The Effectiveness of Podcast-Based Learning for Improving Listening Skills: A Ubiquitous Learning Approach
Article Sidebar
-
Language Learning, Listening Skills, Ubiquitous Learning
Abstract
Background. The widespread availability of podcasts and the ubiquity of mobile devices enable learners to access diverse listening materials anytime and anywhere, making this approach highly suitable for ubiquitous learning.
Purpose. This study aimed to evaluate the effectiveness of podcast-based learning in improving learners’ listening comprehension skills by employing a ubiquitous learning approach. It also sought to examine learners’ engagement and perceptions of the effectiveness of podcast-based learning compared to traditional listening instruction.
Method. The research employed a quasi-experimental design involving two groups of learners. The experimental group used podcasts as the primary listening tool, while the control group relied on traditional listening methods.
Results. The findings revealed a significant improvement in listening comprehension among learners in the podcast-based learning group compared to those in the control group. In addition, learners exposed to podcast-based learning reported higher levels of engagement, satisfaction, and perceived effectiveness than those using traditional methods.
Conclusion. The study concludes that podcast-based learning is an effective and engaging tool for enhancing listening comprehension, particularly in contexts where traditional classroom-based learning is limited or not feasible.
Full text article
References
Arakawa, R., Nakagawa, M., & Yakura, H. (2025). UbiLearn: Supporting English-as-a-Foreign-Language Learners in Reflecting on Conversations Using a Smartwatch. Proceedings of the ACM on Human-Computer Interaction, 9(5). Scopus. https://doi.org/10.1145/3743735
Bussing, A., Lerner, J. Y., & Spell, G. P. (2025). The Language of Delegation: An NLP Analysis of Congressional Bill Text. Legislative Studies Quarterly, 50(4). Scopus. https://doi.org/10.1111/lsq.70012
de Smet, L., Venturato, G., De Raedt, L., & Marra, G. (2025). Relational Neurosymbolic Markov Models. In T. Walsh, J. Shah, & Z. Kolter (Eds.), Proc. AAAI Conf. Artif. Intell. (Vol. 39, Issue 15, pp. 16181–16189). Association for the Advancement of Artificial Intelligence; Scopus. https://doi.org/10.1609/aaai.v39i15.33777
Dutta, A., Maheswari, B., Punitha, N., Raj, A. S. A., Banu, S. S., & Balamurugan, M. (2025). The First Two Months of Kolmogorov-Arnold Networks (KANs): A Survey of the State-of-the-Art. Archives of Computational Methods in Engineering. Scopus. https://doi.org/10.1007/s11831-025-10328-2
Ebrahimi, M. (2025). Ubiquitous learning: The effect of LingAR application on EFL learners’ language achievement and the realization of their motivation towards mobile learning. Interactive Learning Environments, 33(8), 4699–4717. Scopus. https://doi.org/10.1080/10494820.2022.2041044
Eltahir, M. M., Subait, W. B., Alahmari, S., Alharbi, A. A. K., Salama, A. S., & Alneil, A. A. (2025). UBIQUITOUS LEARNING-DRIVEN NATURAL LANGUAGE PROCESSING FOR TWITTER CLIMATE CHANGE SENTIMENT ANALYSIS USING BAYESIAN MACHINE LEARNING FOR FOSTER SUSTAINABLE DEVELOPMENT IN RURAL AREAS. Fractals, 33(2). Scopus. https://doi.org/10.1142/S0218348X25400420
Endress, A. D., & de Seyssel, M. (2025). The specificity of sequential statistical learning: Statistical learning accumulates predictive information from unstructured input but is dissociable from (declarative) memory for words. Cognition, 261. Scopus. https://doi.org/10.1016/j.cognition.2025.106130
Galimova, E. G., Sergeeva, O. V., Zheltukhina, M. R., Sokolova, N. L., Zakharova, V. L., & Drobysheva, N. N. (2025). Mobile learning in science education to improve higher-order thinking skills and communication skills: Scoping review. Frontiers in Communication, 10. Scopus. https://doi.org/10.3389/fcomm.2025.1624012
