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This study examines the integration of biblical authority into curriculum design and pedagogical practices as a foundation for developing a transformational Christian education model in a global, digital context. The main issue raised is the dichotomy between normative theological approaches and contemporary pedagogical practices that are often secular, leading to a weak integration of faith and learning in Christian education. The purpose of this study is to formulate an integrative model that harmonizes biblical authority with modern pedagogical approaches in a contextual, relevant, and applicable manner. The research method uses a qualitative approach through systematic literature study and conceptual analysis of various recent journals and books in the fields of Christian education, curriculum, and transformational pedagogy. The results show that the integration of biblical authority can be constructed through three main dimensions: epistemological (the Bible as a source of truth), curricular (the integration of biblical narratives into the curriculum structure), and pedagogical (learning practices oriented towards character transformation and spirituality). The research synthesis produces the "Biblical Transformative Curriculum-Pedagogy Integration (BTCPI)" model that emphasizes the relationship between theological truth, character formation, and the practice of faith. In conclusion, the holistic integration of biblical authority and modern pedagogy contributes significantly to developing students who are not only academically competent but also possess contextual spiritual and moral maturity.          

The development of globalization and digital culture has given rise to an epistemological crisis that shifts the authority of truth from revelation to social construction, so that the authority of the Bible in Christian education is increasingly marginalized. This condition has an impact on the low internalization of the value of faith, where students understand the Bible cognitively but do not live it in daily practice. This research aims to reconstruct the authority of the Bible in Christian education through an integrative and contextual approach in the midst of global cultural fragmentation. This research uses a qualitative approach with a library research method that integrates theological, pedagogical, and digital cultural contexts. Data were obtained from theological literature, educational theory, and relevant journal articles, which were analyzed using content analysis techniques and theological-constructive reflection. The results show that the crisis of biblical authority is influenced by epistemic shifts, the dominance of post-truth culture, low internalization of values, and non-contextual pedagogical approaches. This research creates a model of reconstruction of biblical authority that integrates the epistemological, pedagogical, and bruxistic dimensions in a transformational manner. In conclusion, this reconstruction does not change the theological substance of the Bible but updates the approach to learning to be more dialogical, reflective, and contextual, so that the authority of the Bible remains relevant in shaping the faith and character of learners in the global era.  

This research aims to analyze the integration of biblical authority in the construction of the theological education curriculum through a theological and pedagogical approach. The authority of the Bible as the normative and final word of God has a central role in the formation of doctrine, ethics, and practices of church life, so it should be the main foundation in the development of the theological education curriculum. In practice, however, there is a tendency that the construction of the curriculum is more influenced by modern academic and pedagogical approaches without adequate integration into biblical theological foundations. This study uses a qualitative method with a literature study approach that analyzes various sources of systematic theology and Christian education theory. The results of the study show that the integration of biblical authority in the theological education curriculum can be carried out through three main aspects, namely: (1) the placement of the Bible as a normative source in the entire curriculum structure, (2) the integration of theological values in learning design, and (3) the development of pedagogical models oriented towards spiritual formation and character. The findings of this study confirm that the construction of a theological education curriculum rooted in biblical authority not only results in academic competence, but also shapes spirituality, integrity of faith, and readiness for ministry. Thus, this research makes a conceptual contribution in developing an integrative theological education curriculum model between theological and pedagogical dimensions.    

 Cryptocurrency price prediction, particularly for highly volatile assets like Solana (SOL), is a crucial challenge in time series data analysis in digital finance. This study aims to compare the performance of the XGBoost machine learning algorithm with the Temporal Fusion Transformer (TFT) deep learning model in predicting Solana's daily closing price. The dataset used consists of historical Solana price data and network fundamentals data in the form of Total Value Locked (TVL). The research process includes data preprocessing, dividing training and test data, model training, and evaluation using the Root Mean Squared Error (RMSE) metric. The results show that using the same-day price feature has the potential to cause target leakage, resulting in invalid prediction accuracy. In testing using pure historical data without data leakage, the XGBoost model performed better than TFT with an RMSE of 4.27, while TFT produced an RMSE of 18.59. Furthermore, the integration of network fundamentals data in the form of TVL did not improve prediction accuracy and even caused a decrease in performance for the XGBoost model with an RMSE of 7.10. The results of this study show that the use of historical price action features is more effective than fundamental network indicators for short-term daily Solana price predictions.

