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All Journal JOURNAL OF APPLIED BUSINESS ADMINISTRATION Social Sciences Insights Journal Educational Studies and Research Journal
Esmeralda, Angel II P.
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Journal : Educational Studies and Research Journal

 1 
Propose Measures for Enhancement: Multiple Intelligence Based Instructional Strategies Esmeralda, Angel II P.
Educational Studies and Research Journal Vol. 1 No. 3 (2024): Educational Studies and Research Journal
Publisher : MID Publisher International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60036/4nt0w594

Abstract

This study explores the effectiveness of multiple intelligence-based instructional strategies in enhancing the learning experiences and development of secondary high school students. Rooted in Howard Gardner’s theory of multiple intelligences, the research aims to identify how various teaching approaches can cater to diverse learner profiles, including mastery, interpersonal, understanding, and self-expressive styles. The study also investigates the impact of these strategies on developing students’ logical-mathematical, verbal-linguistic, and spatial intelligences. Utilizing a descriptive research design, data were collected from teachers at the International Bureau of Management through questionnaires and analyzed using mean scores and weighted averages. Findings reveal that instructional strategies aligned with multiple intelligences significantly contribute to students’ cognitive, affective, and psychomotor development. Mastery and interpersonal teaching styles were particularly effective in fostering engagement and comprehension, while self-expressive approaches promoted creativity and personal insight. Despite the overall positive outcomes, challenges such as managing classroom behavior and facilitating critical thinking were identified as obstacles to optimal intelligence development. The study recommends that educators integrate diverse instructional methods tailored to multiple intelligences and continuously refine their teaching practices to address these challenges. Ultimately, adopting multiple intelligence-based strategies promotes holistic learning, enabling students to realize their full potential across varied intellectual domains.
Sustainable Development Reporting and Financial Performance: Systematic Literature Review 2019-2024 Esmeralda, Angel II P.
Educational Studies and Research Journal Vol. 2 No. 3 (2025): Educational Studies and Research Journal
Publisher : MID Publisher International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60036/g1yhye97

Abstract

This systematic literature review examines the relationship between sustainable development reporting (SDR) and corporate financial performance (CFP), synthesizing empirical evidence from studies published between 2019 and 2024. The review addresses the ongoing debate regarding whether sustainability disclosure creates or destroys shareholder value. Following PRISMA 2020 guidelines, this review analyzed 88 peer-reviewed empirical articles from Scopus, Web of Science, and Google Scholar. Studies were categorized by relationship direction, performance metrics used, reporting frameworks examined, and contextual moderators. The majority of studies (59%) report a positive relationship between SDR and CFP, supporting stakeholder theory and signaling theory. Mixed results (27%) suggest the relationship is contingent on moderating factors including firm size, industry, geographic region, and reporting quality. Negative relationships (9%) are primarily associated with short-term cost perspectives. GRI-aligned reporting and external assurance strengthen the positive SDR-CFP relationship. Market-based measures (Tobin’s Q) show stronger positive associations than accounting-based measures (ROA, ROE). This review provides a comprehensive framework integrating SDR dimensions, transmission mechanisms, and performance outcomes. It identifies critical moderating factors and offers actionable insights for practitioners while highlighting gaps for future research, including the need for longitudinal studies and investigation of SDG-specific reporting impacts.
Fraud Detection and Machine Learning in Auditing:A Systematic Literature Review Esmeralda, Angel II P.; Fadhilah , Nur Hidayah K
Educational Studies and Research Journal Vol. 3 No. 1 (2026): Educational Studies and Research Journal
Publisher : MID Publisher International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60036/y8p1k791

Abstract

This systematic literature review examines the application of machine learning (ML) techniques in fraud detection within the auditing domain, synthesizing findings from peer-reviewed studies published between 2019 and 2024. Following the PRISMA 2020 guidelines, this review analyzed 85 articles from Scopus, Web of Science, IEEE Xplore, and Google Scholar databases. The Kitchenham methodology was employed to ensure rigorous screening, extraction, and synthesis of relevant literature. The review reveals that ensemble methods, particularly Random Forest and XGBoost, demonstrate superior performance in fraud detection tasks. Deep learning architectures, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, show promising results for complex fraud patterns. Key challenges identified include imbalanced datasets, model interpretability, and regulatory compliance. The emergence of Explainable AI (XAI) techniques, such as SHAP and LIME, addresses transparency concerns in audit applications. This review provides a comprehensive synthesis of ML applications in fraud detection specifically within the auditing context, offering a research agenda for future investigations and practical implications for audit practitioners and regulators.
Fraud Detection and Machine Learning in Auditing:A Systematic Literature Review Esmeralda, Angel II P.; Fadhilah, Nur Hidayah K
Educational Studies and Research Journal Vol. 3 No. 1 (2026): Educational Studies and Research Journal
Publisher : MID Publisher International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60036/fy6eyn96

Abstract

This systematic literature review examines the application of machine learning (ML) techniques in fraud detection within the auditing domain, synthesizing findings from peer-reviewed studies published between 2019 and 2024. Following the PRISMA 2020 guidelines, this review analyzed 85 articles from Scopus, Web of Science, IEEE Xplore, and Google Scholar databases. The Kitchenham methodology was employed to ensure rigorous screening, extraction, and synthesis of relevant literature. The review reveals that ensemble methods, particularly Random Forest and XGBoost, demonstrate superior performance in fraud detection tasks. Deep learning architectures, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, show promising results for complex fraud patterns. Key challenges identified include imbalanced datasets, model interpretability, and regulatory compliance. The emergence of Explainable AI (XAI) techniques, such as SHAP and LIME, addresses transparency concerns in audit applications. This review provides a comprehensive synthesis of ML applications in fraud detection specifically within the auditing context, offering a research agenda for future investigations and practical implications for audit practitioners and regulators.
Sustainable Development Reporting and Financial Performance:A Systematic Literature Review 2019-2024 Esmeralda, Angel II P.
Educational Studies and Research Journal Vol. 2 No. 3 (2025): Educational Studies and Research Journal
Publisher : MID Publisher International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60036/agdknk77

Abstract

This systematic literature review examines the relationship between sustainable development reporting (SDR) and corporate financial performance (CFP), synthesizing empirical evidence from studies published between 2019 and 2024. The review addresses the ongoing debate regarding whether sustainability disclosure creates or destroys shareholder value. Following PRISMA 2020 guidelines, this review analyzed 88 peer-reviewed empirical articles from Scopus, Web of Science, and Google Scholar. Studies were categorized by relationship direction, performance metrics used, reporting frameworks examined, and contextual moderators. The majority of studies (59%) report a positive relationship between SDR and CFP, supporting stakeholder theory and signaling theory. Mixed results (27%) suggest the relationship is contingent on moderating factors including firm size, industry, geographic region, and reporting quality. Negative relationships (9%) are primarily associated with short-term cost perspectives. GRI-aligned reporting and external assurance strengthen the positive SDR-CFP relationship. Market-based measures (Tobin’s Q) show stronger positive associations than accounting-based measures (ROA, ROE). This review provides a comprehensive framework integrating SDR dimensions, transmission mechanisms, and performance outcomes. It identifies critical moderating factors and offers actionable insights for practitioners while highlighting gaps for future research, including the need for longitudinal studies and investigation of SDG-specific reporting impacts.
Co-Authors A Halim Abd. Rasyid Syamsuri Akbar, Hardhan Syaifullah Arohman, Rifki Fadhilah , Nur Hidayah K Fadhilah, Nur Hidayah K Halim, Muhammad Irsyad Muhammad Ikhlash Saputra, Muhamad Renaldy Setiani, Cindy