Student engagement and enrichment are key factors as learners have shifted from the traditional classroom to the virtual classroom when a flexible learning environment has been in place. It has posed a challenge for educators to shift from traditional educational methods to the utilization of technology-enhanced learning all the more since online and flexible mode were often used since the pandemic. This paper intends to convey that students may exhibit engagement inside the virtual classroom by posting student-generated questions during a discussion forum in the Canvas LMS as question-based inquiries. More so, students have enjoyed and increase their academic performance by playing and participating in a gamified learning activity. This research discussed an activity in encouraging students' learning, curiosity, and involvement in online learning environments. The authors aim for this objective by fusing the 1) utilization of student-generated questions in the goal of promoting learning and thinking, and 2) boosting student engagement through the use of gamified activities which increases students’ academic performance. These intentions will be realized in the virtual classroom implementation and its assessment. Employing the Canvas New Analytics, 148 first year-second semester students questioned their teacher about a course topic and as their questions were each replied to, they were graded through discussion post with an average grade of 99% for all sections. The game Jeopardy was played as an examination review session for the same sections and 181 students from the four sections have an average grade score of 77.5% in their midterm examination. A course learning objective item is also measured with a university target that at least 60% of students should reach an average grade score of 80% and above. For the CLO target, average of four sections reached 68.3% which is descriptively attained. Generating questioned-based inquiry in the forum discussion showed an increase in engagement and enrichment from students. The findings have a positive impact on them as their posted inquiries and questions gained them grades which indicated high average grade for each section. Utilizing a gamified learning activity in a pure online setting for the school year 2021-2022, allowing the students to participate in educational games is necessary to boost engagement and enrichment as well.

Efficient and reliable medical image analysis is indispensable in modern healthcare settings. The conventional approaches in diagnostics and evaluations from a mere picture are complex. It often leads to subjectivity due to experts' various experiences and expertise. Using convolutional neural networks, we proposed an end-to-end pipeline for automatic Cobb angle measurement to pinpoint scoliosis severity. Our results show that the Residual U-Net architecture provides vertebrae average segmentation accuracy of 92.95% based on Dice and Jaccard similarity coefficients. Furthermore, a comparative benchmark between physician's measurement and our machine-driven approach produces an acceptable mean deviation of 1.57 degrees and a T-test p-value of 0.9028, indicating no significant difference. This study has the potential to help doctors in prompt scoliosis magnitude assessments.

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.

Artificial intelligence-based medical image analysis promises an efficient and reliable diagnosis in today's healthcare. Traditional approaches for cataract screening by medical practitioners often results in subjectivity due to their varying levels of knowledge and expertise. Using transfer learning, ensembles of pre-trained convolutional neural networks, and stacked long short-term memory networks, we developed a non-invasive and streamlined pipeline for automatic cataract severity classification. Empirical results show that our proposed combined models of AlexNet, InceptionV3, Xception, and InceptionResNetV2 using a weighted average algorithm produces 99.20% (normal vs. cataract) and 97.76% (normal to severe) accuracies compared to standalone models. Furthermore, the ensemble model reduces classification error rates by an average of 2.17%. This study has the potential to help doctors to specify the magnitude of cataract stages with highly acceptable precision.

The association between nutrition and health has been repeatedly established by the field of nutrition science and evidence-based practices. Nevertheless, inadequate nutrition is still prevalent among Filipino households. As a response to this public health issue, a nutrition system called Virtual Dietitian (VD) was conceived. Through a mixed-methods approach, VD was beta tested via a user study and System Usability Scale (SUS) by six information technology experts and six registered dietitians. Participants performed the standardized tasks with a mean of 85% completion rate and 106.2 seconds, and graded SUS with a mean score of 83.4 (excellent). Albeit the prototype successfully exhibited the potential of VD as a nutrition system, qualitative feedback from experts revealed some modifications that are needed to accomplish before going to the next phase of the study. Healthcare professionals delivered their feedback on the correctness of processes and meal plan generation while the information technology experts pointed out the deficiencies of VD from the technical perspective (e.g., web standards, layout and design, functionality, navigation, usability). With this beta evaluation, an overview of the true experience gained by end users while using VD was determined without the trouble of undergoing the whole project lifecycle. Feedback from experts, which will be used in the next phase, were beneficial to ensure that the final version of VD will be correct, useful, and valid.