The rapid proliferation of learning analytics, business intelligence (BI), artificial intelligence (AI), and generative AI (GenAI) has significantly expanded universities’ ability to collect, integrate, analyze, and operationalize institutional data. However, despite advances in predictive analytics, dashboards, and AI-driven systems, the translation of analytical outputs into consistent and accountable institutional decision-making remains uneven. This systematic literature review synthesizes contemporary research on analytics-enabled decision-making in higher education with the aim of moving beyond dashboard-centric perspectives toward a socio-technical and computing-oriented understanding of how data are transformed into institutional actions and outcomes. Guided by the PRISMA framework, the review synthesizes evidence across four interconnected dimensions: data ecosystems and learning analytics foundations; analytics capability, BI adoption, and digital readiness; AI and advanced analytics for decision support; and human-in-the-loop (HITL) decision routines and institutional outcomes. The findings show that predictive performance and analytical sophistication alone do not guarantee decision value. Instead, effective analytics-enabled decision-making depends on interoperable data ecosystems, organizational analytics capability, governance mechanisms, explainability, and sustained human oversight. Based on these findings, this review contributes a computing-oriented decision-intelligence framework that conceptualizes analytics-enabled decision-making as an end-to-end socio-technical pipeline linking heterogeneous data acquisition, integration, feature construction, analytical modeling, explainability, human validation, governance, and feedback-based refinement. By integrating learning analytics, BI, AI, GenAI, and HITL mechanisms within a unified framework, the review clarifies how universities can move beyond dashboard-based reporting toward accountable, adaptive, and institutionally actionable decision-support infrastructures.

This study examines the role of Artificial Intelligence (AI) in enhancing student collaborative learning, with a particular emphasis on AI-driven feedback mechanisms and patterns of student interaction in developing effective collaborative skills. Unlike prior studies, this research highlights the mediating effect of AI-driven feedback on teamwork efficiency and overall learning outcomes in collaborative environments. An explanatory quantitative approach was applied using Partial Least Squares Structural Equation Modeling (PLS-SEM) to ensure robust data analysis. Data were collected from 112 university students who were actively engaged in AI-assisted collaborative learning activities, using a structured online survey instrument. The data were subsequently analyzed using SmartPLS software. The results reveal that AI significantly enhances student interaction (β = 0.534, p < 0.000) and improves problem-solving feedback (β = 0.620, p < 0.000), both of which contribute to significantly strengthening collaborative skills (β = 0.716, p < 0.000). However, the findings also indicate that AI alone does not directly improve collaboration without the support of structured pedagogical design and guidance. Therefore, universities should strategically integrate AI-driven feedback into Learning Management Systems (LMS) and strengthen digital literacy initiatives to optimize the effectiveness and sustainability of AI in collaborative learning contexts.

