Improving Student Achievement in Qur’an Hadith Lessons Through Animated Videos
Abstract
Background. The development of Information and Communication Technology (ICT) in the world of education is increasing. Teachers are required to be more creative and innovative in choosing suitable media when delivering learning materials, one of which is animated videos. Animated videos are audiovisual media that can attract students’ attention and present objects in detail, making it easier for students to understand the learning material.
Purpose. The purpose of this research is to improve student achievement in Qur’an Hadith lessons through animated videos. Another goal is to increase students’ motivation so that they do not feel bored or disinterested during the learning process.
Method. The research method used is a quantitative approach with a survey model.
Results. The research method used is a quantitative approach with a survey model.
Conclusion. The limitation of this study is that the researcher only used animated videos for teaching Qur’an Hadith. The researcher hopes that future studies can conduct similar research with different subjects.
Full text article
References
Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2018). Mobile Edge Computing: A Survey. IEEE Internet of Things Journal, 5(1), 450–465. https://doi.org/10.1109/JIOT.2017.2750180
Abdelaal, H. M., Ahmed, A. M., Ghribi, W., & Youness Alansary, H. A. (2019). Knowledge Discovery in the Hadith According to the Reliability and Memory of the Reporters Using Machine Learning Techniques. IEEE Access, 7, 157741–157755. https://doi.org/10.1109/ACCESS.2019.2944118
Al Eid, N. A., & Arnout, B. A. (2020). Crisis and disaster management in the light of the Islamic approach: COVID ?19 pandemic crisis as a model (a qualitative study using the grounded theory). Journal of Public Affairs. https://doi.org/10.1002/pa.2217
Amini, F., Riche, N. H., Lee, B., Leboe-McGowan, J., & Irani, P. (2018). Hooked on data videos: Assessing the effect of animation and pictographs on viewer engagement. Proceedings of the 2018 International Conference on Advanced Visual Interfaces, 1–9. https://doi.org/10.1145/3206505.3206552
Anglemyer, A., Moore, T. H., Parker, L., Chambers, T., Grady, A., Chiu, K., Parry, M., Wilczynska, M., Flemyng, E., & Bero, L. (2020). Digital contact tracing technologies in epidemics: A rapid review. Cochrane Database of Systematic Reviews, 2020(8). https://doi.org/10.1002/14651858.CD013699
Azizah, N., Mochsif, N. D. A., & Kusairi, S. (2021). Review of video-based interactive multimedia needs for senior high school physics learning. 050026. https://doi.org/10.1063/5.0043436
Azmi, A. M., Al-Qabbany, A. O., & Hussain, A. (2019). Computational and natural language processing based studies of hadith literature: A survey. Artificial Intelligence Review, 52(2), 1369–1414. https://doi.org/10.1007/s10462-019-09692-w
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019). Multimodal Machine Learning: A Survey and Taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423–443. https://doi.org/10.1109/TPAMI.2018.2798607
Basar, E., Di Renzo, M., De Rosny, J., Debbah, M., Alouini, M.-S., & Zhang, R. (2019). Wireless Communications Through Reconfigurable Intelligent Surfaces. IEEE Access, 7, 116753–116773. https://doi.org/10.1109/ACCESS.2019.2935192
Bello-Bravo, J., & Pittendrigh, B. R. (2018). Scientific Animations Without Borders (SAWBO): Animating IPM Information and Education Everywhere. Outlooks on Pest Management, 29(2), 58–61. https://doi.org/10.1564/v29_apr_02
Bienhaus, F., & Haddud, A. (2018). Procurement 4.0: Factors influencing the digitisation of procurement and supply chains. Business Process Management Journal, 24(4), 965–984. https://doi.org/10.1108/BPMJ-06-2017-0139
Bond, K. T., & Ramos, S. R. (2019). Utilization of an Animated Electronic Health Video to Increase Knowledge of Post- and Pre-Exposure Prophylaxis for HIV Among African American Women: Nationwide Cross-Sectional Survey. JMIR Formative Research, 3(2), e9995. https://doi.org/10.2196/formative.9995
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018). A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities. Journal of Internet Services and Applications, 9(1), 16. https://doi.org/10.1186/s13174-018-0087-2