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.

Personal financial management is undeniably a worthwhile practice to establish a financial security during a struggling economy and make intelligent monetary decisions regardless of the plethora of spending temptations. Monitoring personal cash flow is part of achieving financial independence, and it is now undemanding to perform because of the available personal budget apps and finance tools. Nevertheless, a missing feature of these technology-driven innovations is the recording, tracking, and monitoring of receipts as well as the generation of personal expenses reports based on these collected pieces of papers. With this application, “Mobile Bookkeeper”, financial enthusiasts can just scan the receipt using the inbuilt camera of any smartphone and details will be automatically transcribed using Optical Character Recognition (OCR). To measure the satisfaction and test the usability of the mobile app, subjective and objective measures via ISO 25062 and ISO 9241 standards were collected, and QUIS 7.0 questionnaire, respectively. The testing results established Mobile Bookkeeper particularly on its receipt scanner feature as a needed mobile finance app. Together with this acceptance is the report highlighting issues and challenges in developing such mobile application especially with OCR integration and its accuracy in text recognition.

With the significant adverse impact of a pandemic like coronavirus disease 2019 (COVID-19) towards the teaching and learning experience, numerous educational institutions are looking for ways to improve their current practices and meet the challenges of this global threat. Despite the recommendations of applying Information and Communications Technologies (ICT) like video games to alleviate the negative effects of the pandemic, it is still not clear whether nursing teachers are willing to use it. Consequently, this study explored nursing teachers’ behavioral intention to employ mobile game-based learning (MGBL), and its relationship amongst core factors of the Theory of Planned Behavior (i.e., perceived behavioral control, subjective norms, and, attitude). Descriptive statistics revealed that most of the nursing teachers were female, a master’s degree holder, with an academic rank of instructor, not a licensed professional teacher, and a permanent and full-time employee at private institutions in the Visayas region of the Philippines. Moreover, they do not play mobile games and do not have an experience when it comes to MGBL. Lastly, Spearman’s correlation analysis revealed that Theory of Planned Behavior factors correlated positively with the intention of nursing teachers to use MGBL. This descriptive-exploratory study serves as a preliminary exploration of MGBL in nursing education and a future study will cover the prediction of nursing teachers’ intention to use MGBL in the classroom.

Since its inception, Technology Acceptance Model (TAM) has been a commonly adopted theory for understanding users’ acceptance of various types of information systems (e.g., online learning systems). Over the years, different information systems theories have been integrated into TAM to further the understanding of users’ intention to accept online learning. To examine the literature, four databases were utilized to discover research articles examining the online learning acceptance and continuance intention of users (e.g., students and teachers). The findings of the systematic review revealed that Task Technology Fit and Theory of Planned Behavior are the most integrated and educationally successful theories into TAM. Meanwhile, course information, satisfaction, perceived usefulness, attitude, system quality, perceived ease of use, and academic performance are the essential drivers for the acceptance or continuance usage of online learning systems. These findings serve as an evidence and reference for educational institutions in developing policies and strategies for the implementation of an online education.

Assistive technologies gained traction in the medical field over the last few decades. Novel approaches have been developed in order to support people with disability to communicate effectively. However, little research has been conducted on the other side of the coin, that is, assistive technologies to help people who do not have a disability to understand and comprehend the language of disabled. This study describes the early development of a hand alphabet recognition that intends to accomplish a functioning dactylology conversion from sign language to English print in a live streaming video. Through a video analysis, each frame is processed using a segmentation technique to partition it into different segments (e.g., pixels of hand gesture). The dactylology conversion algorithm was implemented in a mobile application where users can watch video containing an on-screen sign language interpreter and understand fingerspelling used as a communication by hearing- and speech-impaired people. Through the sample dataset of 13 videos of American Sign Language manually collected (N=10) and recorded (N=3), the application was tested for its accuracy in detecting the alphabet in a video (94.16%), and the correctness of conversion of the detected alphabet into English print (89.65%). This study contributes to the list of existing novel approaches that aims to promote social positive effects as well as improve the quality of life for both disabled and all the people they socialize with.