International Journal in Information Technology in Governance, Education and Business

Exploring ChatGPT’s Proficiency in Nonparametric Statistics: An Initial Review and Benchmark Assessment

2025-04-19T17:50:46+07:00

Artificial Intelligence (AI) is transforming education, particularly in teaching statistics, by enhancing personalized learning and feedback through tools like ChatGPT (Tulsiani, 2024). ChatGPT is an advanced artificial intelligence chatbot developed by OpenAI that uses deep learning to understand and generate human-like text. It is based on the GPT (Generative Pre-trained Transformer) model, trained on vast amounts of text data to assist with answering questions, generating content, and engaging in natural conversations. This study evaluates ChatGPT version 3.5 performance in nonparametric statistical analysis by assessing its ability to generate solutions for seven tests, including the Test of Randomness, ANOVA, Chi-Square Goodness-of-Fit Test, Median Test, Cochran’s Q Test, Wilcoxon-Mann-Whitney Test, and Binomial Probability Test. Using three prompt engineering strategies—Basic Prompt (BP), Structured Prompt (SP), and Error-Awareness Prompt (EAP)—ChatGPT's outputs are compared against manual calculations and statistical software (Jeffreys’s Amazing Statistics Program(JASP) and Excel) for accuracy, consistency, and clarity. Results show significant discrepancies in Basic Prompt outputs between November 2023 and 2024, with sum of squares values of 6421.82 and 6928.00, and an F-value of 0.93 (p = 0.53), indicating no significant difference. Similarly, the effect of prompt type is statistically insignificant (F = 1.43, p = 0.26), as is the absolute error analysis (F = 0.59, p = 0.57). However, differences in statistical test approaches are significant (F = 3.10, p = 0.04), suggesting that method selection impacts accuracy. Findings emphasize the role of structured and error-aware prompts in improving ChatGPT’s performance, highlighting the importance of effective prompt engineering in nonparametric statistics. These insights contribute to improving AI-assisted learning in statistical education and research, ensuring more reliable computational outputs. Lastly, guidelines for effective prompt engineering in Nonparametric Statistics were formulated.

Received Date: February 2, 22025
Revised Date: March 18, 2025
Accepted Date: March 30, 2025

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Morphological Evolution of the Krasnodar Reservoir Bed (2006-2021): Insights from Geomorphometric Analysis and Benthic Form Transformations

2025-04-20T11:26:02+07:00

In the Krasnodar region of the Russian Federation, a water reservoir was established along the Kuban River in 1973 and has undergone gradual siltation and significant morphological changes over the years. This study employs geomorphometry to examine the reservoir’s bathymetry and categorize its mesoscale landforms, drawing on multiple bathymetric surveys. By utilizing Digital Benthic Models (DBM) and geospatial analysis, we examine the morphological evolution from 2016 to 2021. The results reveal notable transformations in benthic forms, including the disappearance of U-shaped valleys and their transition into canyons and plains. Spatial correspondence analysis and quantitative assessments offer insights into the consistency and changes within the reservoir’s landscape. These findings not only contribute to a deeper understanding of sedimentation processes and reservoir morphometry but also have practical implications for reservoir management and environmental conservation.

Received Date: December 4, 2024
Revised Date: March 17, 2025
Accepted Date: March 27, 2025

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The Effect of AI-Powered Technologies on the Motivation to Learn Foreign Languages: A State-of-the-Art Review and Meta-Analysis

2025-06-25T22:15:25+07:00

This paper reviews current research and provides a meta-analysis on how AI technologies, specifically Automatic Speech Recognition (ASR), Text-to-Speech (TTS), Machine Translation (MT), and Generative AI (GenAI), affect motivation in Second Language Acquisition (SLA). Learning a new language is a demanding task that requires ongoing motivation and significant effort. Modern AI tools offer new possibilities for supporting language learners and potentially making the learning process easier and more engaging. However, these same technologies may also reduce motivation by allowing learners to avoid challenging language tasks by relying heavily on technological support. By examining 35 peer-reviewed studies, this review finds that most (66%) report a positive impact of AI tools on learner motivation. Practical evidence suggests these technologies can significantly boost motivation, engagement, and language proficiency, especially under controlled experimental settings. However, concerns remain about learners becoming too dependent on technology, potentially lowering their internal motivation to truly “learn” a language. The paper recommends that teachers actively guide students in using AI tools effectively to ensure meaningful language learning and to maintain genuine motivation. It concludes that integrating AI tools into language education requires careful balance, recognizing their benefits while avoiding excessive reliance and superficial engagement.