García-Zarza, P., Taibi, D., Asensio-Pérez, J. I., Bote-Lorenzo, M. L., & Vega-Gorgojo, G. (2025). Towards the Improvement of Learning Task Generation by Combining Large Language Models with Linked Open Data. In P. Ardimento, G. Zaza, R. Di Fuccio, P. Limone, G. Fulantelli, D. Schicchi, D. Taibi, R. Pecori, P. Raviolo, & M. Rondonotti (Eds.), Commun. Comput. Info. Sci.: Vol. 2467 CCIS (pp. 101–112). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-031-94002-6_8
Hajmalek, M. M., & Sabouri, S. (2025). Tapping into second language learners’ musical intelligence to tune up for computer-assisted pronunciation training. Computer Assisted Language Learning. Scopus. https://doi.org/10.1080/09588221.2025.2488855
Hasnine, M. N. (2025). Multimedia annotations and modalities integration in vocabulary learning systems in pre-AI days: A review and appraisal. In Y.-W. Chen, R. J. Howlett, R. J. Howlett, L. C. Jain, L. C. Jain, & J. Flearmoy (Eds.), Procedia Comput. Sci. (Vol. 270, pp. 4263–4273). Elsevier B.V.; Scopus. https://doi.org/10.1016/j.procs.2025.09.551
Hassan, T., Hussain, I., Ul Haque, H. M., Mirza, H. T., Ali, M. N., & Kim, B.-S. (2025). Semantic Knowledge Based Reinforcement Learning Formalism for Smart Learning Environments. Computers, Materials and Continua, 85(1), 2071–2094. Scopus. https://doi.org/10.32604/cmc.2025.068533
Keller, D., Jagwani, A., & Lazzarini, V. (2025). The Ubimus Plugging Framework: Deploying FPGA-Based Prototypes for Ubiquitous Music Hardware Design. Computers, 14(4). Scopus. https://doi.org/10.3390/computers14040155
Kittredge, A. K., Hopman, E. W. M., Reuveni, B., Dionne, D., Freeman, C., & Jiang, X. (2025). Mobile language app learners’ self-efficacy increases after using generative AI. Frontiers in Education, 10. Scopus. https://doi.org/10.3389/feduc.2025.1499497
Lai, H., Luo, D., Yang, M., Zhu, T., Yang, H., Luo, X., Wei, Y., Xie, S., Hong, F., Shu, K., Dao, F., & Ding, H. (2025). PBertKla: A protein large language model for predicting human lysine lactylation sites. BMC Biology, 23(1). Scopus. https://doi.org/10.1186/s12915-025-02202-1
Lawson McLean, A., & Lawson McLean, A. C. (2025). Silent teachers: Narratives from the simulation lab. Medical Education. Scopus. https://doi.org/10.1111/medu.15711
Ma, C.-H., Hao, X.-L., Meng, X.-F., & Zhang, X.-K. (2025). Survey on Machine Learning for Multi-Dimensional Data Query Processing. Jisuanji Xuebao/Chinese Journal of Computers, 48(1), 100–123. Scopus. https://doi.org/10.11897/SP.J.1016.2025.00100
Maulana, I. (2025). Nurturing an Inquisitive Mind in the Era of All-Knowing Chatbots. In Chatbots and Beyond: Exploring the Future of Conversational Technology in Education (pp. 1–21). wiley; Scopus. https://doi.org/10.1002/9781394411283.ch1
Mendoza, A. P., Barrios Quiroga, K. J., Solano Celis, S. D., & Quintero M., C. G. M. (2025). NAIA: A Multi-Technology Virtual Assistant for Boosting Academic Environments—A Case Study. IEEE Access, 13, 141461–141483. Scopus. https://doi.org/10.1109/ACCESS.2025.3597565
Milne, C., & Watt, S. A. (2025). Privacy. In Mechatron. Futures: Furth. Challenges and Solutions for Mechatron. Systems and their Designers (pp. 107–119). Springer Science+Business Media; Scopus. https://doi.org/10.1007/978-3-031-83571-1_6
Min, Z., Xiao, Q., Abbas, M., & Zhang, D. (2025). Retentive network-based time series anomaly detection in cyber-physical systems. Engineering Applications of Artificial Intelligence, 145. Scopus. https://doi.org/10.1016/j.engappai.2025.110215
