Scopus ID: 85136479756
Advances in Mobile and Distance Learning, (2022), pp. 23-46
Book Chapter | Published: June 24, 2022
Abstract
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.
Scopus ID: 85126595729
2022 4th Asia Pacific Information Technology Conference, (2022), pp. 111-115
Conference Paper | Published: January 14, 2022
Abstract
The sudden transition to emergency remote education (ERE) caused by the pandemic has been a highly complex undertaking for teachers and students alike. For developing countries, such a disruption only aggravates the pre-existing global education crisis and influences the sector in unprecedented ways. Thus, we explored how teachers and students from higher education in a developing country adapt to ERE during the pandemic. Specifically, we attempted to identify the common challenges faced by teachers and students and their coping strategies to handle pandemic-induced stress. To this end, we conducted a comparative cross-sectional study from October to November 2021 with 78 teachers and 94 students from a higher education institution in Manila, Philippines. Our results show that while self-regulation is the greatest challenge among students, it is the conduciveness of the home environment for teachers. Interestingly, although teachers and students have varying concerns, both groups rely on acceptance, humor, and positive reframing as their coping strategies. By painting a holistic picture of the challenges and coping strategies of both teachers and students, education policymakers and administrators can make an informed decision on how to best continue ERE and prepare in advance for the resumption of school in the new normal.
Scopus ID: 85126615287
2022 4th Asia Pacific Information Technology Conference, (2022), pp. 138-143
Conference Paper | Published: January 14, 2022
Abstract
The Learning Management System is an innovative tool to facilitate online learning using technology. It monitors students’ learning progress and actions. As most academic institutions are already shifted from the traditional learning to online and blended learning approaches, analysis of students’ learning behaviors is empirical to design necessary and suited academic intervention programs. With this, the researchers aimed to identify significant attributes affecting student academic performance in an online education environment. The knowledge discovery in databases (KDD) was used to provide step by step process in extracting and evaluating the predictive and cluster models which aim to classify students who will have academic learning difficulty based on sets of parameters and constraints. The study reveals that students with low engagement in online learning are those with problems in terms of their academic performance. Therefore, the study reaffirmed that there is a strong relationship between student behaviors in LMS and academic achievement.
Scopus ID: 85159487429
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 medical imaging, an X-ray image generated using a flat panel detector (digital) typically has poor image quality, affecting the capability of successful medical diagnosis based on the images. The image enhancement process intends to provide better interpretability of the information contained in the images. The main problems considered for medical images include poor quality and low contrast. Therefore, the general objectives of image enhancement include contrast improvement and noise reduction. This study proposes an upgraded X-ray image enhancement hybrid algorithm that utilizes and consists of the Contrast Limited Adaptive Histogram Equalization (CLAHE) method combined with the Wiener filter. Based on the performance metrics results, including MSE, PSNR, and Entropy, as compared to the existing CLAHE method only, the proposed methodology has a lower MSE signifying lower error, a higher PSNR representing a lower amount of distortion, and higher information entropy which indicates higher obtained information. Furthermore, the implementation of the proposed approach is applied to 6000 X-ray images before deep learning classification modeling, which significantly improved from 50% to 78% validation accuracy. Therefore, the proposed method improves the image enhancement methodology and can substantially assist in diagnosing diseases.
Scopus ID: 85159384712
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
Computer programming is one of the fundamental skills in the field of computing [1]. In a computing class, students are expected to learn the skills rather than remembering materials only. This study aims to develop a constraint-based student model (CBM) by analyzing the computing students' C compilation error logs. The proposed modified CBM will be used as input to develop a user behavior of an ongoing study for an intelligent tutoring system. The prototype was developed to obtain compilation error logs from the selected students, it contains five (5) C programming questions that focus on assignment statements. The prototype of the study was pilot tested on two (2) online programming classes with a total of thirty-one (31) freshman college students composed of nine (9) BSCS and twenty-two (22) BSIT participants with a mean age of 18.68, where nineteen (19) or 61.3% are males and twelve (12) or 38.7% are females. The study uses convenience sampling to determine the total number of student participants. The dataset was extracted from the prototype and feature identification was performed on one thousand thirteen (1013) C programming logs which resulted to obtain eight (8) error types. The paper of Khodeir, Wanas, & Elazhary (2018) [2] and Karaci (2018) [3] on constraint-based modeling was reviewed to develop a proposed constraint-based model in the context of C programming focusing on assignment statements. By mapping a student error on the suggested constraint relevance (Cr) and constraint satisfaction, the database for constraints was finished (Cs).
Scopus ID: 85159458487
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-6
Diana Rose T. Rivera, Ernet L. Maceda, ... Leif Oliver B. Coronado
Conference Paper | Published: January 1, 2022
Abstract
This research works focuses on the co-pyrolysis of coconut coir fiber combined with PET in order to increase its heating value, in addition to solid mass reduction for prolonged shell life and storage issues. Co-pyrolysis is a process of efficiently producing high-quality biofuel from two or more materials. Parameters combinations were identified using the Taguchi optimization methodology model in MINITAB19. Nine samples with three replications were evaluated. Results revealed that changing the temperature, duration, and feedstock blends show a significant effect on solid mass yield and heating value. The biochar with 75:25 (coconut coir fiber: PET) shows that duration and temperature directly affect the solid yield. For biochar, with 25:75 (coconut coir fiber: PET), pyrolysis duration contributed largely to the output. The highest solid mass reduction with an average of 55% solid yield was obtained. Despite a high solid mass reduction, the heating value measured is only 13 MJ/kg. Feedstock blend with PET to coconut coir ratios of 75:25, 25:75, and 50:50 resulted to an average solid yield of 70%, 65%, and 83% respectively. In terms of heating value, for all three replications, the biochar sample subjected to 200°C, 30 minutes, and PET to coconut coir ratio of 75:25, with an average solid yield of 67%, had the highest value with 20.94 MJ/kg, 24.42 MJ/kg, and 23.55 MJ/kg for Trial A, B, and C, respectively. The result shows that the incorporation of PET effectively increases the heating value of the coconut coir fiber from 10 MJ/kg to 24.42 MJ/kg.
