Scopus ID: 85132195726
2022 10th International Conference on Information and Education Technology (ICIET), (2022)
Conference Paper | Published: January 1, 2022
Abstract
Recent studies have acknowledged social media as a valuable pedagogical tool for connecting the formal and informal learning gap. However, as a new platform, the literature is sparse on the potential of TikTok as a knowledge source. In this study, we explored programming TikTok videos in the #LearnProgramming webpage in terms of content (programming languages and topics) and characteristics (video styles and types). Although TikTok is principally an entertainment destination, our results show that the platform likewise has informative videos. The 349 videos that we examined received a total of 10,046,000 views, 10,523 comments, 932,871 likes, and 35,095 shares, implying extremely high levels of user engagement. Tiktokers showing tips and tricks are the most recurring content type. From a macro perspective, we noticed that TikTokers do not follow the ethos of the platform (e.g., dancing) when producing educational content. This deviation demonstrates the intent of TikTokers to educate than to entertain. Although it is too early to conclude that TikTok can operate as a nanolearning platform, we discovered a substantial amount of content for and engagement from programming learners. Our results lay a potent foundation for devising actionable scholastic implications, policies, and recommendations concerning TikTok consumption. Future works and research prospects were also discussed to propel the social media and nanolearning literature forward.
Scopus ID: 85144379561
International Journal of Technology Marketing, (2022), Vol. 17, No. 1, pp. 1
Journal Article | Published: January 1, 2022
Abstract
Location-based marketing (LBM) is becoming an integral element of the media mix for making highly personalised offers to the targeted audience at the most opportune time and place. Yet, the literature calls for more usability studies due to the lack of user-centred research. To fill this gap, this study explores the development of PushMapp - a geomarketing tool for launching LBM campaigns - through a user-centred, parallel-iterative approach. Usability analysis shows that this type of application is affected by issues related to security, privacy, advertisement relevancy, and notification overload. Meanwhile, only performance expectancy, effort expectancy, and hedonic motivation appeared to be the significant factors in an LBM mobile application. Experiences from this study provided valuable insights for marketers and business owners who plan to capitalise on LBM strategies by underscoring the importance of integrating users' input, ensuring usability compliance, and conforming to factors of mobile application utilisation.
Scopus ID: 85137332714
International Journal of Gaming and Computer-Mediated Simulations, (2022), Vol. 14, No. 1, pp. 1-18
Journal Article | Published: January 1, 2022
Abstract
Social relationships are a fundamental aspect of human existence. Unsurprisingly, policymakers are incessantly devising strategies that accentuate the benefits of social relationships and diminish the risks of social isolation. The natural manifestation of player-to-player interaction in a video game context poses a unique opportunity to study the effects of co-playing on social relationship formation. However, most studies recruited players with existing relationships (e.g., family and friendship), utilized random commercial video games, or experimented in an online environment. These research gaps warrant further investigation on the utility of video games for promoting social relationships among unacquainted players while in the same physical space. Thus, this study presents the development and evaluation of a couch cooperative video game grounded on sequential team-building mechanics. The findings of this study offer empirical evidence that would have significant practical implications for any organization seeking to increase teamwork and cooperation among its members.
Scopus ID: 85159451013
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-6
Conference Paper | Published: January 1, 2022
Abstract
In this study, there were two (2) derived models which are the compressive strength and slump workability of concrete with waste ceramic tile without adding any additives using an Artificial Neural Network (ANN) model based on five (5) different input parameters which are the Amount of Fine Aggregate (FA), Amount of Coarse Aggregate (CA), Cement Dosage (C), Water-cement ratio (W/C) and the Amount of Waste Ceramic (CW) respectively while concrete slump and compressive strength test result as an output on the model. The two (2) derived models have satisfactory accuracy where the regression values are 0.98007 and 0.99643 and the mean square error of 10.218 and 1.4927, respectively. All models show excellent accuracy has a maximum error of 19.07% and average error of 2.2%. for slump workability, maximum error of 9.26% and average error of 1.81% for compressive strength model. Parametric study was used to describe the behavior of the derived models, the addition of ceramic waste improves the mechanical properties of the concrete, specifically its compressive strength, while the value of slump workability decreases. The study also performs the relative importance calculation, and based on the results, water to cement ratio (w/c) is the main contributing factor for the slump workability and compressive strength model among other parameters, having the most contributing relative importance value of 28.35% on slump model and 27.47% on compressive strength model.
Scopus ID: 85159430839
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-5
Conference Paper | Published: January 1, 2022
Abstract
The paper aims to examine the factors that affects the successful implementation of the Mastery-based Individualized Learning Enhancement System (MILES) in the Far Eastern University (FEU) Institute of Technology. Two periods were analyzed which are the initial implementation, and this is the start of the pandemic period, and the year after the initial implementation of MILES. The Artificial Neural Network (ANN)-Particle Swarm Optimization (PSO)-based Sensitivity Analysis (SA) was utilized to determine the relative importance (RI) index among the influencing factors that affects the students’ level of satisfaction of the MILES implementation. Survey questionnaires were deployed through the canvas platforms and were answered by the students. In the initial survey, a total of 5763 students responded. For the SY 2020-2021, it was observed that the most influential variable to the student’s performance during the MILES Implementation is Course Adviser Rating while the parameters with the least impact to the student’s performance is the Student’s Status as regular or irregular student. For the survey on SY 2021-2022, the highest relative index is for the lesson preference while the lowest importance index is for the opportunities. Findings of the study shows that the use of NN-PSO based sensitivity analysis is an effective tool for establishing the significance of each variable to a target output.