Brusasco, C., Santori, G., Bruzzo, E., Trò, R., Robba, C., Tavazzi, G., Guarracino, F., Forfori, F., Boccacci, P., & Corradi, F. (2019). Quantitative lung ultrasonography: A putative new algorithm for automatic detection and quantification of B-lines. Critical Care, 23(1), 288. https://doi.org/10.1186/s13054-019-2569-4
Bryant, C., Szejda, K., Parekh, N., Deshpande, V., & Tse, B. (2019). A Survey of Consumer Perceptions of Plant-Based and Clean Meat in the USA, India, and China. Frontiers in Sustainable Food Systems, 3, 11. https://doi.org/10.3389/fsufs.2019.00011
Caliskan, S., Guney, Z., Sakhieva, R. G., Vasbieva, D. G., & Zaitseva, N. A. (2019). Teachers’ Views on the Availability of Web 2.0 Tools in Education. International Journal of Emerging Technologies in Learning (IJET), 14(22), 70. https://doi.org/10.3991/ijet.v14i22.11752
Chong, J., Soufan, O., Li, C., Caraus, I., Li, S., Bourque, G., Wishart, D. S., & Xia, J. (2018). MetaboAnalyst 4.0: Towards more transparent and integrative metabolomics analysis. Nucleic Acids Research, 46(W1), W486–W494. https://doi.org/10.1093/nar/gky310
Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. The Lancet Infectious Diseases, 20(5), 533–534. https://doi.org/10.1016/S1473-3099(20)30120-1
Feehan, L. M., Geldman, J., Sayre, E. C., Park, C., Ezzat, A. M., Yoo, J. Y., Hamilton, C. B., & Li, L. C. (2018). Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data. JMIR MHealth and UHealth, 6(8), e10527. https://doi.org/10.2196/10527
Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. https://doi.org/10.1016/j.ejor.2017.11.054
Galon, J., & Bruni, D. (2019). Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nature Reviews Drug Discovery, 18(3), 197–218. https://doi.org/10.1038/s41573-018-0007-y
Goldford, J. E., Lu, N., Baji?, D., Estrela, S., Tikhonov, M., Sanchez-Gorostiaga, A., Segrè, D., Mehta, P., & Sanchez, A. (2018). Emergent simplicity in microbial community assembly. Science, 361(6401), 469–474. https://doi.org/10.1126/science.aat1168
Harris, P. A., Taylor, R., Minor, B. L., Elliott, V., Fernandez, M., O’Neal, L., McLeod, L., Delacqua, G., Delacqua, F., Kirby, J., & Duda, S. N. (2019). The REDCap consortium: Building an international community of software platform partners. Journal of Biomedical Informatics, 95, 103208. https://doi.org/10.1016/j.jbi.2019.103208
Housten, A. J., Kamath, G. R., Bevers, T. B., Cantor, S. B., Dixon, N., Hite, A., Kallen, M. A., Leal, V. B., Li, L., & Volk, R. J. (2020). Does Animation Improve Comprehension of Risk Information in Patients with Low Health Literacy? A Randomized Trial. Medical Decision Making, 40(1), 17–28. https://doi.org/10.1177/0272989X19890296
Hu, H., Gu, J., Zhang, Z., Dai, J., & Wei, Y. (2018). Relation Networks for Object Detection. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3588–3597. https://doi.org/10.1109/CVPR.2018.00378
Jawed, S., Amin, H. U., Malik, A. S., & Faye, I. (2019). Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities. Frontiers in Behavioral Neuroscience, 13, 86. https://doi.org/10.3389/fnbeh.2019.00086
Kaya & Bilge. (2019). Deep Metric Learning: A Survey. Symmetry, 11(9), 1066. https://doi.org/10.3390/sym11091066
Kim, J. (2020). Learning and Teaching Online During Covid-19: Experiences of Student Teachers in an Early Childhood Education Practicum. International Journal of Early Childhood, 52(2), 145–158. https://doi.org/10.1007/s13158-020-00272-6
Lawson, A. P., & Mayer, R. E. (2021). The Power of Voice to Convey Emotion in Multimedia Instructional Messages. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-021-00282-y
Li, M., Lu, J., Chen, Z., & Amine, K. (2018). 30 Years of Lithium?Ion Batteries. Advanced Materials, 30(33), 1800561. https://doi.org/10.1002/adma.201800561
Li, S., Xu, L. D., & Zhao, S. (2018). 5G Internet of Things: A survey. Journal of Industrial Information Integration, 10, 1–9. https://doi.org/10.1016/j.jii.2018.01.005
Liu, C., & Elms, P. (2019). Animating student engagement: The impacts of cartoon instructional videos on learning experience. Research in Learning Technology, 27(0). https://doi.org/10.25304/rlt.v27.2124
