The education sector is constantly progressing its competency paradigm by establishing a nexus between practical, theoretical, and technical dimensions of teaching and learning. In the modern age of education, hackathons are becoming increasingly prominent in providing an optimal academic environment that connects classroom learnings to real‐life scenarios. This study explored the motivational orientation behind student participation in hackathons through the framework provided by self‐determination theory. Specifically, it investigated the role of intrinsic and extrinsic motivations in encouraging initial and continuous hackathon participation. The partial least squares‐structural equation modeling method was used to analyze data collected from 437 students in 12 countries. According to the findings, although intrinsic motivation influences participation intention, extrinsic motivation drives continuance participation. When intrinsic and extrinsic motivational constructs were analyzed individually, it was found that continuance participation demands both motivational orientations. Comparisons of demographic characteristics indicate that older students with more extensive educational experience may have higher intentions to participate and continue participating in these events. This study offers insights into how the education sector can increase hackathon participation by tapping on students’ motivational orientation. From a methodological point of view, it is apparent to recommend the promotion of hackathons as a core extracurricular activity at a school level, and more indispensably, as pedagogy at a classroom level. In a world where students are encouraged to fail early, fast, and often, participating in hackathons is a tactical preparation for eventual success.

Due to the advent of the COVID-19 pandemic, the Philippine government encouraged enterprises and businesses to utilize flexible work arrangements such as work-from-home (WFH) or telecommuting setup. Nowadays, the key components necessary for a telecommuting include a WiFi-enabled IT equipment, secured work environment, and reliable internet connection, while research shows that type of work and computer literacy are also key factors for telework implementation. Multiple studies in relation to telework have already been conducted but some studies were deemed inconclusive and need further analysis. Therefore, in this study, a Mamdani fuzzy-based model was developed for telework capability assessment for Philippine government employees based on four significant factors namely: internet speed, IT equipment availability, computer literacy, and type of work, which are expressed in linguistic representations. The proposed fuzzy system can provide a feedback telework capability score based on the four input parameters which may also be characterized with the potential telecommuting cost requirement.

Automated chess is an emerging challenge from the field of robotics to human interaction. Customarily, chess playing robots use video camera for detecting the state of the board and robotic arm to manipulate the pieces, which makes it either expensive or too fragile to move. In this study, an automated chess board called “Chessbot” was built by combining a multitude of technologies and techniques such as voice command recognition, x and y plotting and recognition of chess pieces in a Cartesian plane, artificial intelligence algorithm, and Android mobile application. To properly execute chess moves either from a human player or computer via voice commands or a mobile game interface, the microcontroller provides motors the current and landing squares of chess pieces on the board. For the evaluation, several matches were simulated to perform various testing procedures such as voice command recognition, move log accuracy, display and user input acceptance accuracy, self-arrangement, and chess move reliability. Upon testing, Chessbot performs all the necessary tasks efficiently and accurately, which indicates the possibility of using this automated device for chess games at a hobbylevel or professional matches.

The firearms manufacturing industry has investigated replacing firearms frames with lightweight materials, such as polymer, in recent years. This kind of innovation in the manufacturing industry can help the users be more comfortable and reduce possible injuries. Using additive manufacturing or 3D printing, prototyping the firearms' frame made of polymer is possible. Additive manufacturing uses a 2-dimensional layer fabrication approach to form almost any 3-dimensional object. However, there are no studies published yet regarding the performance of the firearms frames made of polymer. In addition, supporting studies regarding the capability to withstand the pressure and stress from the blowback and recoil process should also be done. To solve the gap, this study investigates the stress and deformation of a 3D-printed pistol frame made of polymer using Finite Element Analysis (FEA). A comparison between steel and polymer frame materials is also analyzed to show its performance. The analysis results revealed that changing the frame's material to polymer will decrease the weight by 51.73%. The resulting stress is slightly comparable for both materials but with an increase in the displacement due to recoil. The study is expected to provide valuable theoretical references for the design. Employing better structure, other curves, and increasing the thickness are recommended to remove the high concentration of stress on some model areas.

Hearing is one of the most basic human senses. Research shows that 97 percent of the total population of elderly people suffer from hearing loss. This project is designed to address problems that the existing hearing aid users face throughout their use of the product which includes prices, high maintenance, sensitivity to noise, limited functional range and low-quality sound produced. This project aims to provide a hearing aid with noise filtration using Noise Reduction (NR) algorithm. NR algorithm which uses Multi-channel Wiener Filter is implemented to reduce noise and improve speech intelligibility. The proponents create a program for the Intel compute stick using NR algorithm to function as the filter and for the Raspberry Pi as the user interface of the device. The signal gathered by the microphone was processed by the Intel compute stick then outputted through the earphone audio output. The system is tested through simulation in terms of filtering the noise.

