Abdullah, M. (2019). School Culture to Serve Performance of Madrasah in Indonesia. QIJIS (Qudus International Journal of Islamic Studies), 7(1), Article 1. https://doi.org/10.21043/qijis.v7i1.4809
Al Naqbi, S. H. (2024). A Mixed-Method Approach to Post-Implementation Success of Technology Performance in UAE Universities: Assessing DeLone and McLean IS Success Model. SAGE Open, 14(2), 21582440241240827. https://doi.org/10.1177/21582440241240827
Alfalah, A. A. (2023). Factors influencing students’ adoption and use of mobile learning management systems (m-LMSs): A quantitative study of Saudi Arabia. International Journal of Information Management Data Insights, 3(1), 100143. https://doi.org/10.1016/j.jjimei.2022.100143
Almaiah, M. A., Hajjej, F., Shishakly, R., Lutfi, A., Amin, A., & Awad, A. B. (2022). The Role of Quality Measurements in Enhancing the Usability of Mobile Learning Applications during COVID-19. Electronics, 11(13), 1951. https://doi.org/10.3390/electronics11131951
Almulla, M. A. (2024). Investigating influencing factors of learning satisfaction in AI ChatGPT for research: University students perspective. Heliyon, 10(11), e32220. https://doi.org/10.1016/j.heliyon.2024.e32220
Alotaibi, R. S., & Alshahrani, S. M. (2022). An extended DeLone and McLean’s model to determine the success factors of e-learning platform. PeerJ Computer Science, 8, e876. https://doi.org/10.7717/peerj-cs.876
Alyousefi, N., Alnojaidi, J., Almohsen, A., Alghanoum, S., Alassiry, G., Alsanad, L., & Alzeer, S. (2023). How Do Medical Students Perceive Their Research Experiences and Associated Challenges? Advances in Medical Education and Practice, 14, 9–20. Scopus. https://doi.org/10.2147/AMEP.S395235
Antipova, T., & Rocha, A. (Eds.). (2019). Digital Science (Vol. 850). Springer International Publishing. https://doi.org/10.1007/978-3-030-02351-5
Aqillah, M. A., Wahyuningsih, S., & Aninnas, A. (2024). Da’wah Ecology in Digital Space: A Study of Tiktok Content Pandawara Group Account. TATHO: International Journal of Islamic Thought and Sciences, 27–38. https://doi.org/10.70512/tatho.v1i1.5
Arim, S. N., Ajmain, M. T., Abdul Razak, K., Mohamad Salleh, M. N., Yusof, A. S., & Mohd Noor, S. S. (2024). Navigating Educational Turbulence: A Systematic Literature Review on Challenges Faced by Islamic Education Amid the Pandemic. In Studies in Systems, Decision and Control (Vol. 537, pp. 663–680). Scopus. https://doi.org/10.1007/978-3-031-62106-2_50
Baharun, H., Najiburrahman, N., Zamroni, Z., Mundiri, A., & Maulida Thohir, P. F. D. (2025). Quality of Service and Customer Satisfaction from ROI in Pesantren: A BPS-Mediated Study. TEM Journal, 1260–1268. https://doi.org/10.18421/TEM142-27
Baig, M. I., & Yadegaridehkordi, E. (2025). Factors influencing academic staff satisfaction and continuous usage of generative artificial intelligence (GenAI) in higher education. International Journal of Educational Technology in Higher Education, 22(1), 5. https://doi.org/10.1186/s41239-025-00506-4
Blau, I., Goldberg, S., Friedman, A., & Eshet-Alkalai, Y. (2021). Violation of digital and analog academic integrity through the eyes of faculty members and students: Do institutional role and technology change ethical perspectives? Journal of Computing in Higher Education, 33(1), 157–187. https://doi.org/10.1007/s12528-020-09260-0
Buabeng-Andoh, C. (2021). Exploring University students’ intention to use mobile learning: A research model approach. Education and Information Technologies, 26(1), 241–256. https://doi.org/10.1007/s10639-020-10267-4
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. Scopus. https://doi.org/10.1007/BF02310555
Davis, J. E. (2017). Identity and social change (p. 213). Scopus. https://doi.org/10.4324/9780203789339
Davison, R. M., Chughtai, H., Nielsen, P., Marabelli, M., Iannacci, F., van Offenbeek, M., Tarafdar, M., Trenz, M., Techatassanasoontorn, A. A., Díaz Andrade, A., & Panteli, N. (2024). The ethics of using generative AI for qualitative data analysis. Information Systems Journal, 34(5), 1433–1439. https://doi.org/10.1111/isj.12504
