Comparative Study of Synchronous VS Asynchronous Distance Learning
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The evolution of distance learning has led to the widespread adoption of synchronous and asynchronous learning models, each offering unique advantages and challenges for students and educators. Synchronous learning provides real-time interaction, fostering immediate feedback and engagement, while asynchronous learning offers flexibility, enabling students to learn at their own pace. Understanding the effectiveness of these two models is essential for optimizing digital education strategies in higher education. This study aims to compare the impact of synchronous and asynchronous learning on student engagement, knowledge retention, and academic performance. A mixed-methods research design was employed, integrating quantitative analysis of student achievement data with qualitative insights from learner surveys and instructor interviews. Findings indicate that synchronous learning enhances real-time engagement and collaboration, whereas asynchronous learning promotes independent learning and accommodates diverse schedules. Statistical analysis reveals that both models contribute to academic success, but their effectiveness varies based on learner preferences, course design, and technological access. The study concludes that a blended approach, combining synchronous and asynchronous elements, may offer the most effective learning experience. Future research should explore long-term outcomes and best practices for integrating both models in diverse educational contexts.
Altaleb, H., Mouti, S., & Beegom, S. (2023). Enhancing College Education: An AI-driven Adaptive Learning Platform (ALP) for Customized Course Experiences. Dalam Hachimi H., Abdo A.A., & Benmamoun Z. (Ed.), Int. Conf. Optim. Appl., ICOA - Proc. Institute of Electrical and Electronics Engineers Inc.; Scopus. https://doi.org/10.1109/ICOA58279.2023.10308834
Azevedo, B. F., Pacheco, M. F., Fernandes, F. P., & Pereira, A. I. (2024). Dataset of mathematics learning and assessment of higher education students using the MathE platform. Data in Brief, 53. Scopus. https://doi.org/10.1016/j.dib.2024.110236
BenMessaoud, F., Bolchini, D., Ash, E., & Tseng, C.-M. (2023). FazBoard: An AI-Educational Hybrid Teaching and Learning System. Dalam Arai K. (Ed.), Lect. Notes Networks Syst.: Vol. 813 LNNS (hlm. 305–315). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-031-47454-5_23
Chen Q. & Li J. (Ed.). (2021). Asia Pacific Web and Web-Age Information Management Joint International Conference on Web and Big Data, APWeb-WAIM 2020 in conjunction with 3rd International Workshop on Knowledge Graph Management and Applications, KGMA 2020, 2nd International Workshop on Semi-structured Big Data Management and Applications, SemiBDMA 2020 and 1st International Workshop on Deep Learning in Large-scale Unstructured Data Analytics, DeepLUDA 2020. Communications in Computer and Information Science, 1373 CCIS. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107345604&partnerID=40&md5=b0fce28b324913f171ebf763d3963ec7
Cristea, A. I., Alamri, A., Kayama, M., Stewart, C., Alshehri, M., & Shi, L. (2018). Earliest predictor of dropout in MOOCs: A longitudinal study of futurelearn courses. Dalam Andersson B., Johansson B., Barry C., Lang M., Linger H., & Schneider C. (Ed.), Proc. Int. Conf. Inf. Syst. Dev.: Des. Digit., ISD. Association for Information Systems; Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086228712&partnerID=40&md5=f79d263fbbba4dbf85101d84fba5b3f3
Dai, J.-Y., Yeh, K.-L., Kao, M.-T., Yuan, Y.-H., & Chang, M.-W. (2021). Applying petri-net to construct knowledge graphs for adaptive learning diagnostics and learning recommendations. Journal of Research in Education Sciences, 66(3), 61–105. Scopus. https://doi.org/10.6209/JORIES.202109_66(3).0003
Dunagan, L., & Larson, D. A. (2021). Alignment of Competency-Based Learning and Assessment to Adaptive Instructional Systems. Dalam Sottilare R.A. & Schwarz J. (Ed.), Lect. Notes Comput. Sci.: Vol. 12792 LNCS (hlm. 537–549). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-030-77857-6_38
Fessl, A., Wertner, A., & Pammer-Schindler, V. (2018). Challenges in developing automatic learning guidance in relation to an information literacy curriculum. Dalam Fessl A., Thalmann S., d’Aquin M., Holtz P., & Dietze S. (Ed.), CEUR Workshop Proc. (Vol. 2209). CEUR-WS; Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055702175&partnerID=40&md5=e1f06a416a8acdef537b17e46b195229