Scopus ID: 85159461605
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
The college entrance examination is vital for program admission. Typically, entrance examinations are conducted onsite using paper and pens. When the COVID-19 pandemic hit, the entrance examination was lifted and physical gatherings were prohibited. Since many schools cannot offer an online admissions exam, they rely on grades and interviews to admit and qualify students for degree programs. However, academic standards differ between schools, and grades may not be enough to assess students' capacity. Thus, this study aims to develop an Online Proctored Entrance Examination System (OPEES) with Degree Program Recommender for colleges and universities to help institutions administer onsite or online entrance tests and generate course suggestions using a rulebased algorithm. The study employed the scrum methodology in software development. OPEES allows applicants to submit applications online, and institutions can manage user accounts, tailor exams and degree programs’ criteria, manage exam dates, and assign proctors. Online proctoring using Jitsi, an opensource multiplatform voice, video, and instant messaging tool with end-to-end encryption, ensures exam integrity. The system’s features were evaluated by 102 respondents, comprised of end-users (students and school personnel) and IT professionals, using the FURPS (Functionality, Usability, Reliability, Performance, and Supportability) software quality model. In the software evaluation, the overall system proved to be functional as perceived by the respondents, as manifested by the mean rating of 4.61. In conclusion, the system's architecture was deemed feasible and offers a better way to streamline admission examinations and determine a student’s applicable degree program by enabling institutions to customize their exams and degree program requirements. It will be beneficial to look into recommendation system algorithms and historical enrollment data to improve the system’s use case.
Scopus ID: 85159461464
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
The daily maximum multi-annual series including the rainfall frequency analysis, are one of the inputs for the design process for stormwater management, that entails numerous procedures: (a) rainfall data gathering from Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA), (b) information gathering, and (c) checking all received datasets for missing or different data. To address these setbacks, 6 rain gauge stations located in Region VIII, Visayas, Philippines were used to first determine whether or not the Gumbel Extreme Value (GEV) was the better suitable method to use in producing Rainfall Intensity Duration Frequency (RIDF) than Log-Pearson Type III (LP3) by performing Chi-square test; secondly, to select the better RIDF values; and lastly, the isohyetal maps should be developed for return periods of 2, 5, 10, 25, 50, and 100 years. GEV was a better fit for the x2 values (27.96, 54.59, 52.82, 87.96, 11.78, 7.66) obtained through chi-square test were close to or smaller than the critical value of 30.144. The RIDFs produced in GEV were used in plotting isohyetal maps. In all return periods, Borongan generated the highest rainfall intensity value.
Scopus ID: 85159477384
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
Carbonation is a dangerous threat to concrete since it reduces the alkalinity of normal or self-compacting concrete (SCC), allowing iron to corrode and spall the cover. The goal of this research is to use an artificial neural network to create a corrosion prediction model for steel in self-compacting concrete that has been subjected to carbonation. In this study, MATLABR2019a was used to create a feedforward back propagation neural network. As a training function, the researchers utilized the Levenberg-Marquardt back propagation (TRAINLM) which adjusts weights and bias values using Levenberg-Marquardt optimization. The researchers used gradient descent with momentum weight/bias learning (LEARNGDM) for the adaptation learning function, which is a technique that aids the gradient in determining which way to go. The network’s performance was measured using the mean square error (MSE). The Hyperbolic tangent sigmoid transfer function (TANSIG) was also employed as the transfer function since the values obtained by this function range from +1 to -1, considering both the positive and negative aspects of the parameter. To minimize overfitting, the number of hidden nodes should be fewer than the number of input parameters. The researchers tested 4-12 hidden nodes. Modeling was done using data from 102 experimental studies of self-compacting concrete exposed to corrosion. Using feed-forward back propagation ANN with 1 hidden layer and 8 hidden nodes, a Pearson R-value of 0.98748 and a mean square error of 0.5725 were obtained. The factor that most affect the carbonation depth were water-cement ratio and fly ash content. The suggested model was able to analytically describe the connection and behaviors of the various mixtures to the carbonation depth in the parametric investigation. The parameters characteristics were likewise described by the model.
Scopus ID: 85109945501
Journal of Information Technology & Politics, (2022), Vol. 19, No. 2, pp. 165-179
Rex P. Bringula, Annaliza E. Catacutan-Bangit, ... Arlene Mae C. Valderama
Journal Article | Published: January 1, 2022
Abstract
This study determined the items that could predict university students’ susceptibility to disinformation (e.g., fake news). Toward this goal, randomly-selected students from the four private universities in Manila answered a content-validated and pilot-tested survey form. Through binary logistic regression analysis, it was found that frequent visits to Instagram, sharing a political post of a friend, and liking a post of a political party could increase the susceptibility of students to fake news. On the other hand, sharing the post of a political party, and seeking the opinion of experts could decrease the susceptibility of students to fake news. Of these items, liking a post with a similar opinion of a political party – a confirmation bias – had the highest contribution to fake news susceptibility of students. It is worth noting that the most reliable source of information, i.e. the library, is the least utilized fact-checking resource. It can be concluded that technological, internal, and external factors contribute either positively or negatively to the susceptibility of students to fake news. Implications to combat fake news are offered.