Deng, P., Chen, B., & Wang, L. (2023). Predicting students’ continued intention to use E-learning platform for college English study: The mediating effect of E-satisfaction and habit. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1182980
Ding, S., Wu, D., Zhao, L., & Li, X. (2022). Data Utilization and Governance in Smart Healthcare. In S. Ding, D. Wu, L. Zhao, & X. Li (Eds.), Smart Healthcare Engineering Management and Risk Analytics (pp. 57–67). Springer Nature. https://doi.org/10.1007/978-981-19-2560-3_3
Estriegana, R., Teixeira, A. M., Robina-Ramirez, R., Medina-Merodio, J.-A., & Otón, S. (2024). Impact of communication and relationships on student satisfaction and acceptance of self- and peer-assessment. Education and Information Technologies, 29(12), 14715–14731. https://doi.org/10.1007/s10639-023-12276-5
Faizah, R., & Ramadhan, F. (2024). Enhancing Administrative Efficiency Through Google Workspace: A Case Study At Pesantren Nurul Ikhlas Sidoarjo. International Journal of Educational Research & Social Sciences, 5(4), 763–770. https://doi.org/10.51601/ijersc.v5i4.868
Gao, M., Kortum, P., & Oswald, F. (2018). Psychometric Evaluation of the USE (Usefulness, Satisfaction, and Ease of use) Questionnaire for Reliability and Validity. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 62(1), 1414–1418. https://doi.org/10.1177/1541931218621322
Giraldi, L., Giovannetti, M., & Cedrola, E. (2023). User Experience on E-learning Platforms in Higher Education. Research Square. https://doi.org/10.21203/rs.3.rs-2753702/v1
Gkintoni, E., Antonopoulou, H., Sortwell, A., & Halkiopoulos, C. (2025). Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy. Brain Sciences, 15(2), 203. https://doi.org/10.3390/brainsci15020203
Graham, C. R., Woodfield, W., & Harrison, J. B. (2013). A framework for institutional adoption and implementation of blended learning in higher education. Internet and Higher Education, 18, 4–14. Scopus. https://doi.org/10.1016/j.iheduc.2012.09.003
Guo, L., Burke, M. G., & Griggs, W. M. (2025). A new framework to predict and visualize technology acceptance: A case study of shared autonomous vehicles. Technological Forecasting and Social Change, 212, 123960. https://doi.org/10.1016/j.techfore.2024.123960
Guo, Q., Graham, C. R., Borup, J., Sandberg, B., & West, R. E. (2024). Parental support challenges for K-12 student online engagement. Distance Education, 45(4), 579–605. Scopus. https://doi.org/10.1080/01587919.2024.2397481
Hamilton, E. M., & Rane, A. (2022). Speaking Their Language: Does Environmental Signage Align to Personal Dimensions of Environmentally Responsible Behavior in Undergraduate Residence Halls? Sustainability, 14(4), 2025. https://doi.org/10.3390/su14042025
Haviz, M., Maris, I. M., Azis, D., & Helmita, R. (2024). An investigation into science motivation, technology acceptance, and satisfaction intention during the transition to E-learning of prospective biology teachers. Cogent Education, 11(1), 2393065. https://doi.org/10.1080/2331186X.2024.2393065
Henderson, W., Homan, H., & Bayne, K. (2024). EXPERIENTIAL LEARNING INSTRUCTIONAL METHODS. In Effective Teaching: Instructional Methods and Strategies for Occupational Therapy Education (pp. 101–127). Scopus. https://doi.org/10.4324/9781003523956-6
Hii, P. K., Goh, C. F., Tan, O. K., Amran, R., & Ong, C. H. (2023). An information system success model for e-learning postadoption using the fuzzy analytic network process. Education and Information Technologies, 28(8), 10731–10752. https://doi.org/10.1007/s10639-023-11621-y
Jeyaraj, A. (2020). DeLone & McLean models of information system success: Critical meta-review and research directions. International Journal of Information Management, 54, 102139. https://doi.org/10.1016/j.ijinfomgt.2020.102139