Decision-making in choosing a degree in engineering, especially for women, involves trade-offs between numerous conflicting objectives. To increase the number of female students enrolling in the Mechanical Engineering Department of Far Eastern University, a survey was conducted among the 30 female engineering students of the Mechanical Engineering Department of Far Eastern University, Institute of Technology. In this study, the objective is to determine the factor that affects female students in choosing engineering field. A multi-criteria decision analytical process was done using a pairwise comparison matrix wherein the goal is to rank the n alternatives. The results of the study revealed that 39.48% of their decision is based on their own choice. It is followed by the influence of the economy with 19%, while the influence of family, available opportunity and the impact of the latest trend have close relative weights, with value within 10% to 12%. Lastly, peer pressure had the least influence with relative weights of 7%. Since personal choice is based on individual's skills, it is recommended that universities should invest in various activities and workshops during their promotional campaign. This will help incoming college students to familiarized and be confident to include engineering in their degree of choice.

Engineering students face challenges of on-time successful degree completion. Universities incorporate academic advising as a solution to these challenges. Decision support systems enhance the effectivity of academic advising. Combined with machine learning, it can predict future student performance providing useful information. Compared to common ‘black box’ models, linguistic rules provide better interpretation and insight discovery. However, existing models often use positive predictors of academic excellence, with limited consideration on factors of negative effect. This work, therefore, generates linguistic rules for academic advising based on three predictors using rough set theory (RST) and then compared with artificial neural network (ANN) for benchmarking. Forty-eight samples of mechanical engineering students taking up machine design were considered. RST attained accuracy of 72.92% while ANN attained 66.66%. The model generated 13 linguistic rules, having reflected unrealized insights. The findings from this study may be utilized by academic advisers for pattern recognition, in identifying ‘at-risk’ students.

Interest in FRP composite bars as reinforcement to concrete has increased over the years as it showed solutions to the drawbacks of steel such as its corrosion issues and vulnerability when employed in adverse environmental conditions. However, it is still not widely incorporated as a replacement to conventional steel primarily due to the complexity of its bond strength mechanism. This, therefore, imposes the need to establish a comprehensive relationship for the bond property of the FRP reinforced concrete. This paper developed a novel Artificial Neural Network (ANN) bond strength prediction model for FRP reinforced concrete using 184 hinged beam database from various existing experiments. From series of simulations performed, the model N 7-10-1 with ten nodes in the hidden layer appeared to be the best fit with the experimental results yielded the most favorable performance among other existing models. From the parametric analysis conducted, the compressive strength of the FRP reinforced concrete has proved to be the most dominant parameter in evaluating its bond behavior as determined by relative importance of 17.82%. Overall, the proposed ANN model has demonstrated the best prediction for FRP bond strength in comparison to previous studies and code equations.

Higher education institutions worldwide were compelled to deliver their courses online due to mobility restrictions and lockdowns during the COVID-19 pandemic. This sudden shift has disrupted the educational system leaving millions unprepared for the new mode of instruction. One critical area that received little attention during this transition is student assessment. Many assessment methods designed for face-to-face classes have been adapted for online learning without much consideration. The conversion to emergency remote education has likewise exacerbated existing and uncovered new socioeconomic issues that demand immediate action. A scoping review has been carried out to map the concepts and develop a socioeconomic inclusive assessment framework for online learning in higher education. This framework will serve as a guide in designing assessment tasks that are more socioeconomically inclusive, making online learning more equitable. This chapter offers practical implications for developing a more inclusive assessment design that is beneficial to a broader group of students.

The unwelcomed arrival of the COVID-19 pandemic has exacerbated inequities and inequalities in accessing educational opportunities among different social groups. Abruptly, the idea of inclusivity in education has become more of an abstract phenomenon that widens the digital divide and creates social injustice. The resulting intensification of digital disparities demands an immediate coordinated response from all education and government stakeholders to guarantee that no one is left behind as we navigate the so-called new normal. Without an appropriate intervention and sound policy guidance, negative repercussions may be so widespread that they will remain a problem in the education sector far into the future. Socioeconomic Inclusion During an Era of Online Education aims to answer emerging questions on inclusive online education by exploring and collating the experiences and lessons learned during the implementation of emergency remote education. With the earlier-than-expected arrival of the online education era, best practices and innovative approaches from various educational institutions are concrete paradigms for safeguarding the promise of an undivided future of learning through equal access to quality education from a distance. Covering topics from learning space to education governance, this reference work is ideal for policymakers, administrators, practitioners, researchers, scholars, instructors, and students seeking to adjust and adapt to technology-enabled education during and after the COVID-19 era.