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020). Deep Learning for Generic Object Detection: A Survey. International Journal of Computer Vision, 128(2), 261–318. https://doi.org/10.1007/s11263-019-01247-4
Lundervold, A. S., & Lundervold, A. (2019). An overview of deep learning in medical imaging focusing on MRI. Zeitschrift Für Medizinische Physik, 29(2), 102–127. https://doi.org/10.1016/j.zemedi.2018.11.002
Maredia, M. K., Reyes, B., Ba, M. N., Dabire, C. L., Pittendrigh, B., & Bello-Bravo, J. (2018). Can mobile phone-based animated videos induce learning and technology adoption among low-literate farmers? A field experiment in Burkina Faso. Information Technology for Development, 24(3), 429–460. https://doi.org/10.1080/02681102.2017.1312245
McCarthy, E., Tiu, M., & Li, L. (2018). Learning Math with Curious George and the Odd Squad: Transmedia in the Classroom. Technology, Knowledge and Learning, 23(2), 223–246. https://doi.org/10.1007/s10758-018-9361-4
Nainggolan, E. R., Asymar, H. H., Nalendra, A. R. A., Anton, Sulaeman, F., Sidik, Radiyah, U., & Susafarati. (2018). The Implementation of Augmented Reality as Learning Media in Introducing Animals for Early Childhood Education. 2018 6th International Conference on Cyber and IT Service Management (CITSM), 1–6. https://doi.org/10.1109/CITSM.2018.8674350
Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, C., Agha, M., & Agha, R. (2020). The socio-economic implications of the coronavirus pandemic (COVID-19): A review. International Journal of Surgery, 78, 185–193. https://doi.org/10.1016/j.ijsu.2020.04.018
O’Shea, T. J., Roy, T., & Clancy, T. C. (2018). Over-the-Air Deep Learning Based Radio Signal Classification. IEEE Journal of Selected Topics in Signal Processing, 12(1), 168–179. https://doi.org/10.1109/JSTSP.2018.2797022
Owen, C., Till, K., Weakley, J., & Jones, B. (2020). Testing methods and physical qualities of male age grade rugby union players: A systematic review. PLOS ONE, 15(6), e0233796. https://doi.org/10.1371/journal.pone.0233796
Oztemel, E., & Gursev, S. (2020). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31(1), 127–182. https://doi.org/10.1007/s10845-018-1433-8
Patra, J. K., Das, G., Fraceto, L. F., Campos, E. V. R., Rodriguez-Torres, M. del P., Acosta-Torres, L. S., Diaz-Torres, L. A., Grillo, R., Swamy, M. K., Sharma, S., Habtemariam, S., & Shin, H.-S. (2018). Nano based drug delivery systems: Recent developments and future prospects. Journal of Nanobiotechnology, 16(1), 71. https://doi.org/10.1186/s12951-018-0392-8
PhD student, The Department of the History and Source Studies of Central Asian People, Tashkent State Institute of Oriental Studies, Tashkent, Uzbekistan., Rakhmonqulovich*, K. N., Khudoyberdiyevich, D. A., & Professor, Doctor of Historical Sciences, The Department of the History and Source Studies of Central Asian People, Tashkent State Institute of Oriental Studies, Tashkent, Uzbekistan. (2019). Conflicting Views Regarding the Hadiths. International Journal of Innovative Technology and Exploring Engineering, 8(12), 2090–2094. https://doi.org/10.35940/ijitee.L3286.1081219
Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Computers & Education, 147, 103778. https://doi.org/10.1016/j.compedu.2019.103778
Rezaei, A., Schramm, G., Willekens, S. M. A., Delso, G., Van Laere, K., & Nuyts, J. (2019). A Quantitative Evaluation of Joint Activity and Attenuation Reconstruction in TOF PET/MR Brain Imaging. Journal of Nuclear Medicine, 60(11), 1649–1655. https://doi.org/10.2967/jnumed.118.220871
Rostam, N. A. P., & Malim, N. H. A. H. (2021). Text categorisation in Quran and Hadith: Overcoming the interrelation challenges using machine learning and term weighting. Journal of King Saud University - Computer and Information Sciences, 33(6), 658–667. https://doi.org/10.1016/j.jksuci.2019.03.007
Saad, W., Bennis, M., & Chen, M. (2020). A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems. IEEE Network, 34(3), 134–142. https://doi.org/10.1109/MNET.001.1900287
