Leading & Enlightening Journal UMY
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Factors Affecting English Teachers’ Implementation of Active Learning: A Case from Ethiopia
No full textBackground: The Ethiopian Ministry of Education introduced active learning into the education system because the traditional teacher-centered method effectively limited students’ target-language learning. However, teachers still dominate most of the instructional process, and students remain passive listeners.
Objective: This study aims to investigate factors affecting the implementation of active learning by secondary school English teachers in North Shoa Zone, Oromia, Ethiopia.
Methods: A descriptive survey research design using a mixed-methods approach was employed. The study participants were 72 English teachers teaching grades 11 and 12 across 15 public schools. Data were collected through questionnaires, interviews, and classroom observations. The data collected through questionnaires were analyzed using descriptive statistics, including mean and standard deviation, in SPSS. Conversely, data obtained through interviews and observations were analyzed thematically. Finally, the data were merged and interpreted side-by-side.
Findings: The findings revealed student-related challenges, including limited language proficiency, a lack of interest, and fear of class discussion. The result also showed that resource-related factors, including large class sizes, fixed seating arrangements, insufficient instructional time, and teachers’ tendency to use teacher-centered methods, posed major challenges to the effective implementation of active learning.
Conclusion: The findings indicated that although active learning has been introduced into the education system, its implementation remains partial due to various factors that impede its effective implementation. Therefore, teachers should shift their teaching methods from teacher-centered to active learning. Furthermore, to improve the successful implementation of active learning, stakeholders should address the identified challenges by providing adequate resources and support
The Effect of Governance, Education, and Economic Conditions on GDP in ASEAN
No full textThis study investigates the effect of governance, education, and economic conditions on GDP in eight ASEAN countries—Indonesia, Malaysia, Thailand, Singapore, the Philippines, Vietnam, Laos, and Cambodia—during the period 2006–2020. Governance is represented by the Corruption Perceptions Index and Ease of Doing Business indicators, while education and inflation are¬¬ used to reflect human capital and economic stability, respectively. Data were obtained from the World Bank and Transparency International. Using panel data regression, the study combined cross-sectional and time-series data. Based on the Chow test, the Common Effect Model (CEM) was identified as the most appropriate. Due to classical assumption violations, robust standard errors were employed to ensure reliable estimates. The results show that Ease of Doing Business, education, and inflation significantly influence GDP. An improved business environment and higher educational attainment contribute to economic growth, while controlled inflation ensures macroeconomic stability. Meanwhile, corruption showed no significant impact on GDP. The findings highlight the importance of improving governance quality, expanding access to quality education, and maintaining stable inflation to support sustained economic growth in ASEAN
Valuing Public Parks for Post-disaster Urban Recovery: Evidence from Rikuzentakata, Japan
No full textThis study examines how an ordinary riverside park in a disaster-affected and shrinking city can be repositioned as social infrastructure supporting community recovery and urban resilience. Focusing on Kawahara River Park in Rikuzentakata, Japan—heavily damaged by the 2011 Great East Japan Earthquake—we applied a contingent valuation survey with a stated-preference design to estimate residents’ and visitors’ willingness to pay (WTP) for park-based improvements such as events, cherry-tree planting, and preparedness facilities. Using multiple-bounded logit estimation, the study linked WTP outcomes to implementable policy choices for pricing and investment sequencing. The median WTP stabilised at JPY 1,600–1,700, interpreted as an upper reference point for feasible fee levels. WTP increased significantly with event programming and symbolic landscape attributes (e.g., cherry-avenue scenery and community interaction), while hard preparedness facilities produced no additional WTP—consistent with the notion that safety infrastructure should be publicly financed. Based on these findings, the study proposes a phased policy portfolio: short-term event programming to strengthen local interaction and economic activity, followed by long-term investment in symbolic landscapes that sustain collective resilience. This research contributes to the literature by integrating disaster recovery, community resilience, and environmental valuation into a unified framework, demonstrating that even ordinary public parks can function as cost-effective social infrastructure for disaster risk reduction and sustainable urban recovery in resource-constrained regions
Dynamics of budget absorption: The role of budget political moderation on human resource regulation and competence in local governments