Geetha, M. C. S., Kaviyassri, K., Pacifica, J. J., & Kaviyadharshini, M. (2025). Education 4.0: Unraveling the Data Science Connection. Dalam Cyber security and Data Science Innovations for Sustainable Development of HEICC: Healthcare, Education, Industry, Cities, and Communities (hlm. 196–212). CRC Press; Scopus. https://doi.org/10.1201/9781032711300-14
Hamiz, M., Bakri, M., Kamaruddin, N., & Mohamed, A. (2018). Assessment analytic theoretical framework based on learners’ continuous learning improvement. Indonesian Journal of Electrical Engineering and Computer Science, 11(2), 682–687. Scopus. https://doi.org/10.11591/ijeecs.v11.i2.pp682-687
Hernández, R., & Amado-Salvatierra, H. R. (2018). An adaptive learning approach using a full engagement educational framework. Dalam Lecture. Notes. Data Eng. Commun. Tech. (Vol. 13, hlm. 622–631). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-319-69835-9_58
Liu, H., Malone, N., Yedjou, C., Chadwick, R., Haag, J., & Spector, M. (2024). Automating Formative Assessment for STEM Courses in Hybrid Learning Environments. IEEE Global Eng. Edu. Conf., EDUCON. IEEE Global Engineering Education Conference, EDUCON. Scopus. https://doi.org/10.1109/EDUCON60312.2024.10578901
Mujawar, N. K., & Nirmale, R. L. (2024). Inclusivity and accessibility: The key pillars of progressive education. Dalam Prog. In Educ. (Vol. 83, hlm. 91–123). Nova Science Publishers, Inc.; Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206274196&partnerID=40&md5=b0f04fb111cc80b9dfcda0c887567d35
Nutalapati, H., Velmurugan, S., & Tiglao, N. M. (2024). Coding Buddy: An Adaptive AI-Powered Platform for Personalized Learning. Int. Symp. Networks, Comput. Commun., ISNCC. 2024 International Symposium on Networks, Computers and Communications, ISNCC 2024. Scopus. https://doi.org/10.1109/ISNCC62547.2024.10759044
Opincariu, M. (2019). Integrating emotional linguistic attributes in elearning designs. Revista Transilvania, 2019(8), 56–64. Scopus.
Quigley, D., Caccamise, D., Weatherley, J., & Foltz, P. (2020). Exploring video engagement in an intelligent tutoring system. Dalam Sottilare R.A. & Schwarz J. (Ed.), Lect. Notes Comput. Sci.: Vol. 12214 LNCS (hlm. 519–530). Springer; Scopus. https://doi.org/10.1007/978-3-030-50788-6_38
Quijano-Cabezas, P. A., Duque-Méndez, N., & Jiménez-Builes, J. A. (2024). Data Generation Strategies for the Application of Adaptive Learning Analytics. Dalam Duque-Méndez N.D., Aristizábal-Quintero L.A., Orozco-Alzate M., & Aguilar J. (Ed.), Commun. Comput. Info. Sci.: Vol. 2209 CCIS (hlm. 193–210). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-031-75236-0_15
Raj, N. S., Prasad, S., Harish, P., Boban, M., & Cheriyedath, N. (2021). Early Prediction of At-Risk Students in a Virtual Learning Environment Using Deep Learning Techniques. Dalam Sottilare R.A. & Schwarz J. (Ed.), Lect. Notes Comput. Sci.: Vol. 12793 LNCS (hlm. 110–120). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-030-77873-6_8
Saeed, M. M. A., Saeed, R. A., Ahmed, Z. E., Gaid, A. S. A., & Mokhtar, R. A. (2024). AI technologies in engineering education. Dalam AI-Enhanc. Teach. Methods (hlm. 61–87). IGI Global; Scopus. https://doi.org/10.4018/979-8-3693-2728-9.ch003
Saul, K., Howard, A. K. T., Webster, Z., & Spencer, D. (2022). An Adaptive Learning Engineering Mechanics Curricular Sequence. ASEE Annu. Conf. Expos. Conf. Proc. ASEE Annual Conference and Exposition, Conference Proceedings. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138247314&partnerID=40&md5=fe8db267dd6e549c026dfc4e39c468f3
Singh, C., & Pandey, A. (2019). Analysing trends in student’s performance across maharashtra through non-adaptive and adaptive online assessments based on the underlying framework of classical test and item response theory. Dalam Jain L.C., Johri P., & Balas V.E. (Ed.), Adv. Intell. Sys. Comput. (Vol. 847, hlm. 305–325). Springer Verlag; Scopus. https://doi.org/10.1007/978-981-13-2254-9_27
van der Stappen, E., & Baartman, L. (2019). Automated feedback for workplace learning in higher education. Dalam Draaijer S., Joosten-ten Brinke D., & Ras E. (Ed.), Commun. Comput. Info. Sci. (Vol. 1014, hlm. 73–90). Springer Verlag; Scopus. https://doi.org/10.1007/978-3-030-25264-9_6
Xi, J., Chen, Y., & Wang, G. (2018). Design of a personalized massive open online course platform. International Journal of Emerging Technologies in Learning, 13(4), 58–70. Scopus. https://doi.org/10.3991/ijet.v13i04.8470
Zhang, N., Biswas, G., Chiu, J. L., & McElhaney, K. W. (2019). Analyzing students’ design solutions in an NGSS-aligned earth sciences curriculum. Dalam Isotani S., Millán E., Ogan A., McLaren B., Hastings P., & Luckin R. (Ed.), Lect. Notes Comput. Sci.: Vol. 11625 LNAI (hlm. 532–543). Springer Verlag; Scopus. https://doi.org/10.1007/978-3-030-23204-7_44
