Computer programming education is often delivered using individual learning strategies leaving group learning techniques as an under-researched pedagogy. This pose a research gap since novice programmers tend to form their own group discussions after lecture meetings and laboratory activities, and often rely on peers when a topic or activity is difficult. Thus, this study intends to evaluate the impact of cooperative learning using jigsaw technique when teaching computer programming to novice programmers. A quasi-experimental research using a nonequivalent control group pretest-posttest design was adopted to examine the impact of jigsaw teaching strategy. After a 14-week programming course, pre- and post-test results revealed a significant increase in terms of attitude and self-efficacy, and the experimental group demonstrated significantly higher scores than in the control group. Therefore, it was concluded that cooperative learning using Jigsaw technique is a valid and effective teaching strategy when handling novice programmers in an introductory programming course.

Flood is imminent when heavy rain occurs, identifying the level of water in plain sight is difficult to achieve. There are currently available ways to detect flood water but usually are very expensive and needs a huge equipment with sensors. The research has proposed an alternative solution to expensive ways on detecting flood and water levels. The study created an application to detect body of water by using image processing technique called Region-based segmentation algorithm to detect water on the image and Canny Edge Detection with computation using Pixel Ratio on a selected water region to determine the height of the water or flood. A CCTV camera was used to capture the image and was fed on the application through the network infrastructure. Once captured, the image was processed to detect the body of water and measurement of its level. The testing of the application was done on a controlled environment and the application was able to detect the water body on the picture. It was able to detect the edge of the water based on a selected region where the water is found. The measurement of the actual height of the water, closely matches the height of stated in the application. Thus, the research has found a way to detect body of water and gauge its water level using image processing, in which, have found a way to detect and measure water affordably. This research can be a step, in future research like monitoring the streets’ flood level when heavy rains occurs. This is a much more safe and affordable way to monitoring the increase and decrease of flood.

Attaining high retention rates among engineering institutions is a predominant is-sue. A significant portion of engineering students face challenges of retention. Academic advising was implemented to resolve the issue. Decision support sys-tems were developed to support the endeavor. Machine learning have been inte-grated among such systems in predicting student performance accurately. Most works, however, rely on a black box model approach. Rule induction generates simpler if-then rules, exhibiting clearer understanding. As most research works considered attributes for positive academic performance, there is the need to con-sider ‘negative’ attributes. ‘Negative’ attributes are critical indicators to possibility of failure. This work applied rule induction techniques for course grade predic-tion using ‘negative’ attributes. The dataset is the academic performance of 48 mechanical engineering students taking a machine design course. Students’ at-tributes on workload, course repetition, and incurred absences are the predictors. This work implemented two rule induction techniques, rough set theory (RST) and adaptive neuro fuzzy inference system (FIS). Both models attained a classifi-cation accuracy of 70.83% with better performance for course grades of ‘Pass’ and ‘High’. RST generated 16 crisp rules while ANFIS generated 27 fuzzy rules, yielding significant insights. Results of this study can be used for comparative analysis of student traits between institutions. The illustrated framework can be used in formulating linguistic rules of other institutions.

This research exhibits the current state of the groundwater resources of the Province of Marinduque more than 20 years after the mining disaster. The sampling locations included thirty – five (35) sites that were extending all six municipalities of the province. The concentration of chromium, iron, manganese, lead, and zinc exceeded the maximum admissible limit (MAL) based on the Philippine National Standards for Drinking Water (PNSDW) 2017. Thirteen of the sampling sites were classified as severe pollution based on its pollution index. The highest pollution indices were found to be at Brgy. Sumangga, a riverside barangay in the Municipality of Mogpog. These indices were utilized to produce a spatial metal concentration map of the Province of Marinduque using the Empirical Bayesian Kriging (EBK) method. Based on the map, the groundwater of the municipality of Torrijos needs prompt attention for remediation. The findings revealed that the province of Marinduque's groundwater quality is in danger of deteriorating. It is possible to infer that EBK is an effective method for monitoring groundwater quality based on the data and correlation provided. The results of this study could assist in planning rapid response and strategies that are beneficial in the execution of programs that will enhance the adaptive capacity of the province.

Corrosion of steel reinforcement due to hostile environments is regarded as one vital structural health concerns in concrete structures. Specifically, the development of corrosion affects the necessary bond strength of rebar in concrete contributing to the loss of resilience and possible structural failures. It is thus essential to understand the effects of corrosion on bond strength so that remedial measures can be done on existing and deteriorating RC structures. Hence, this study investigated through laboratory experiments and Artificial Neural Network (ANN) modeling the effects of corrosion on bond strength. Experimental results showed that at small amounts of corrosion less than 0.27%, the bond strength was observed to increase. At these levels, the amounts of corrosion products were sufficient enough to expand freely through the permeable structure of concrete and occupy the pore spaces. Beyond this level, however, the bond strength of concrete deteriorated significantly. There was an observed average decrease of 1.391 MPa in the bond strength values for every percent increase in the amount of corrosion. The expansive and progressive internal radial stress due to corrosion resulted to the development of internal and surface cracks in concrete. In the parametric investigation of the derived ANN model, the bond strength was also observed to decline continuously with the growth of corrosion derivatives as represented by the relative magnitudes of the ultrasonic pulse velocity (UPV). The prediction results of the model can be utilized as basis for design and select appropriate mitigating measures to prolong the service life of concrete structures.