Khan, F. M., Singh, N., Gupta, Y., Kaur, J., Banik, S., & Gupta, S. (2022). A Meta-analysis of Mobile Learning Adoption in Higher Education Based on Unified Theory of Acceptance and Use of Technology 3 (UTAUT3). Vision, 09722629221101159. https://doi.org/10.1177/09722629221101159
Kotian, H., Varghese, A. L., & Rohith, M. (2022). An R Function for Cronbach’s Alpha Analysis: A Case-Based Approach. National Journal of Community Medicine, 13(8), 571–575. Scopus. https://doi.org/10.55489/njcm.130820221149
Kraft, M. A., & Bolves, A. J. (2022). Can technology transform communication between schools, teachers, and parents? Evidence from a randomized field trial. Education Finance and Policy, 17(3), 479–501. https://doi.org/10.1162/edfp_a_00344
Liu, F., & Yin, Z. (2017). Estimation of Nonparametric Regression Models with Measurement Error Using Validation Data. Applied Mathematics, 08(10), 1454–1463. https://doi.org/10.4236/am.2017.810106
López-Arteaga, T., Moreno-Rubio, C., & Mohedano-Moriano, A. (2023). Risk factors for opioid addiction in chronic non-cancer pain. Heliyon, 9(9). https://doi.org/10.1016/j.heliyon.2023.e19707
Ma, L. (2025). Investigation of the impact of cognitive load on EFL learners’ satisfaction with MOOCs: The mediating role of expectation confirmation and perceived usefulness. BMC Psychology, 13(1), 416. https://doi.org/10.1186/s40359-025-02735-8
Maier, C., Thatcher, J. B., Grover, V., & Dwivedi, Y. K. (2023). Cross-sectional research: A critical perspective, use cases, and recommendations for IS research. International Journal of Information Management, 70, 102625. https://doi.org/10.1016/j.ijinfomgt.2023.102625
Mashudi, M., & Hilman, C. (2024). Digital-Based Islamic Religious Education: A New Orientation in Enhancing Student Engagement and Spiritual Understanding. Global International Journal of Innovative Research, 2(10), 2488–2501. https://doi.org/10.59613/global.v2i10.342
Menon, D. (2022). Uses and gratifications of educational apps: A study during COVID-19 pandemic. Computers & Education Open, 3, 100076. https://doi.org/10.1016/j.caeo.2022.100076
Mumcu, B. B., & Çebi, A. (2025). You have a notification: The role of push notifications in shaping students’ engagement, self-regulation and academic procrastination. International Journal of Educational Technology in Higher Education, 22(1), 36. https://doi.org/10.1186/s41239-025-00537-x
Ngo, T. T. A., An, G. K., Nguyen, P. T., & Tran, T. T. (2024). Unlocking Educational Potential: Exploring Students’ Satisfaction and Sustainable Engagement with ChatGPT Using the ECM Model. Journal of Information Technology Education: Research, 23, 021. https://www.informingscience.org/Publications/5344
Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2021). Habit, hedonic motivation, performance expectancy and technological pedagogical knowledge affect teachers’ intention to use mobile internet. Computers & Education Open, 2, 100041. https://doi.org/10.1016/j.caeo.2021.100041
Oliveras-Ortiz, Y., Bouillion, D. E., & Asbury, L. (2021). Learning Spaces Matter: Student Engagement in New Learning Environments. Journal of Education, 201(3), 174–182. Scopus. https://doi.org/10.1177/0022057420908062
Oudbier, S. J., Smets, E. M., Nieuwkerk, P. T., Neal, D. P., Nurmohamed, S. A., Meij, H. J., & Dusseljee-Peute, L. W. (2025). Patients’ Experienced Usability and Satisfaction With Digital Health Solutions in a Home Setting: Instrument Validation Study. JMIR Medical Informatics, 13. https://doi.org/10.2196/63703
Owens, M., Ravi, V., & Hunter, E. (2023). Digital Inclusion as a Lens for Equitable Parent Engagement. TechTrends. Scopus. https://doi.org/10.1007/s11528-023-00859-5
Pan, G., Mao, Y., Song, Z., & Nie, H. (2024). Research on the influencing factors of adult learners’ intent to use online education platforms based on expectation confirmation theory. Scientific Reports, 14(1), 12762. https://doi.org/10.1038/s41598-024-63747-9
Parasuraman, A. (2000). Technology Readiness Index (Tri): A Multiple-Item Scale to Measure Readiness to Embrace New Technologies. Journal of Service Research, 2(4), 307–320. Scopus. https://doi.org/10.1177/109467050024001
