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Khoirunnisa Imama
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Interdisciplinary Journal of Pedagogy and Research in Media Technology
Published by CV. SPDF HARMONY
ISSN : -     EISSN : 31235689     DOI : https://doi.org/10.64268/inspire.v1i2.67
Core Subject : Humanities, Science, Education, Social,
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Social Sciences Other
Focus Interdisciplinary Journal of Pedagogy and Research in Media Technology (INSPIRE) emphasizes interdisciplinary studies that examine pedagogy, media, technology, and analytical innovation in relation to decision-making processes, judgment, and the development of evidence-based practices across diverse organizational and social contexts. INSPIRE publishes research articles and review papers that contribute to scholarly advancement through interdisciplinary approaches. Scope The journal specializes in investigating the theoretical and practical aspects of pedagogy, media, technology, and decision analysis across various organizational and social contexts from diverse disciplinary perspectives. INSPIRE welcomes contributions addressing innovation, research, and development in the following general areas: • Pedagogy • Media Studies • Media Technology • Digital Media and Communication • Decision-Making and Judgment • Decision Sciences • Data Analytics and Evidence-Based Decision • Human–Computer Interaction • Artificial Intelligence and Media Applications • Information Systems and Knowledge Management • Digital Innovation and Creative Industries • Media, Technology, and Decision-Making in Organizational and Social Contexts • Interdisciplinary Studies in Pedagogy, Media, Technology, and Decision Sciences • Social Sciences (Miscellaneous)
Arjuna Subject : Umum - Umum
Articles 15 Documents
 1   2 
The impact of whatsapp marketing on brand loyalty and sales conversion: Roles of customer engagement, chatbots, and real-time communication Surjadeep Dutta; Arunava Mookherjee; Tisha Biswas; Anindita Sinha
Interdisiplinary Journal of Pedagogy and Research in Media Technology Vol. 2 No. 1 (2026): Interdisciplinary Journal of Pedagogy and Research in Media Technology
Publisher : CV. SPDFHarmony

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64268/inspire.v2i1.109

Abstract

Background: The worldwide expansion of WhatsApp into a global communication platform has created new digital marketing opportunities which enable businesses to establish direct contact with customers through a real-time interactive communication method. WhatsApp delivers better message open rates than standard email marketing methods which makes it suitable for personalized interactive communication especially in developing countries such as India.Aims: The research aims to evaluate how WhatsApp marketing strategies impact customer engagement results. The study investigates how customer engagement connects to brand loyalty within the framework of WhatsApp marketing.Methods: The study used conceptual framework to find out how successful WhatsApp Marketing Strategies are in improving customer engagement and increasing business performance. The secondary Data has been collected from SCOPUS, EBSCO, PROQUEST, IGI Global, IEEE Databases and Journals. Most of the references has been taken from the Year 2018-2025.Result: The research results demonstrate that WhatsApp marketing techniques produce better customer engagement results, which serve as the main connection that affects business performance. The system provides real-time communication together with multimedia content and Chatbot automation to improve responsiveness while delivering personalized experiences, which results in more customer interactions and better response rates to call-to-action messages. The use of automation tools enables businesses to achieve better operational results and enhanced customer satisfaction.Conclusion:  The research demonstrates that WhatsApp marketing tools need to be used strategically because their Chatbot automation and interactive messaging features help businesses to boost customer interaction and buying likelihood. The first requirement for businesses to achieve lasting success with WhatsApp marketing needs them to build customer trust through effective privacy protection and data security measures.
Early-warning analytics with LLM intervention rationales for student retention decisions: Classroom interaction modeling with xAPI-edu-data and dropout/success prediction Qi Xin
Interdisiplinary Journal of Pedagogy and Research in Media Technology Vol. 2 No. 1 (2026): Interdisciplinary Journal of Pedagogy and Research in Media Technology
Publisher : CV. SPDFHarmony