Mental health issues among university students have become a growing concern, driven by academic pressures, career uncertainties, and complex social transitions. However, a large proportion of students remains reluctant to seek professional psychological support due to social stigma and limited access to institutional counseling services. This study proposes the design and implementation of an Android-based chatbot application that integrates mood tracking and sentiment analysis to continuously monitor the emotional states of university students. The system employs a fine-tuned RoBERTa (Robustly Optimized BERT Pretraining Approach) model trained on the EmoContext conversational dataset (SemEval-2019 Task 3), comprising 30,160 labeled three-turn dialogue instances across four emotion classes: angry, happy, sad, and others. The model was fine-tuned for four epochs using the AdamW optimizer with a learning rate of 2e-5 and a maximum sequence length of 128 tokens. Evaluation on a held-out validation set of 6,032 samples yielded an overall accuracy of 88.28%, a macro-average F1-score of 0.87, and a weighted-average F1-score of 0.88. Per-class F1-scores were 0.89 (angry), 0.83 (happy), 0.91 (others), and 0.86 (sad). The classified emotion is transmitted in real time to the chatbot response logic, which generates empathetic replies and personalized relaxation recommendations based on the detected mood. Primary data collection through questionnaires and interviews with 62 and 19 university students respectively confirmed the need for accessible digital mental health support. The results demonstrate that RoBERTa-based fine-tuning on conversational data provides a reliable foundation for real-time emotion-aware mental health chatbot systems.

Suspicious urban sound recognition is a critical component of intelligent public safety and urban monitoring systems, enabling the automated identification of anomalous acoustic events such as gunshots, sirens, and other security-sensitive sounds. However, existing deep learning approaches often struggle to simultaneously capture long-range temporal dependencies and global contextual relationships, particularly under noisy and acoustically complex urban conditions. This limitation can reduce reliability in safety-critical scenarios where missed detections carry significant risk. To address these challenges, this study proposes a Multi-Branch Bidirectional Long Short-Term Memory (BiLSTM) framework with Multi-Head Self-Attention (MHSA) for enhanced sequential and contextual feature modeling. Mel-frequency cepstral coefficients (MFCCs) are extracted from a curated subset of the UrbanSound8K dataset, comprising five suspicious sound classes, and used as input to the proposed architecture. The multi-branch design enables complementary temporal representations, while the self-attention mechanism provides lightweight contextual weighting of BiLSTM outputs. Experimental results demonstrate that the proposed model achieves a test accuracy of 95.59%, outperforming conventional Dense and LSTM-based baseline models under identical experimental settings. An ablation study further confirms the contribution of multi-branch integration and attention-based enhancement to overall performance. Class-wise evaluation reveals consistently high recall across all sound categories, particularly for safety-critical classes such as gunshots and sirens. These findings indicate that the proposed framework provides robust and reliable performance, making it suitable for real-time smart city surveillance and public safety applications.

 This research addresses the growing tendency of contemporary education to prioritize cognitive achievement while marginalizing character formation and spiritual development, resulting in an imbalanced educational paradigm that affects the holistic growth of learners in various educational contexts. In response, the research critically examines the concept and principles of Christian education as articulated by Ignatius Loyola, particularly in relation to contemporary educational challenges and the need for meaningful transformation in learning processes today. The study employs a qualitative approach using a literature review method, drawing on relevant academic sources such as books, peer-reviewed journal articles, and theological writings to support systematic and critical analysis. The findings indicate that Ignatian educational thought emphasizes holistic human formation through the integration of intellectual, moral, and spiritual dimensions, grounded in the principles of Ad Majorem Dei Gloriam, Cura Personalis, and Find God in All Things, which together foster reflective, value-oriented learning. The study contributes to contemporary educational discourse by offering an alternative framework that promotes holistic development, strengthens character formation, and addresses the limitations of cognitively oriented educational models in diverse educational practices.

The global ecological crisis is not only a scientific issue but also reflects the decline of human moral and spiritual awareness toward nature. This study aims to examine the acculturation approach of Islam Nusantara in Islamic Religious Education (PAI) learning as an effort to foster students’ ecological awareness. The research employed a qualitative library research method using various relevant sources, including books, national and international journal articles, and educational policy documents. Data analysis was conducted through data reduction, thematic presentation, and interpretive-analytical conclusion drawing. The findings indicate that Islam Nusantara, as a product of the acculturation between universal Islamic values and local Nusantara wisdom, contains various ecological traditions such as sasi, subak, and hutan larangan (forbidden forests), which can be contextually integrated into PAI learning. The developed learning model includes the use of local wisdom as learning resources, participatory project-based learning methods, and thematic Qur’anic interpretation related to environmental issues. The ecotheological approach of Islam Nusantara is considered effective in connecting religious teachings with students’ ecological realities, thereby fostering a religious-ecological character that views environmental preservation as part of worship. Although challenges remain in theological, pedagogical, and institutional aspects, this approach contributes strategically to shaping environmentally responsible Muslim generations.