Safitri, D., Lestari, I., Maksum, A., Ibrahim, N., Marini, A., Zahari, M., & Iskandar, R. (2021). Web-Based Animation Video for Student Environmental Education at Elementary Schools. International Journal of Interactive Mobile Technologies (IJIM), 15(11), 66. https://doi.org/10.3991/ijim.v15i11.22023
Saraswati, D. L., Dinihari, Y., Nurrahmah, A., Sari, T. A., & Wiyanti, E. (2020). The application of using video scribe on geometry optics material. Journal of Physics: Conference Series, 1464(1), 012005. https://doi.org/10.1088/1742-6596/1464/1/012005
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. International Journal of Computer Vision, 128(2), 336–359. https://doi.org/10.1007/s11263-019-01228-7
Shaw, W. M. K. (2019). What is “Islamic” Art?: Between Religion and Perception (1st ed.). Cambridge University Press. https://doi.org/10.1017/9781108622967
Shinta, A., Hanif*, M., Gunarhadi, G., & Roemintoyo, R. (2019). Motion Graphic Animation Videos to Improve the Learning Outcomes of Elementary School Students. European Journal of Educational Research, 8(4), 1245–1255. https://doi.org/10.12973/eu-jer.8.4.1245
Siarohin, A., Lathuiliere, S., Tulyakov, S., Ricci, E., & Sebe, N. (2019). Animating Arbitrary Objects via Deep Motion Transfer. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2372–2381. https://doi.org/10.1109/CVPR.2019.00248
Son, C., Hegde, S., Smith, A., Wang, X., & Sasangohar, F. (2020). Effects of COVID-19 on College Students’ Mental Health in the United States: Interview Survey Study. Journal of Medical Internet Research, 22(9), e21279. https://doi.org/10.2196/21279
Springmann, M., Clark, M., Mason-D’Croz, D., Wiebe, K., Bodirsky, B. L., Lassaletta, L., de Vries, W., Vermeulen, S. J., Herrero, M., Carlson, K. M., Jonell, M., Troell, M., DeClerck, F., Gordon, L. J., Zurayk, R., Scarborough, P., Rayner, M., Loken, B., Fanzo, J., … Willett, W. (2018). Options for keeping the food system within environmental limits. Nature, 562(7728), 519–525. https://doi.org/10.1038/s41586-018-0594-0
Thomford, N., Senthebane, D., Rowe, A., Munro, D., Seele, P., Maroyi, A., & Dzobo, K. (2018). Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery. International Journal of Molecular Sciences, 19(6), 1578. https://doi.org/10.3390/ijms19061578
Tscholl, D. W., Handschin, L., Neubauer, P., Weiss, M., Seifert, B., Spahn, D. R., & Noethiger, C. B. (2018). Using an animated patient avatar to improve perception of vital sign information by anaesthesia professionals. British Journal of Anaesthesia, 121(3), 662–671. https://doi.org/10.1016/j.bja.2018.04.024
Tu, M. (2018). An exploratory study of Internet of Things (IoT) adoption intention in logistics and supply chain management: A mixed research approach. The International Journal of Logistics Management, 29(1), 131–151. https://doi.org/10.1108/IJLM-11-2016-0274
Wang, C., Horby, P. W., Hayden, F. G., & Gao, G. F. (2020). A novel coronavirus outbreak of global health concern. The Lancet, 395(10223), 470–473. https://doi.org/10.1016/S0140-6736(20)30185-9
Wu, Y., Yuan, C.-H., & Pan, C.-I. (2018). Entrepreneurship Education: An Experimental Study with Information and Communication Technology. Sustainability, 10(3), 691. https://doi.org/10.3390/su10030691
Xu, Q., Xu, Y., Sun, H., Chan, Q., Shi, K., Song, A., & Wang, W. (2018). Quantitative intravoxel incoherent motion parameters derived from whole-tumor volume for assessing pathological complete response to neoadjuvant chemotherapy in locally advanced rectal cancer: IVIM Assessing pCR in Rectal Cancer. Journal of Magnetic Resonance Imaging, 48(1), 248–258. https://doi.org/10.1002/jmri.25931
Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019). Object Detection With Deep Learning: A Review. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212–3232. https://doi.org/10.1109/TNNLS.2018.2876865
Zhong, B.-L., Luo, W., Li, H.-M., Zhang, Q.-Q., Liu, X.-G., Li, W.-T., & Li, Y. (2020). Knowledge, attitudes, and practices towards COVID-19 among Chinese residents during the rapid rise period of the COVID-19 outbreak: A quick online cross-sectional survey. International Journal of Biological Sciences, 16(10), 1745–1752. https://doi.org/10.7150/ijbs.45221
Authors
Copyright (c) 2024 Martias Martias, Saleh bin Nur, Azamat Nazarov

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.