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64268/inspire.v2i1.117

Abstract

Background: Student-retention early-warning systems have substantially improved predictive performance, yet their outputs often remain limited to risk scores that provide little guidance for educational intervention.Aims: This study proposes and empirically evaluates a reproducible two-stage framework that integrates classroom interaction modeling, institutional retention prediction, and structured LLM-ready intervention rationales for student-retention decision support.Methods: The framework was evaluated using two complementary benchmark datasets. xAPI-Edu-Data was used for classroom interaction modeling, whereas Predict Students' Dropout and Academic Success was used for institutional retention prediction. The datasets were analyzed independently rather than merged. Stratified train-test splits, ensemble machine learning models, feature ablation, feature-importance analysis, probability calibration, and rationale-quality evaluation were employed.Result: LightGBM achieved the highest Macro-F1 (0.7775) on the xAPI-Edu-Data benchmark, while XGBoost produced the best overall performance on the institutional retention dataset, achieving a multiclass accuracy of 0.7672, a Macro-F1 of 0.6964, and a ROC-AUC of 0.8889. In binary dropout prediction, XGBoost achieved a ROC-AUC of 0.9360 and an Average Precision of 0.9101. Behavioral engagement, attendance, academic progression, and tuition-related variables consistently emerged as the most informative predictors. The structured rationale layer achieved complete evidence alignment, actionability, and monitoring specificity while generating 392 unique intervention rationales.Conclusion:  The proposed framework demonstrates that early-warning analytics can move beyond risk prediction by integrating predictive analytics, explainable AI, and structured intervention rationale generation into a transparent, evidence-grounded decision-support workflow for improving student-retention decisions in higher education.
An integrated deep CNN–LSTM framework for disaster management through reliable information retrieval and educational awareness Zair Bouzidi
Interdisiplinary Journal of Pedagogy and Research in Media Technology Vol. 2 No. 1 (2026): Interdisciplinary Journal of Pedagogy and Research in Media Technology
Publisher : CV. SPDFHarmony

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64268/inspire.v2i1.116

Abstract

Background: Social media has become one of the primary sources of disaster-related information; however, the rapid dissemination of heterogeneous, duplicated, and misleading content presents significant challenges for retrieving reliable information that can support disaster management and educational awareness. Conventional machine learning approaches often struggle to simultaneously capture semantic representations and contextual dependencies within large-scale disaster communications.Aims: This study aims to develop and evaluate an integrated Deep CNN–LSTM-based disaster management framework for retrieving reliable disaster-related information from heterogeneous online sources while supporting educational awareness through safe information dissemination.Methods: A quantitative experimental design was employed using disaster-related textual datasets collected from multiple online platforms, including earthquake, flood, wildfire, and COVID-19 events. The proposed framework integrated Deep Convolutional Neural Networks (Deep CNN) for semantic feature extraction with Long Short-Term Memory (LSTM) networks for sequential contextual learning. Model performance was compared with six benchmark machine learning and deep learning approaches, namely Support Vector Machine (SVM), Neural Network (NN), Feed-forward Neural Network (FFNN), Recurrent Neural Network (RNN), LSTM, and Hybrid CNN–LSTM. The framework was evaluated using retrieval performance together with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²).Result: The proposed framework consistently retrieved a greater amount of relevant disaster-related information across multiple disaster scenarios than the benchmark models. Quantitative evaluation further demonstrated robust predictive capability, achieving an RMSE of 15,286.2154, an MAE of 13,789.75, and the highest coefficient of determination (R² = 0.9793), indicating strong model fitting and reliable predictive performance.Conclusion:  The integrated Deep CNN–LSTM framework effectively combines semantic feature extraction and contextual sequence learning to improve disaster information retrieval from heterogeneous online sources. The proposed framework contributes to intelligent disaster management by providing reliable disaster-related information that supports educational awareness and evidence-based decision-making during disaster preparedness and emergency response.
Character building in the digital era: Strategies of aqidah akhlak teachers in an Indonesian islamic junior high school Dila Novi Fitria; Gani Gani; Dewi Sela Eka Selvia; Abrar Hammadi Huwaimid Al-Harbi
Interdisiplinary Journal of Pedagogy and Research in Media Technology Vol. 2 No. 1 (2026): Interdisciplinary Journal of Pedagogy and Research in Media Technology
Publisher : CV. SPDFHarmony