This study aims to analyze the strategies employed by madrasah principals to enhance teacher professionalism through the implementation of managerial functions, academic supervision, entrepreneurship, and socio-spiritual leadership at MAN 1 Bandung and MAN 2 Bandung. The study employs a qualitative approach using a descriptive case study method. Data collection techniques included in-depth interviews, participatory observation, and document analysis, while data analysis was conducted using the Miles and Huberman model through the stages of data reduction, data presentation, and drawing conclusions. The research findings indicate that strategies for enhancing teacher professionalism begin with needs-based planning, followed by strengthening clinical academic supervision, fostering a collaborative and innovative work culture, and implementing continuous evaluation. The implementation of these strategies has a positive impact on improving teachers’ pedagogical and professional competencies, work discipline, and the quality of learning. The novelty of this research lies in the discovery of the “Resilient Professionalism” model a model of teacher professionalism that emphasizes resilience and adaptability to changes in curriculum, technology, and educational dynamics. This model is built through the synergy of the spiritual-managerial leadership of the madrasah principal and the collective efficacy of teachers to achieve sustainable quality in Islamic education.

Strengthening scientific literacy in elementary science learning requires interactive and contextual instructional strategies. Game-based learning has been shown to enhance student motivation and engagement (Clark et al., 2016). This study aims to develop and examine the effectiveness of an interactive quiz-based educational game on the topic of food chains and ecosystems for fifth-grade students. The research employed a Research and Development approach using the ADDIE model, consisting of analysis, design, development, implementation, and evaluation stages (Yusuf & Rahman, 2023). The participants were 25 students. Data were collected through pretest and posttest instruments and analyzed using the N-Gain formula to measure learning improvement. The findings indicate a moderate to high increase in students’ learning outcomes after the implementation of the developed media. In addition, students demonstrated greater participation during the learning process. These results suggest that interactive quiz-based educational games can serve as an effective alternative strategy to enhance conceptual understanding in elementary science education.

Communication is an essential human right, yet a significant communication gap persists between individuals with sensory disabilities, specifically the deaf and speech-impaired, and the general public. While many technological solutions have been proposed to translate sign language, existing models primarily rely on heavy deep learning architectures such as Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN/LSTM). These models often demand high computational power, leading to latency and limiting real-time application on standard devices. This study proposes a lightweight, fast, and highly responsive sign language translation system specifically designed to recognize static alphabets (A-Z) and single-character air writing. The system utilizes MediaPipe for hand tracking, where feature extraction is intelligently processed by calculating the relative spatial coordinates of fingertips to the wrist, reducing dependency on raw camera coordinates. Classification is performed using a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel, prioritizing computational efficiency without sacrificing accuracy. To enhance user experience, the system introduces three key novelties: smart relative feature extraction, an anti-duplication hold system with a 1-second timer to prevent input spamming, and a non-blocking multithreaded audio execution (Daemon Thread) utilizing Google Text-to-Speech (gTTS), ensuring the webcam feed remains fluid during audio playback. Additionally, an alternative air-writing mode is integrated, utilizing geometric heuristics and PyTesseract OCR to read single drawn letters in the air. The results indicate that the proposed system operates swiftly and efficiently, bridging the communication barrier with a hardware-friendly approach.