Ultra-High Strength Concrete (UHSC) is known for its brittleness compared to traditional concrete, which can lead to sudden collapses. When it comes to columns, failures are particularly serious and require the use of confinement models to accurately predict the strength and strain of confined UHSC columns. While previous confinement models exist, many equations either underestimate or overestimate the confinement of concrete due to idealized assumptions and the exclusion of significant variables. This study employs a hybrid machine learning approach to capture the complex interactions in confinement behavior and accommodate a broader range of peak strength and axial strain parameters in UHSC. Statistical performance measures indicate the superiority of the proposed models over existing equations. Through causal inference, the study assesses the effects and relative importance of each parameter on peak strength and axial strain. The visualizations provided by the performance plots helped identify patterns and correlations that would have been difficult to discern through numerical analysis alone. The developed NN-PSO models are proven effective in reasonably predicting the peak strength and axial strain of UHSC columns.

Data analysis is an integral part of research. Most researchers examine their results by using graphs, tables, charts, and figures. These methods are effective, but knowledge transfer is limited because it only depends on what the authors or researchers have presented. The need to scrutinise further the given data is essential. One way of addressing this problem is to utilise a graphical user interface (GUI), wherein a user can manually choose some parameters of an extensive dataset to display and analyse. In this paper, the results of the four variants of clustering techniques, namely the Ant Colony Optimization (ACO), Gaussian Mixture Model (GMM), K-Power Means (KPM), and Kernel-Power Density-Based Estimation (KPD), in grouping the wireless multipath propagations, are evaluated through the use of a GUI. The accuracy performance of each clustering algorithm can be obtained by choosing in the GUI the corresponding channel scenario that the user would like to investigate. A deeper analysis of the clustering characteristics can also be done by selecting other parameters in the GUI. This selection gives a better understanding of the behaviour of each clustering technique and provides an effective way of presenting and analysing the different sets of data.

The shift to a circular economy requires careful planning, the first step of which is to understand the drivers of the transition. There have been few papers in the literature that have analyzed and mapped interrelationships of these transition drivers from the perspective of different sectors. This work presents a methodological framework for mapping causality networks for macro-level transition towards circular economy based on sector perceptions. Fuzzy DEMATEL is used to allow linguistic inputs to be quantified. This procedure allows drivers to be characterized as causes or effects based on their position in the causality network. A case study presents the Philippines as a representative developing country for circular economy transition. The inputs of seventeen respondents from retail and trade, manufacturing, construction, water services, food services, electricity services, academic services, and health services were elicited through a survey. These responses were then aggregated into the industry and service sectors. The drivers considered were government support, company culture, consumer demand, social recognition, economic attractiveness, and information to practitioners. Results show that economic attractiveness and consumer demand are unanimously seen as the causal drivers. All sectors identify company culture as an effect driver. The findings also indicate varying perceptions among sectors. Although these findings apply specifically to the Philippines, this methodology itself can be used for mapping driver interrelationships of other countries and regions.

From the outbreak of a novel COronaVIrus Disease (COVID-19) in Wuhan to the first COVID-19 case in the Philippines, Filipinos have been enthusiastically engaging on Twitter to convey their sentiments. As such, this paper aims to identify the public opinion of Filipino twitter users concerning COVID-19 in three different timelines. Toward this goal, a total of 65,396 tweets related to COVID-19 were sent to data analysis using R Statistical Software. Results show that “mask”, “health”, “lockdown”, “outbreak”, “test”, “kit”, “university”, “alcohol”, and “suspension” were some of the most frequently occurring words in the tweets. The study further investigates Filipinos’ emotions regarding COVID-19 by calculating text polarity of the dataset. To date, this is the first paper to perform sentiment analysis on tweets pertaining to COVID-19 not only in the Filipino context but worldwide as well.

According to National Center for Education Statistics, almost half of the first-time freshmen full time students who began seeking a bachelor’s degree do not graduate. The imbalance between the student enrolment and student graduation can be solved by early predicting and identifying students who are prone of not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions. The study focused on the application of the ensemble models in predicting student graduation. Ensemble modeling is the process of running two or more related but different analytical models and then synthesizing the results into a single score or spread in order to improve the accuracy of predictive analytics and data mining applications. The study recorded an increase of classification accuracy in predicting student graduation using ensemble models and combining multiple algorithms.