Received Date: April 16, 2025
Revised Date: May 10, 2025
Accepted Date: May 16, 2025

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iCensr: A Web Image Detection and Censorship Plugin Utilizing the YOLO Deep Learning Method

2025-06-25T22:09:14+07:00

The research introduced and developed a content moderation tool designed for Chromium-based browsers such as Google Chrome. It delved into assessing the effectiveness of YOLO v8 within iCensr, a browser plugin aimed at improving online browsing by ensuring a secure web environment. The primary objective of the plugin is to detect and censor objectionable images, including those depicting nudity, violence, and illicit drugs, across diverse websites, thus regulating content exposure online. The study evaluated the models of iCensr using Mean Average Precision (mAP). A total of 58 participants assessed the iCensr plugin through a Likert-scale survey based on ISO/IEC 25010:2023 acceptability standards. The outcomes of the evaluation suggest that iCensr is deemed "Highly Acceptable," indicating its potential to contribute to safer online interactions. The research underscores the significance of digital tools like iCensr in mitigating online risks and fostering a secure online environment for users of all ages. Additionally, the researchers recommend that future developers and researchers expand the censorship categories, implement other techniques, create a mobile version, and acquire better datasets for enhancing its functionality and effectiveness.

Received Date: April 8, 2025
Revised Date: May 2, 2025
Accepted Date: June 7, 2025

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Evaluation of a UPOU MOOC in terms of Selected Metrics from the Biggs’ 3P Model: Process Variable Result

2025-06-25T22:45:30+07:00

Since 2012, the University of the Philippines Open University (UPOU), following its mission of providing greater access to quality education and Republic Act 10650 (Open Distance Learning Law), has been offering Massive Open Online Courses, or MOOCs (Almodiel et al., 2020). However, evaluation of MOOCs must be done to ensure that learners are receiving quality education. This study follows the MOOC Quality Guidelines’ framework using Biggs’ (1993) 3P Model by the Commonwealth of Learning (2016). For this research, the August 2022 MOOC “Artificial Intelligence for Quality Assurance in Education” was assessed for quality assurance. It was evaluated in terms of selected metrics from the Biggs’ 3P Model, with this paper presenting the results of evaluating the learning process as a process variable. The process variable in this study refers to discussion forum posts made by the learner after every module. Only the posts made by learners who had given their consent for the study were analyzed using the Linguistic Inquiry and Word Count (LIWC) in three dimensions: analytic, clout, and dictionary words. With a total of 70 participants—42 male and 28 female—results showed a high percentage of engagement and quality of the posts from its analytics (66%-76%) and dictionary words (81%-87%), with average frequency (42%-55%) under the clout dimension. Evaluation of the MOOC will help provide best practices in offering MOOCS, thereby contributing to society as an open university in providing quality education for all. Results on the presage and product variables will be discussed in separate papers.

Received Date: April 10, 2025
Revised Date: May 15, 2025
Accepted Date: June 14, 2025

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Assessing On-the-Job Training (OJT) in Computer Studies: Implications for OJT Manual Development

2025-06-26T14:33:26+07:00

This study was conducted to disseminate information and raise awareness among OJT students in the College of Computer Studies internship program. The researcher concludes that the intern students have acquired the necessary knowledge and skills required for their respective courses, as evidenced by the high ratings results given to them by the host establishment in terms of competencies, skills, attitude, and personality development as excellent, and that both the school and host establishment have provided enough support and training for OJT since the practicums encountered minimal problems in their OJT in terms of training, work environment, and school support. Moreover, it was proven that the students and the company successfully handled time work distribution and had a very high rating for their competence. The OJT students voluntarily assist others (as needed) in completing their responsibilities. The study directly affects the student’s demographic profile, with different factors affecting their skills, ability, training, and competence, with 89 responses. There are numerous areas in which this study of the student's internship could improve the process of their On-the-Job training course. The study focuses on the analysis and interpretation of the survey results conducted by the researcher. And the basis for the development of the OJT manual. The researcher administered survey questionnaires via Google Forms to the College of Computer Studies undergraduate students. It is suggested that an on-the-job training manual be proposed for faculty members, students, and companies involved in the OJT programs.