Sentiment analysis for Indonesian regional languages faces two persistent challenges: labeled training data is extremely limited for most regional varieties, and transformer models pre-trained on Bahasa Indonesia do not generalize reliably to languages with substantially different morphological structures. Prior work on the NusaX benchmark has primarily relied on direct fine-tuning, treating each regional language independently and without exploiting linguistic proximity between related languages as a transfer signal. This paper proposes Language-Similarity-Guided Transfer (LSGT), a sequential fine-tuning strategy that first adapts a pre-trained model to a pivot language selected using character trigram similarity, followed by fine-tuning on the target language. Four transformer models are evaluated across all 12 NusaX languages using the official train/validation/test splits: IndoBERT, NusaBERT, mBERT, and XLM-R. Performance is evaluated using four metrics: accuracy, macro F1, macro precision, and macro recall. Experimental results show that LSGT improves macro F1 in 44 of 48 model-language combinations, demonstrating that the fine-tuning strategy itself is a major factor in low-resource cross-lingual sentiment classification. XLM-R benefits most strongly from LSGT, achieving an average improvement of +0.137 macro F1 and a peak gain of +0.298 on Madurese. SHAP-based token attribution analysis further reveals that predictions rely heavily on named entities and domain-specific nouns rather than sentiment-bearing vocabulary, indicating a dataset-level bias inherited from the original SmSA corpus and propagated through the NusaX translation pipeline.

A mid the growing complexity of contemporary education, the challenge of integrating intellectual achievement with moral and spiritual development has become increasingly urgent. This study examines educational strategies in the Qur’an through the perspective of tafsir tarbawi, focusing on four core approaches: exemplary conduct, advice, dialogue, and storytelling. The research aims to explore how the Qur’an presents pedagogical methods that not only convey knowledge but also shape character and internalize values in learners. Employing a qualitative library research design with thematic and tafsir tarbawi approaches, this study analyzes relevant Qur’anic verses and supporting scholarly literature. The findings reveal that the Qur’an offers a holistic and dynamic model of education in which exemplary conduct functions as a behavioral model, advice serves as a means of value internalization, dialogue promotes reflective and critical thinking, and storytelling strengthens moral awareness through narrative experience. These strategies are interconnected and operate across cognitive, affective, and behavioral domains. The study concludes that Qur’anic educational strategies remain highly relevant for contemporary Islamic education, particularly in strengthening character formation, reflective learning, and the integration of values in educational practice.

Social Studies (IPS) learning in elementary schools plays an essential role in developing students’ critical thinking skills and civic attitudes as part of 21st-century competencies. However, in practice, IPS learning is still commonly teacher-centered and lacks contextual learning experiences, resulting in low student engagement and limited development of higher-order thinking skills. This study aims to analyze the implementation of Project-Based Learning (PjBL) in IPS learning to enhance critical thinking skills and civic attitudes of grade VI students at SDN Prekbun. The research employed a qualitative approach with a case study design. Data were collected through classroom observations, semi-structured interviews with teachers, students, school principals, and parents, as well as documentation of learning activities. Data analysis was conducted using the interactive model of Miles and Huberman, including data reduction, data display, and conclusion drawing and verification. The findings indicate that the implementation of PjBL effectively enhances students’ active participation, critical thinking skills in analyzing social issues, and civic attitudes such as responsibility, cooperation, and social awareness. Learning becomes more meaningful as students are directly involved in contextual, collaborative, and problem-based project activities. Despite these positive outcomes, several challenges were identified, including limited learning facilities, time management constraints, and variations in students’ confidence levels. This study implies that PjBL is an effective instructional model for strengthening character education and developing 21st-century skills in elementary social studies learning

This study addresses the issue of suboptimal student academic performance, which is frequently linked to the selection of instructional strategies that do not actively engage learners. Learning outcomes represent a multidimensional construct involving knowledge acquisition, attitude development, and skill formation. Therefore, the application of an appropriate learning model is essential to support meaningful learning experiences. This research aims to investigate the impact of implementing a Problem Based Learning (PBL) model supported by audio-visual media on students’ academic achievement. A quantitative approach with a quasi-experimental design was employed. The study involved two groups: an experimental class receiving the PBL intervention and a control class experiencing conventional instruction. Data were collected through pretest and posttest assessments and analyzed using both descriptive and inferential statistical techniques. The findings reveal a statistically significant difference between the two groups, indicated by a significance value below 0.05. This confirms that integrating problem-based strategies with audio-visual support contributes positively to improving students’ learning outcomes. The results highlight the importance of student-centered learning environments in fostering deeper understanding and engagement.

