Scopus ID: 85136478850
Socioeconomic Inclusion During an Era of Online Education, (2022), pp. xviii-xxiii
Editorial | Published: June 24, 2022
Abstract
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 teaching and learning online not only during a pandemic (i.e., emergency remote education) but also during “normal times”. As much as this book raises socioeconomic issues involving online education as the primary mode of teaching and learning, a theme as diverse and expansive as inclusivity and technology-based instruction restricts the opportunity of addressing the multiplicity of viewpoints across various spectrums. Therefore, the anthology of these chapters is consequently and intentionally diverse and echoes the mission of inclusive education by compiling a variety of perspectives, experiences, traditions, and frameworks.
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: 85152187938
2022 2nd International Conference in Information and Computing Research (iCORE), (2022), pp. 139-143
Jake Dave M. Esteban, Michaela D. Gipala, ... Renato R. Maaliw
Conference Paper | Published: January 1, 2022
Abstract
Oclean's goal is to raise awareness about the harmful effects of plastic waste in the ocean. The game was created for use on a mobile device. It's a 2D game with a single player endless mode. A scoring system and a timer were also included in the game. In addition, the game has a gallery where players could see what is happening in real life. This initiative believes that little acts can generate significant effects in the fight against plastic pollution in our oceans. In addition, interactive information about the issues of marine plastic trash was also provided. The game's set of objectives were completed, and an unending casual game about ocean cleaning and marine plastic garbage was created effectively.
Scopus ID: 85134876150
2022 IEEE World AI IoT Congress (AIIoT), (2022), pp. 039-045
Renato R. Maaliw, Karen Anne C. Quing, ... Ranie B. Canlas
Conference Paper | Published: January 1, 2022
Abstract
Grit plays a crucial role in determining high individual success more than intellectual talent alone. However, there is no existing literature that ventured into the trait identification in an e-learning environment. This study presents a comprehensive computational-driven strategy for detecting a learner's grit using machine learning. Empirical results show that DBSCAN and Random Forest models produce average accurate prediction consistency of 92.67% against the questionnaire method. Knowledge interpretation using feature importance and association mining quantifies perseverance and sustained interest as the most pressing component of grit. Correlational analysis reveals that grit has a weak connection with course grades (short-term goal) but demonstrates a strong positive association with professional achievement (long-term goal) and maturation. Collectively, our findings substantiate that breakthrough accomplishment is contingent not solely on cognitive ability but on constant interests and resilience.
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.
Scopus ID: 85130206509
2022 International Conference on Decision Aid Sciences and Applications (DASA), (2022), pp. 1469-1473
Jhon Michael C. Manalo, Alvin Sarraga Alon, ... Ricky C. Sandil
Conference Paper | Published: January 1, 2022
Abstract
Pothole detection is critical in defining optimal road management solutions and maintenance. The researcher used deep learning and yolov3 to create a pothole detection system in this study. A deep learning algorithm called YOLOv3 is used to develop a model that can successfully identify potholes. The detection model had an average precision of 95.43%, and identified potholes had accuracies ranging from 33% to 69%, which is to be anticipated given the numerous various forms and sizes of potholes.
Scopus ID: 85138253909
2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), (2022), pp. 1-5
Dennis C. Malunao, Roger S. Tamargo, ... Roldan D. Jallorina
Conference Paper | Published: January 1, 2022
Abstract
Immediate identification of plant disease is one of the important solutions in Agricultural problems. In this study, the researchers develop an early detection system for tomato leaf diseases. It is important to create a system that will detect and classify a certain disease present in the leaf to prevent further loss. In order to do that, the researchers used an algorithm called YOLOv3 for training a model that accurately detects specific diseases for tomato leaves. The proposed model is able to classify the diseases and has a mean average precision(mAP) of 98.28 %. The result of the trained model varied with the accuracies ranging from 75% - 99%, for detecting the two common tomato leaf diseases such as, Early Blight and Septoria Leaf Spot.
Scopus ID: 85132186753
2022 10th International Conference on Information and Education Technology (ICIET), (2022), pp. 93-98
Riegie D. Tan, Keno Piad, ... Joseph Espino
Conference Paper | Published: January 1, 2022
Abstract
The emergence of information technology used in all factors of our everyday lives has exponentially increased the amount of unstructured data. This huge quantity of records is a great source for finding and thus, may be used for extracting actionable information. In the academe, for instance, teachers and school administrators can adjust their approach to teaching/learning by getting feedback from students through a Learning Management System that can automatically analyze the semantic orientation of words and contextual polarity of these feedbacks - categorizing them into positive and negative. Identifying and classifying words expressed in the students' feedback about learning materials can provide structured information that can guide the teacher, impact its design and target the students' needs. This study implemented a lexicon-based strategy for automatic sentiment analysis using VADER as a model. Student feedbacks are extracted from an LMS developed to demonstrate the usability and effectiveness of the adopted approach; among other features of LMS that will help teachers improve its implementation. Results of the LMS sentiment analysis are compared to human-annotated sentiments to verify and validate the output, as well as, check its accuracy using Confusion Matrix. It aims to create a structured representation of student sentiments through LMS to help teachers improve the design of learning materials.