No full textResearch aims: This study examines the effects of regulations, human resource competence, and budget politics on budget absorption. In addition, it seeks to analyze the moderating role of budget politics in strengthening or weakening the relationships between regulations, human resource competence, and budget absorption. Design/Methodology/Approach: This research adopted a quantitative approach using a questionnaire survey. The study population comprised all government officials from 27 regional apparatus work units in Nagan Raya Regency, Aceh Province, totaling 108 respondents, including service secretaries, financial administration officials, expenditure treasurers, and heads of finance subdivisions. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Research findings: The results indicate that budget politics positively influence public budget absorption, whereas regulation and human resource competence do not have a direct effect. The moderation analysis further reveals that budget politics has a significant negative moderating effect on the relationship between regulation and budget absorption, implying that heightened political intensity may weaken the effectiveness of regulatory frameworks. Conversely, budget politics does not moderate the relationship between human resource competence and public budget absorption.Theoretical contribution/Originality: This study has expanded the literature on the political budget cycle by emphasizing the significance of balancing political stability, regulatory flexibility, and adaptive human resource capacity to improve the effectiveness of public budget absorption.Practical/Policy implication: Local governments should design regulations that are adaptive to political dynamics, strengthen managerial human resource capacity, and optimize digital technologies to enhance transparency and efficiency in budget management
Can bitcoin serve as a reliable safe haven? Amid uncertainty and volatility
No full textResearch aims: This study examines whether Bitcoin can serve as a safe-haven asset amid global market uncertainty during the 2022–2025 period, characterized by geopolitical tensions, post-pandemic inflation, and heightened financial volatility.Design/Methodology/Approach: The study employs a quantitative approach using daily data on Bitcoin, gold, oil, the S&P 500 index, and the Volatility Index (VIX) from January 2022 to June 2025. All variables are transformed into logarithmic returns and analyzed using an ARCH model to capture time-varying volatility and assess the influence of global market factors on Bitcoin returns..Research findings: The empirical results indicate that the VIX has a statistically significant negative effect on Bitcoin returns, implying that rising global uncertainty weakens rather than strengthens Bitcoin’s value. The S&P 500 exerts a significant positive influence, showing that Bitcoin moves pro-cyclically with equity markets and behaves like a risky asset. Oil prices have no significant impact, while gold returns exhibit a significant but unstable co-movement, lacking consistent value preservation. Overall, these findings reject Bitcoin’s safe-haven role and characterize it as a speculative digital asset with high sensitivity to stock market dynamics.Theoretical contribution/Originality: This study contributes to the safe-haven and digital finance literature by providing recent empirical evidence that distinguishes Bitcoin from genuine safe-haven assets. Grounded in formal safe-haven theory and volatility dynamics, it challenges the “digital gold” narrative and clarifies the boundary between high-risk digital assets and traditional safe havens.Practitioner/Policy implication: For investors, the results of this study confirm the need for caution in treating Bitcoin as a portfolio diversification instrument, as its behavior is more like that of a risky asset than a hedge asset. For Policymakers and regulators, these results show the importance of public education regarding Bitcoin's volatility risks and its limitations as a safe haven
Exploring robotic process automation adoption among accounting professionals in South Africa: Application of the UTAUT model
No full textResearch aims: The rapid advancement of robotic process automation (RPA) technologies presents significant transformation opportunities for the accounting profession, yet adoption rates remain inconsistent across different contexts. This study investigates factors influencing RPA adoption among accounting professionals in South Africa, employing the Unified Theory of Acceptance and Use of Technology (UTAUT) framework.Design/Methodology/Approach: Using descriptive and inferential statistics, the study analysed quantitative and qualitative data gathered from 100 accounting and auditing professionals.Research findings: Findings revealed Social Influence as core predictor while skills and training gaps, resistance to change, and resource constraints were notable barriers. A significant awareness-implementation gap was also observed for RPA knowledge versus usage.Theoretical contribution/Originality: This study contribes theoretically by demonstrating that social legitimation may outweigh technical performance in professional settings within emerging markets, a contexts where peer validation and collective professional endorsement are crucial. By theorizing awareness-implementation paradox, it noted that attitude and knowledge are vital yet, insufficient for behavioural change. Additionally, it provides context-sensitive validation of UTAUT constructs from an emerging economy.Practitioner/Policy implication: The findings reinforce technology-centric adoption, with professional services contexts exhibiting unique dynamics. Overall, it highlights prioritizing social factors, management endorsement and peer advocacy as implementation strategies for RPA adoption over technical features. These findings provide evidence-based guidance for organisations and professional bodies seeking to advance RPA adoption within the South African accounting professional context
Deforestation and Customary Forests: A Comparison of Indonesia and the Philippines
Full text linkDeforestation in Indonesia and the Philippines continues to intensify, threatening biodiversity, carbon stocks, and the cultural survival of indigenous peoples. The urgency of addressing this issue is evident in the persistent loss of primary forests, weak protection of customary land rights, and the limited use of transparent monitoring systems in both countries. This study compares the effectiveness of forest governance frameworks in Indonesia and the Philippines by examining the role of indigenous communities and the integration of Earth Observation (EO) technology in deforestation monitoring. Using a normative juridical approach supported by literature review, this research analyzes regulatory policies, legal decisions, and forest management mechanisms. The findings show that Indonesia’s REDD+, CBFM, and licensing reforms are constrained by structural barriers, low community participation, and inadequate recognition of customary forests. Meanwhile, the Philippines demonstrates stronger community-based approaches, including rainforestation and more advanced EO-based monitoring that supports evidence-driven policy making. This study highlights the urgent need for Indonesia to strengthen legal recognition of customary forests, reform its licensing system, and adopt more transparent, technology-based monitoring. The comparative insights offered by this research contribute to developing inclusive, rights-based, and data-supported forest governance in both countrie
