With the continual development of artificial intelligence and smart computing in recent years, quantitative approaches have become increasingly popular as an efficient modeling tool as they do not necessitate complicated mathematical models. Many nations have taken steps, such as transitioning to online schooling, to decrease the harm caused by coronaviruses. Inspired by the demand for technology in early education, the present research uses a radial basis function (RBF) neural network (NN) modeling technique to predict preschool instructors’ technology usage in classes based on recognized determinant characteristics of technology acceptance. In this regard, this study utilized the RBFNN approach to predict preschool teachers’ technology acceptance behavior, based on the theory of planned behavior, which states that behavioral achievement, in our case the actual technology use in class, depends on motivation, intention and ability, and behavioral control. Thus, this research design is based on an adapted version of the technology acceptance model (TAM) with eight dimensions: D1. Perceived usefulness, D2. Perceived ease of use, D3. Perceived enjoyment, D4. Intention to use, D5. Actual use, D6. Compatibility, D7. Attitude, and D8. Self-efficacy. According to the TAM, actual usage is significantly predicted by the other seven dimensions used in this research. Instead of using the classical multiple linear regression statistical processing of data, we opted for a NN based on the RBF approach to predict the actual usage behavior. This study included 182 preschool teachers who were randomly chosen from a project-based national preschool teacher training program and who responded to our online questionnaire. After designing the RBF function with the actual usage as an output variable and the other seven dimensions as input variables, in the model summary, we obtained in the training sample a sum of squares error of 37.5 and a percent of incorrect predictions of 43.3%. In the testing sample, we obtained a sum of squares error of 14.88 and a percent of incorrect predictions of 37%. Thus, we can conclude that 63% of the classified data are correctly assigned to the models’ dependent variable, i.e., actual technology use, which is a significant rate of correct predictions in the testing sample. This high significant percentage of correct classification represents an important result, mainly because this is the first study to apply RBFNN’s prediction on psychological data, opening up a new interdisciplinary field of research.

Degradation of mangrove ecosystems is a major concern in many countries of the world. This study assessed the level of knowledge, attitudes, and participation in mangrove conservation among two selected coastal communities of Gonzaga, Cagayan, Philippines. The study used a descriptive survey research design to a total of eighty participants selected through site selection approach. Results of the study revealed that the respondents from both coastal communities have a “high extent” level of knowledge, attitude and participation in mangrove benefits, conservation, and rehabilitation. Consequently, it is evidential that participation of both groups of respondents to community training and meetings was generally in a low -level extent implying necessary intervention. Test of difference showed that male respondents have the higher extent of participation over the women counterpart. Meanwhile, the middle age group had the highest level of participation in mangrove conservation. Also, the respondents who have long years of residence and living in proximity from the mangrove conservation areas have the higher level of their awareness and perception on sustainable mangrove program. This generally implies that gender, age, years of residence, and proximity of mangrove areas play important roles in public perception towards sustainable mangrove conservation programs in Gonzaga, Cagayan, Philippines. Results of this study will serve as the basis for implementing more effective mangrove conservation program.