity. A higher number of disconnected components also indicates diversity.
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