Scopus ID: 85159468457
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-4
Conference Paper | Published: January 1, 2022
Abstract
In this paper, the analysis of metal and plastic straw choice of individuals residing in Metro Manila is conducted using discrete choice modelling. The mathematical model was correlated with the key factors determined in the study. Using the RStudio software as tool, a discrete choice model is generated. The key factors for straw choice of plastic and metal were hygiene, comfortability, trend, habituation, value, and willingness to buy their choice of straw.
Scopus ID: 85159386039
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-6
Tony Rey R. Escalona, Diana Rose T. Rivera, ... Vee Jay Ramos
Conference Paper | Published: January 1, 2022
Abstract
The solar-powered smart waste segregation trash bin with image processing can help in managing the solid waste generated in a community. This device is designed to segregate four types of recyclable solid waste materials namely; plastic, glass, metallic, and paper with the help of image processing and a series of sensors installed in the device for detection, selection, and feedback. The solid waste material is scanned using a camera and compared to the waste images datasets that have been tested and trained model by the researchers, using TensorFlow and python 3.7. The smart waste segregation trash bin can also sense the amount of solid waste inside the compartment with the help of an ultrasonic sensor. Aside from that, through the GSM module, it can generate an SMS notification to the authorized personnel once the bin reaches its maximum capacity. The device is also equipped with a solar panel that is capable of generating the required energy for the system. The result of the testing shows a 100% success rate for paper and plastics, 70% for metals, and 50% for glass. In terms of generating an SMS message to the user when the bins are full, all trials performed for the device to display real-time level monitoring had a favorable outcome indicated by a 100% success rate.
Scopus ID: 85159408581
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-6
Conference Paper | Published: January 1, 2022
Abstract
When it comes to learning, there are beneficial effects of incorporating gestures in learning mathematics especially within the early ages of life. At first, we learn Math by counting and incorporating our fingers as a guide, as well as writing numbers along on a piece of paper. These gestures are known to be examples of hands-on learning experience for children. Aside from counting, they learn basic mathematical operations as well.Teaching children mathematics is not an easy task. The child’s attentiveness plays a huge factor in the learning process. The average attention span of the child from 2 to 10 years of age is 20 to 30 minutes only. Making the learning interactive can help in maximizing the learning experience of the child within the timespan. Thus, we aim to develop a compact device that is interactive with children when it comes to learning basic mathematical operations such as addition, subtraction, and multiplication.This device has software that uses computer-vision and image detection as a way of teaching children math in an interactive way. The child has two different options of answering, by using hand gestures or premade printed numbers. The child is asked five random basic mathematical questions within a time limit. It has three levels of difficulty which the child must pass in order to progress each level. Furthermore, the software also has audio feedback as a way of letting the child know the correct answer. With these features, children are engaged in an interactive learning opportunity in mathematics.
Scopus ID: 85159468687
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-6
Conference Paper | Published: January 1, 2022
Abstract
Earthquakes or seismic events cause several disturbances in the ground which can harm or damage the structural stability and could turn fatal. Liquefaction causes a sudden movement shift that is out of sync with the rest of the structure. This might cause several structural damages to the property leading to casualties. In the Philippine geographical context, the West Valley Fault which traverses Metro Manila is a seismic threat capable of producing a maximum magnitude of 7.2. This study aims to assess the probability of liquefaction in Farnecio St., Quaipo, Manila as well as the determination whether the structures present will suffer from critical failure or not. Probabilistic Seismic Hazard Analysis and Monte Carlo Simulation were used to determine the seismic hazard. Recorded earthquake history from the Philippine Institute of Volcanology and Seismology was used as part of the seismic analysis. The seismic hazard analysis shows that a magnitude 5.3 earthquake has a 44.44 % probability of occurrence within the 10-to-20-kilometer distance from the seismic source. A peak ground acceleration of 0.458g and 0.548g was also determined for return periods of 500 and 2500 years for which a Uniform Hazard Response Spectrum was generated. The probability of damage for 2% in 50 years and 10% in 50 years is 39.63% and 30.41%, respectively.
Scopus ID: 85159479494
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-6
Conference Paper | Published: January 1, 2022
Abstract
Pork meat is a very important source of proteins and other nutrients, so it requires a high level of quality. There is a serious health risk associated with the consumption of spoiled or contaminated pork meat, which is why it is extremely important to monitor its freshness. In this study, sensor arrays consisting of RGB IR sensors, thermal sensors, electronic noses (gas sensors) for detecting the color, temperature, and carbon dioxide and ammonia level of the pork meat were used to evaluate pork meat quality and estimate shelf life. The use of various supervised machine learning approaches has been applied with optimization to perform classification as to whether the meat was fresh or not, as well as regression analysis to predict the amount of exposure time for the meat that can be used in computing shelf-life estimates. Several high-performance algorithms were then tested, evaluated, and compared after hyperparameters of each model were optimized using grid search. As a result of a comparative analysis of the machine learning used, gentle boost ensembles outperformed other machine learning methods in detecting pork meat quality with 92.8% accuracy, while gaussian process regression predicted shelf life with the lowest RMSE, MSE and MAE.