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64268/inspire.v2i1.131

Abstract

Background: The rapid expansion of digital technology has reshaped students’ learning environments and created new challenges for character education in Islamic schools. Social media, internet platforms, and digital communication increasingly influence students’ moral behaviour, requiring Aqidah Akhlak teachers to adopt strategies that keep Islamic values relevant in contemporary digital contexts.Aims: This study examines the pedagogical strategies used by Aqidah Akhlak teachers to foster students’ character, identifies the challenges encountered, and explores adaptive responses implemented in an Indonesian Islamic junior high school.Methods: A qualitative case study was conducted at MTs Darul Huda Sukabumi, Bandar Lampung. Participants were selected purposively and included an Aqidah Akhlak teacher, the headmaster, and seventh-grade students. Data were collected through semi-structured interviews, classroom observations, and document analysis, then analyzed using Miles, Huberman, and Saldaña’s interactive model.Results: Three themes emerged. First, character development was implemented through exemplary practice, habituation, contextualized moral instruction, and school–family partnerships. Second, implementation was challenged by digital influences, limited supervision outside school, and differing levels of family support. Third, teachers responded through persuasive moral guidance, continuous reinforcement, and collaborative monitoring involving parents and the madrasah. These findings show that character education becomes more effective when Islamic values are connected to students’ everyday digital experiences.Conclusion: Character education in Islamic junior high schools should extend beyond conventional religious instruction by integrating exemplary practice, contextual learning, sustained moral reinforcement, and collaboration among teachers, families, and schools.
Mapping self-regulated learning research in mathematics education for quality education: A bibliometric analysis (2016–2025) Nur Fadillah; Kidung Valen Nastiti; Naysa Nafisa Putri; Meiliza Salsabila Putri; Adji W.S. Minadja
Interdisiplinary Journal of Pedagogy and Research in Media Technology Vol. 2 No. 1 (2026): Interdisciplinary Journal of Pedagogy and Research in Media Technology
Publisher : CV. SPDFHarmony

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64268/inspire.v2i1.132

Abstract

Background: Although Self-Regulated Learning (SRL) has been extensively studied in mathematics education, existing studies primarily report empirical findings, while the intellectual structure, thematic evolution, and emerging research directions of the field remain insufficiently synthesized. This limitation hinders a comprehensive understanding of research development and future priorities.Aims: This study aims to analyze trends in publications and citations, the most influential authors and journals, co-occurrence patterns of keywords, temporal trends in research topics, and the density of research topics related to SRL in mathematics education to support Quality Education.Methods: A quantitative bibliometric approach was employed using the Dimensions database. Publications published between 2016 and 2025 were retrieved using the keywords "Self-Regulated Learning" and "Mathematics Education." Of the 181 records identified, 91 journal articles met the predefined inclusion criteria and were analyzed using VOSviewer (version 1.6.20). Performance analysis was conducted to examine publication and citation trends, while science mapping using co-occurrence, overlay, and density visualizations identified the intellectual structure and thematic development of the field.Result: The results indicate that publications and citations related to SRL in mathematics education have increased during the 2016–2025 period. Furthermore, Tatang Herman and Dadang Juandi were the authors with the highest number of publications three each while the Journal of Physics: Conference Series was the journal with the highest number of publications, totaling six. Co-occurrence analysis identified three interconnected research clusters dominated by motivation, achievement, self-efficacy, engagement, feedback, and learning environment. Overlay visualization revealed a shift from studies emphasizing individual learner characteristics toward technology-supported learning, instructional frameworks, and learning environments. Density visualization showed that achievement, engagement, feedback, and quantitative empirical approaches remain the most intensively studied themes.Conclusion: This study advances current understanding by identifying emerging themes and research gaps in SRL within mathematics education. The findings provide evidence-based directions for future research integrating pedagogical, psychological, and technological perspectives, while informing the design of innovative mathematics learning.

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