Received Date: April 2, 2025
Revised Date: May 25, 2025
Accepted Date: June 18, 2025

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Data-Driven Decision-Making through Real-time Student Progress Monitoring: Academic Administrators Perspectives

2025-06-28T03:29:12+07:00

Academic administrators guide institutions and play a significant role in improving students' academic paths. Realizing this crucial role, data-driven leadership becomes indispensable for school leaders to enhance performance and ensure student success. This data-driven leadership involves a systematic process of making informed and strategic decisions through analyzing relevant available data. Several studies highlight the advantages of data-driven decision-making in fostering administrative collaboration, transparency, and evidence-based decision-making, thus improving institutional effectiveness, elevating student achievement, and enhancing resource management efficiency. This study examined the effectiveness of real-time student progress monitoring and the perceived importance of data-driven decision-making among academic administrators. This study uses a mixed-methods research design to offer greater flexibility in the research process, enhance validity, increase practical applicability, and foster a more in-depth understanding of the phenomena being examined Results showed a high perceived value of data-driven decisions (x̄=4.491) and strong acknowledgment of the role of real-time monitoring in academic decision-making and achievement (x̄= 4.480). However, the evaluation of existing student information systems was moderate (x̄= 3.800), indicating the need for system enhancements to better support informed administrative decisions.

Received Date: April 9, 2025
Revised Date: May 22, 2025
Accepted Date: June 18, 2025

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A Cross-Platform Educational Mobile Application with Dynamic Content Management: Development and Evaluation of LAYAG

2025-06-27T01:09:13+07:00

Mobile learning technology integration in higher education is vital for institutional digital transformation, since 85.74% of the worldwide population uses cellphones for education. This project develops and evaluates LAYAG, an innovative assistant mobile app with an integrated Content Management System (CMS) to solve significant difficulties in centralized academic service delivery at Lyceum of the Philippines University-Cavite. The research uses Flutter, Dart, Firebase, MySQL, XAMPP, PHP, HTML, CSS, and mixed-methods evaluation to construct an educational technology adoption solution. By integrating academic portals, book borrowing request systems, academic calendars, password management, and real-time alerts, LAYAG successfully integrates mobile learning technologies. The application's dynamic CMS uses adaptive learning platform concepts to let non-technical administrators modify material without code changes, solving static content restrictions in institutional mobile apps. LAYAG was designed for Android smartphones version 8.0 and above to optimize user experience, security, and straightforward navigation following university mobile application best practices. The comprehensive review included 40 participants (15 students, 15 faculty/employees, and 10 IT experts) utilizing Modified Android Core App Quality Standards and ISO/IEC 25010 criteria. Excellent performance with 100% functionality and compatibility test pass rates across Android versions (11.0, 12.0, and 13.0) and device combinations. All stakeholder groups rated LAYAG "Highly Acceptable" across all assessment parameters, with mean ratings of 3.50–3.82 on a 4-point scale. Privacy and Security obtained the best scores (M = 3.82, SD = 0.39 for students), indicating excellent data protection. The Technology Acceptance Model validation showed excellent perceived utility and ease of use ratings, validating institutional educational technology adoption theories. LAYAG's successful implementation shows how strategic integration of mobile learning technologies with comprehensive content management systems can improve academic service accessibility and administrative operational efficiency in higher education. The study provides the first comprehensive evaluation of integrated mobile applications and CMS development for institutional contexts, providing practical guidance for universities pursuing similar digital transformation initiatives while maintaining robust security standards and user-centered design principles.

Received Date: April 9, 2025
Revised Date: May 22, 2025
Accepted Date: June 18, 2025

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Arduino Programming Education Using Tinkercad: A Mixed-Method Study on Usability and Student Engagement During the COVID-19 Pandemic

2025-06-29T02:30:40+07:00

The COVID-19 pandemic necessitated rapid transitions from traditional face-to-face instruction to emergency remote teaching, particularly challenging hands-on technical education. This mixed-methods study evaluates the usability and effectiveness of Tinkercad, a web-based Arduino simulation platform, for programming education during the pandemic. Using a convergent parallel design, the research examined 42 first-year Computer Technology students at a Philippine state university who transitioned from physical laboratory work to online simulation-based learning. Data collection employed the standardized System Usability Scale (SUS) survey, open-ended questions, and academic performance comparisons with pre-pandemic cohorts. Results revealed a below-average overall SUS score of 58, with notable gender differences (female students: 62, male students: 54). Despite suboptimal usability ratings, 74% of students expressed willingness to use Tinkercad frequently, and 67% found it easy to use. However, academic performance declined from pre-pandemic averages (2.4 to 3.3), indicating learning challenges. Key barriers included pandemic-induced mental health impacts, inadequate internet infrastructure in the Philippines, and the inherent limitations of simulation-based learning compared to hands-on experiences. The study demonstrates that while Tinkercad can maintain educational continuity during crisis situations, successful implementation requires addressing infrastructure constraints, providing enhanced technical support, and incorporating mental health considerations. Findings suggest simulation platforms serve as viable emergency alternatives but highlight the continued importance of hands-on experiences in technical education and the need for hybrid learning approaches.

Received Date: April 3, 2025
Revised Date: May 15, 2025
Accepted Date: June 19, 2025

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