Intelligent Detection and Classification of Security Attacks in WSNs Using Deep Learning
Full text linkWireless sensor networks (WSNs) are critical components of modern communication systems but remain vulnerable to complex and diverse cyber threats. This paper proposes a hybrid framework integrating generative adversarial networks (GANs) and recurrent neural networks (RNNs) to address security challenges in WSNs. The RNN module captures temporal dependencies for precise attack detection, while the GAN module generates synthetic samples to mitigate data imbalance. The framework achieves a detection rate of 98.47% and an accuracy of 98.79%, outperforming traditional methods such as support vector machines, naïve bayes classifiers, and random forests by a statistically significant margin (p < 0.05). Moreover, the framework maintains a low false positive rate of 2%, ensuring minimal disruption to legitimate network operations. The adaptability of the framework is validated through case studies on heterogeneous WSN architectures, including low-power IoT sensor networks and large-scale industrial deployments. Specifically, tests on the CICIDS and UNSW-NB15 datasets demonstrate its effectiveness in dynamic environments where attack patterns evolve in real time. The framework’s real-time detection capability is further confirmed by achieving an average inference time of 1.25 seconds per sequence, making it suitable for time-sensitive applications such as military surveillance and industrial automation. To enhance adversarial robustness, the framework integrates adversarial training techniques, gradient regularization, and adversarial perturbation detection mechanisms, ensuring resilience against evasive attack strategies and adversarial samples. Empirical evaluations show that adversarial perturbation detection reduces attack evasion rates by 36.8%, strengthening the reliability of security measures in real-world deployments
A Dual-Model Spatiotemporal Flood Prediction System Using Sentinel-1 SAR Imagery and Meteorological Data: A Case Study in Palembang, Indonesia
Full text linkFloods remain one of the most destructive and frequent natural hazards, especially in urban river basins like Palembang, Indonesia. Improving early warning systems through the integration of radar and meteorological data has become increasingly feasible with advances in remote sensing and machine learning. The research contribution is the development of a dual-model spatiotemporal prediction framework that combines Sentinel-1 SAR imagery and meteorological data using eXtreme Gradient Boosting (XGBoost) for spatial classification and Long Short-Term Memory (LSTM) for temporal forecasting. Radar backscatter values were extracted from Sentinel-1 Single Look Complex (SLC) data and processed into one-kilometer resolution grids using SNAP and QGIS. These were merged with weather variables precipitation, humidity, wind speed, and solar radiation sourced from local meteorological stations. The XGBoost model achieved a precision of 98.3%, accuracy of 99.94%, recall of 97.5%, and F1-score of 97.9%, with SHAP analysis identifying rainfall and wind speed as dominant flood predictors. Spatial predictions aligned closely with historically flood-prone areas along the Musi River. In contrast, the LSTM model, despite forecasting floods up to 12 days in advance with average accuracy of 91.6%, suffered from class imbalance, resulting in a recall of only 22.9% and precision of 36.3%, limiting its applicability for real-time early warning. These findings demonstrate that while spatial classification using radar and weather data is highly effective, temporal forecasting remains challenged by data imbalance and uneven class distribution. Future research should explore cost-sensitive learning, uncertainty quantification, and real-time validation to enhance the system’s operational reliability
Effect of Organic Fertilizer on Growth and NPK Uptake of Dayak Onion Cultivated on Inland Peat Soil
No full textPeatlands have the potential to support sustainable agricultural production when properly managed, particularly through the use of organic inputs. This study aimed to evaluate the effects of organic fertilizer on the growth and nitrogen, phosphorus, and potassium nutrient uptake efficiency of Dayak onion plants cultivated in inland peat soil. The research was conducted from September to December 2023 at the Experimental Garden of the Faculty of Agriculture at the University of Palangka Raya. A randomized block design was used with five levels of cow manure application: 0, 5, 10, 15, and 20 t.ha-1, each replicated four times. The application of cow manure significantly increased leaf number at 50, 60, and 70 days after planting (DAP), as well as the increase in leaf number between 40 and 50 days, root and shoot fresh weight, number of tillers, and fresh bulb weight. The 10 t.ha-1 treatment increased leaf number by 87% at 50 DAP, 74.2% at 60 DAP, and 43.8% at 70 DAP compared to the control treatment. However, it did not significantly affect plant height. The regression equation is y = −0.0081x² + 0.2226x + 2.8726, with R² = 0.99. The dose of cow manure fertilizer that produces maximum tuber fresh weight is approximately 13.75 t ha⁻¹. This dose shows the best growth response before tuber weight decreases at higher fertilization levels. These findings indicate that applying organic fertilizer, particularly at an optimal dosage, can improve the productivity of Dayak onion grown in peat soil, supporting the sustainable use of marginal land for horticultural development