Pérez-Guerrero, E. E., Guillén-Medina, M. R., Márquez-Sandoval, F., Vera-Cruz, J. M., Gallegos-Arreola, M. P., Rico-Méndez, M. A., Aguilar-Velázquez, J. A., & Gutiérrez-Hurtado, I. A. (2024). Methodological and Statistical Considerations for Cross-Sectional, Case–Control, and Cohort Studies. Journal of Clinical Medicine, 13(14), 4005. https://doi.org/10.3390/jcm13144005
Proff, A., Musalam, R., & Matar, F. (2025). Lessons learned for leaders: Implications for parent-school communication in post-pandemic learning environments. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1496319
Rulinawaty, Kasmad, Samboteng, L., Purwanto, A. J., Kuncoro, S., Jasrial, Tahilili, M. H., Efendi, Y., & Karyana, A. (2024). Investigating the influence of the updated DeLone and McLean information system success model on the effectiveness of learning management system implementation. Cogent Education, 11(1), 2365611. https://doi.org/10.1080/2331186X.2024.2365611
Sayaf, A. M. (2023). Adoption of E-learning systems: An integration of ISSM and constructivism theories in higher education. Heliyon, 9(2). https://doi.org/10.1016/j.heliyon.2023.e13014
See, B. H., Gorard, S., El-Soufi, N., Lu, B., Siddiqui, N., & Dong, L. (2020). A systematic review of the impact of technology-mediated parental engagement on student outcomes. Educational Research and Evaluation, 26(3–4), 150–181. Scopus. https://doi.org/10.1080/13803611.2021.1924791
Senbekov, M., Saliev, T., Bukeyeva, Z., Almabayeva, A., Zhanaliyeva, M., Aitenova, N., Toishibekov, Y., & Fakhradiyev, I. (2020). The Recent Progress and Applications of Digital Technologies in Healthcare: A Review. International Journal of Telemedicine and Applications, 2020(1), 8830200. https://doi.org/10.1155/2020/8830200
Son, Y. S., & Kwon, K. H. (2023). Utilization of smart devices and the evolution of customized healthcare services focusing on big data: A systematic review. mHealth, 10, 7. https://doi.org/10.21037/mhealth-23-24
Taber, K. S. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education, 48(6), 1273–1296. Scopus. https://doi.org/10.1007/s11165-016-9602-2
Taufikin, T., Zamroni, Z., & Nurhayati, S. (2025). Advancing Islamic Education Through Total Quality Management: Insights from Tahfiz Qur’an Practices. Al-Tanzim: Jurnal Manajemen Pendidikan Islam, 9(2), Article 2. https://doi.org/10.33650/al-tanzim.v9i2.10706
Tuck, A. B., & Thompson, R. J. (2024). The Social Media Use Scale: Development and Validation. Assessment, 31(3), 617–636. https://doi.org/10.1177/10731911231173080
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3), 425–478. Scopus. https://doi.org/10.2307/30036540
Wang, Y. (2023). Research on psychological satisfaction of educatiwork and learning of literary works. HTS Teologiese Studies / Theological Studies, 79(4). Scopus. https://doi.org/10.4102/hts.v79i4.8844
Woodhouse, H., Passey, D., & Anderson, J. (2024). Using Digital Technologies to Build Connections between Families and Schools as Children Transition to School. Education Sciences, 14(5), 520. https://doi.org/10.3390/educsci14050520
Ye, J.-H., Lee, Y.-S., Wang, C.-L., Nong, W., Ye, J.-N., & Sun, Y. (2023). The Continuous Use Intention for the Online Learning of Chinese Vocational Students in the Post-Epidemic Era: The Extended Technology Acceptance Model and Expectation Confirmation Theory. Sustainability, 15(3), 1819. https://doi.org/10.3390/su15031819
Zheng, H., Han, F., Huang, Y., Wu, Y., & Wu, X. (2025). Factors influencing behavioral intention to use e-learning in higher education during the COVID-19 pandemic: A meta-analytic review based on the UTAUT2 model. Education and Information Technologies, 30(9), 12015–12053. https://doi.org/10.1007/s10639-024-13299-2
Zhou, L., Xue, S., & Li, R. (2022). Extending the Technology Acceptance Model to Explore Students’ Intention to Use an Online Education Platform at a University in China. SAGE Open, 12(1), 21582440221085259. https://doi.org/10.1177/21582440221085259