Use of Arabic Language Learning Media at Madani Islamic Boarding School Payakumbuh Integrated Islamic Junior High School
Article Sidebar
-
Arabic Language, Learning Media, SMP-IT Madani
Abstract
The use of learning media is important in improving the effectiveness of Arabic language learning at SMP-IT Madani Islamic Boarding School Payakumbuh. In today's digital era, the integration of technology in education is becoming increasingly relevant to create a more interactive and engaging learning environment for students. The purpose of this study is to find out the process of using technological media in improving students' understanding of Arabic, as well as to identify the challenges faced in its application. The research method used was descriptive qualitative with a case study approach. Data were collected through direct observation, in-depth interviews with teachers and students, and analysis of related documents. This approach enabled the researcher to gain an in-depth understanding of the teachers' and students' experiences and perceptions of the use of technological media in learning. The result of this study is that the use of technological media has provided various benefits in Arabic language learning at SMP IT Madani Islamic Boarding School. Teachers use blended learning approach and utilise learning videos from YouTube to teach various Arabic language skills, especially speaking ability and vocabulary memorisation. The conclusion of this study is that technological media has great potential to improve the quality of Arabic language learning at SMP-IT Madani Islamic Boarding School Payakumbuh. To optimise its benefits, it is necessary to increase access to technology for all students, continuous training for teachers, and development of learning content in accordance with the curriculum. With these steps, it is hoped that the Arabic learning process can become more effective and enjoyable for students.
Full text article
References
Abdelgwad, M. M. (2022). Arabic aspect sentiment polarity classification using BERT. Journal of Big Data, 9(1). https://doi.org/10.1186/s40537-022-00656-6
Abdul, W. (2021). Intelligent real-time Arabic sign language classification using attention-based inception and BiLSTM. Computers and Electrical Engineering, 95(Query date: 2024-06-28 13:04:23). https://doi.org/10.1016/j.compeleceng.2021.107395
Akhtar, S. (2020). Improving mispronunciation detection of arabic words for non-native learners using deep convolutional neural network features. Electronics (Switzerland), 9(6), 1–17. https://doi.org/10.3390/electronics9060963
Al-Ayyoub, M. (2019). A comprehensive survey of arabic sentiment analysis. Information Processing and Management, 56(2), 320–342. https://doi.org/10.1016/j.ipm.2018.07.006
Al-Dabet, S. (2021). Enhancing Arabic aspect-based sentiment analysis using deep learning models. Computer Speech and Language, 69(Query date: 2024-06-28 13:04:23). https://doi.org/10.1016/j.csl.2021.101224
Alshalan, R. (2020). A deep learning approach for automatic hate speech detection in the saudi twittersphere. Applied Sciences (Switzerland), 10(23), 1–16. https://doi.org/10.3390/app10238614
Fouad, K. M. (2022). Arabic fake news detection using deep learning. Computers, Materials and Continua, 71(2), 3647–3665. https://doi.org/10.32604/cmc.2022.021449
Hamed, H. (2021). Deep learning approach for Translating Arabic Holy Quran into Italian language. 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021, Query date: 2024-06-28 13:04:23, 193–199. https://doi.org/10.1109/MIUCC52538.2021.9447650
Hawes, Z. (2019). Kindergarten children’s symbolic number comparison skills predict 1st grade mathematics achievement: Evidence from a two-minute paper-and-pencil test. Learning and Instruction, 59(Query date: 2024-06-28 13:04:23), 21–33. https://doi.org/10.1016/j.learninstruc.2018.09.004
Husain, F. (2021a). A Survey of Offensive Language Detection for the Arabic Language. ACM Transactions on Asian and Low-Resource Language Information Processing, 20(1). https://doi.org/10.1145/3421504
Husain, F. (2021b). Leveraging Offensive Language for Sarcasm and Sentiment Detection in Arabic. WANLP 2021 - 6th Arabic Natural Language Processing Workshop, Proceedings of the Workshop, Query date: 2024-06-28 13:04:23, 364–369.
Ikhlas, R. Z., Japakiya, R., & Muzayanah, T. (2023). Utilization of Canva Application as a Learning Media Video Creation. Journal of Social Science Utilizing Technology, 1(3), 158–169. https://doi.org/10.55849/jssut.v1i3.558
Mahmoud, A. (2021). Semantic similarity analysis for corpus development and paraphrase detection in arabic. International Arab Journal of Information Technology, 18(1), 1–7. https://doi.org/10.34028/iajit/18/1/1
Mozannar, H. (2019). Neural arabic question answering. ACL 2019 - 4th Arabic Natural Language Processing Workshop, WANLP 2019 - Proceedings of the Workshop, Query date: 2024-06-28 13:04:23, 108–118.
Mudinillah, A., & Rizaldi, M. (2021). Using the Canva Application as an Arabic Learning Media at SMA Plus Panyabungan. At-Tasyrih: jurnal pendidikan dan hukum Islam, 7(2), 95–106. https://doi.org/10.55849/attasyrih.v7i2.67
Nassif, A. B. (2021). Deep learning for Arabic subjective sentiment analysis: Challenges and research opportunities. Applied Soft Computing, 98(Query date: 2024-06-28 13:04:23). https://doi.org/10.1016/j.asoc.2020.106836
Oueslati, O. (2020). A review of sentiment analysis research in Arabic language. Future Generation Computer Systems, 112(Query date: 2024-06-28 13:04:23), 408–430. https://doi.org/10.1016/j.future.2020.05.034
Saeed, R. M. K. (2022). An ensemble approach for spam detection in Arabic opinion texts. Journal of King Saud University - Computer and Information Sciences, 34(1), 1407–1416. https://doi.org/10.1016/j.jksuci.2019.10.002
Salam, M. Y. & Adam Mudinillah. (2021). Canva Application Development for Distance Learning on Arabic Language Learning in MTs Thawalib Tanjung Limau Tanah Datar. JTP - Jurnal Teknologi Pendidikan, 23(2), 101–111. https://doi.org/10.21009/jtp.v23i2.20650
Sundler, A. J., Lindberg, E., Nilsson, C., & Palmér, L. (2019). Qualitative thematic analysis based on descriptive phenomenology. Nursing Open, 6(3), 733–739. https://doi.org/10.1002/nop2.275
Authors
Copyright (c) 2024 Rifki Zaitul Ikhlas, Abdul Muntaqim Al Anshory, Nur Hasaniyah
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.