This study aims to develop a Strategic Learning Enabler model by reconfiguring the role of Information and Communication Technology (ICT) units to enhance Generation Z student productivity in digital higher education. The research is motivated by the gap between digital transformation demands and the predominantly technical-operational role of ICT units. Using a quantitative explanatory design, this study examines the causal relationships among ICT role reconfiguration, adaptive learning systems, and student productivity. Data were collected through questionnaires distributed to Generation Z students engaged in digital learning environments and analyzed using Structural Equation Modeling (SEM). The findings indicate that Generation Z students exhibit high responsiveness to structured tasks but lack continuity in learning activities due to weak self-regulated learning. The study also reveals that adaptive learning systems significantly mediate the relationship between ICT transformation and student productivity. The proposed model emphasizes digital governance, learning analytics, and micro-tasking structures as key components in building sustainable academic productivity. This research contributes both theoretically and practically by offering an institutional framework for digital learning transformation in higher education.

This study aims to analyze the integration of policy, curriculum, and school culture in realizing an environmentally friendly school at SDN Tanggul Wetan 2. The study used a qualitative approach with a case study type. The research subjects included the principal, teachers, students, program coordinators, and parents. Data collection techniques were carried out through observation, in-depth interviews, and documentation. Data analysis used the Miles and Huberman model which includes data reduction, data presentation, and drawing conclusions. The results showed that the school policy has integrated environmental care values ​​through the Adiwiyata program supported by external partnerships and periodic evaluations. The curriculum implementation was carried out contextually by integrating environmental materials into various subjects and using project-based learning methods. School culture is realized through habituation of activities such as community service, waste sorting, and energy saving which have an impact on changes in student behavior. Supporting factors include the principal's leadership, teacher participation, parental support, and the school environment, while inhibiting factors include the lack of consistent student habits. Thus, the integration of policy, curriculum, and school culture has proven to be key in building a sustainable environmentally friendly character.

The Qiroati method is an instructional approach to reading the Qur’an that emphasizes direct practice of tartil recitation in accordance with the rules of tajwid. This method is practical, systematic, and implemented gradually, meaning that learners are not allowed to proceed to the next level before achieving fluency at the current stage. One of the main challenges in learning to read and memorize the Qur’an is the limited use of structured teaching methods, as some teachers still rely primarily on personal experience and conventional reading approaches. This condition contributes to students’ lack of fluency in reading and memorizing the Qur’an, as well as decreased learning motivation due to monotonous and less effective instructional practices. This study aims to examine the implementation of the Qiroati method in improving students’ Qur’anic reading ability within the Tahfidzul Qur’an program at SMP Daruttaqwa Full Day School. The findings are expected to provide useful insights for educators and contribute to academic knowledge. This research employs a field research design with a descriptive qualitative approach, focusing on describing the application of the Qiroati method in enhancing students’ reading ability in relation to Qur’an memorization. Data were collected through interviews, tests, and documentation, while data analysis involved data collection, data display, and conclusion drawing. The results indicate that the implementation of the Qiroati method effectively improves students’ ability to read the Qur’an in the Tahfidzul Qur’an program. This is evidenced by the improvement in students’ reading scores compared to their previous performance. In practice, the method involves teachers modeling correct recitation, followed by students reading collectively. Therefore, the Qiroati method not only enhances students’ accuracy in reading the Qur’an but also fosters a more engaging and effective learning environment.