Nie, A., Chandak, Y., Suzara, M., Malik, A., Woodrow, J., Peng, M., Sahami, M., Brunskill, E., & Piech, C. (2025). The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement But May Increase Adopters’ Exam Performances. L Proc. ACM Conf. Learn. Scale, 376–380. Scopus. https://doi.org/10.1145/3698205.3733960
Nikitina, L., Su, L. L., & Furuoka, F. (2025). The Relationship Between L2 Motivation and Epistemic Emotions of Boredom and Curiosity: A Study Among Adolescent Learners of English. Journal of Psycholinguistic Research, 54(1). Scopus. https://doi.org/10.1007/s10936-024-10119-z
Pietsch, M., Aydin, B., Montecinos, C., & Belliba?, M. ?. (2025). Organizational ambidexterity and student achievement: Do knowledge exploration and exploitation in schools make a difference? Journal of Innovation and Knowledge, 10(1). Scopus. https://doi.org/10.1016/j.jik.2024.100636
Prem, P., Saggu, S. K., Boadu, A., Saju, S., Nisbet, K., & Cummine, J. (2025). Neuroanatomical correlates of auditory and visual statistical learning: Cortical and subcortical volume predictors. Neuroscience, 587, 157–168. Scopus. https://doi.org/10.1016/j.neuroscience.2025.09.035
Raj, V., Bhattacharjee, S., & Nekkala, S. (2025). Predicting if SQL Injection Query can get access to Database using machine learning techniques. Int. Conf. Recent Adv. Inf. Technol., RAIT. Scopus. https://doi.org/10.1109/RAIT65068.2025.11088918
Snehi, J., Kansal, I., & Kumar, A. (2025). Navigating the linguistic landscape: Unveiling the future trajectory of natural language processing in an evolving digital era. In High-Performance Automation Methods for Computational Intelligent Systems: Design and Enabling Technologies (pp. 151–177). CRC Press; Scopus. https://doi.org/10.1201/9781003643609-6
Srivastava, M. (2025). Robotics for Personalized Motor Skills Instruction. ACM/IEEE Int. Conf. Hum.-Rob. Interact., 1888–1890. Scopus. https://doi.org/10.1109/HRI61500.2025.10973968
Sun, G., Ayepah-Mensah, D., Maale, G. T., Omer, M. B., Kuadey, N. A., Kwantwi, T., Liu, Y., & Liu, G. (2025). Toward AI-Native Task Orchestration for Collaborative Computing in SAGSINs. IEEE Communications Magazine, 63(12), 112–118. Scopus. https://doi.org/10.1109/MCOM.004.2400304
Sun, Q., Cetin, E., & Tang, Y. (2025). TRANSFORMER-SQUARED: SELF-ADAPTIVE LLMS. Int. Conf. Learn. Represent., ICLR, 28754–28771. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-105010210555&partnerID=40&md5=82e9b5ec83e612e20e3bcdf8f1445989
Suzuki, R., Abtahi, P., Zhu-Tian, C., Dogan, M. D., Colaço, A., Gonzalez, E. J., Ahuja, K., & González-Franco, M. (2025). Programmable reality. Frontiers in Virtual Reality, 6. Scopus. https://doi.org/10.3389/frvir.2025.1649785
Wang, X., Zhang, Z., Xiao, L., Chen, H., Ge, C., & Zhu, W. (2025). Towards multimodal graph large language model. Science China Information Sciences, 68(11). Scopus. https://doi.org/10.1007/s11432-025-4627-3
Wang, Z., Shi, Z., Zhou, H., Gao, S., Sun, Q., & Li, J. (2025). Towards Objective Fine-tuning: How LLMs’ Prior Knowledge Causes Potential Poor Calibration? In W. Che, J. Nabende, E. Shutova, & M. T. Pilehvar (Eds.), Proc. Annu. Meet. Assoc. Comput Linguist. (Vol. 1, pp. 14830–14853). Association for Computational Linguistics (ACL); Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-105021011504&partnerID=40&md5=ce11e36086d631d28c92beff11102362
Wei, Q., Huang, J., Gao, Y., & Dong, W. (2025). One Model to Fit Them All: Universal IMU-based Human Activity Recognition with LLM-assisted Cross-dataset Representation. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 9(3). Scopus. https://doi.org/10.1145/3749509
Yan, J., Chen, J., Hu, C., Zheng, B., Hu, Y., Sun, J., & Wu, J. (2025). SMALL MODELS ARE LLM KNOWLEDGE TRIGGERS FOR MEDICAL TABULAR PREDICTION. Int. Conf. Learn. Represent., ICLR, 92990–93005. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-105010261432&partnerID=40&md5=fae5dc40f4a75e539cc9278a26ae8fb3
