A Study of The Attitudes and Motivations of Learning of Junior High School Students on The Technological Process in Islamic Studies
Article Sidebar
-
Islamic Studies, Learning Attitudes and Motivations, Technological Processes
Abstract
The influence of technological processes is something that can no longer be separated from human life today, including the field of education. Students’ responses and acceptances of the technological process determine the extent to which this technology can be used in the learning process. The purpose of this study is to find out how the influence of attitudes and motivations for learning from junior high school students on the technological process in Islamic studies. This research uses quantitative methods with a survey model. The tool used in this survey model is a questionnaire on a google form. The results found in this study are that learning attitudes and motivations in students largely determine developments in the technological process in Islamic studies. From this research, it can be concluded that the existence of a technological process in Islamic studies will shape the character of students for the better and increase student learning motivation. The limitation of this study is that researchers only conduct research on the attitudes and learning motivations of junior high school students regarding technological processes in Islamic studies. Researchers hope that subsequent researchers can conduct similar studies at the level of education and other subjects.
Full text article
References
Ahmadi, Dr. M. R. & Guilan University, Guilan, Iran. (2018). The Use of Technology in English Language Learning: A Literature Review. International Journal of Research in English Education, 3(2), 115–125. https://doi.org/10.29252/ijree.3.2.115
Almerich, G., Suárez-Rodríguez, J., Díaz-García, I., & Orellana, N. (2019). Estructura de las competencias del siglo XXI en alumnado del ámbito educativo. Factores personales influyentes. Educación XX1, 23(1). https://doi.org/10.5944/educxx1.23853
Alverson, C. Y., Lindstrom, L. E., & Hirano, K. A. (2019). High School to College: Transition Experiences of Young Adults With Autism. Focus on Autism and Other Developmental Disabilities, 34(1), 52–64. https://doi.org/10.1177/1088357615611880
Andryukhina, L. M., Sadovnikova, N. O., Utkina, S. N., & Mirzaahmedov, A. M. (2020). Digitalisation of Professional Education: Prospects and Invisible Barriers. The Education and Science Journal, 22(3), 116–147. https://doi.org/10.17853/1994-5639-2020-3-116-147
Asrial, A., Maison, M., & Perdana, R. (2022). A study of junior high school students’ attitudes and learning motivation on science process. Cypriot Journal of Educational Sciences, 17(8), 2745–2759. https://doi.org/10.18844/cjes.v17i8.7816
Bond, M., Marín, V. I., Dolch, C., Bedenlier, S., & Zawacki-Richter, O. (2018). Digital transformation in German higher education: Student and teacher perceptions and usage of digital media. International Journal of Educational Technology in Higher Education, 15(1), 48. https://doi.org/10.1186/s41239-018-0130-1
Brooks, S., Wang, X., & Schneider, C. (2020). Technology Addictions and Technostress: An Examination of the U.S. and China. Journal of Organizational and End User Computing, 32(2), 1–19. https://doi.org/10.4018/JOEUC.2020040101
Cheung, A. H., Zuckerbrot, R. A., Jensen, P. S., Laraque, D., Stein, R. E. K., GLAD-PC STEERING GROUP, Levitt, A., Birmaher, B., Campo, J., Clarke, G., Emslie, G., Kaufman, M., Kelleher, K. J., Kutcher, S., Malus, M., Sacks, D., Waslick, B., & Sarvet, B. (2018). Guidelines for Adolescent Depression in Primary Care (GLAD-PC): Part II. Treatment and Ongoing Management. Pediatrics, 141(3), e20174082. https://doi.org/10.1542/peds.2017-4082
Dou, D., & Shek, D. T. L. (2022). Hong Kong high school students’ perceptions of the new secondary school curriculum. Frontiers in Pediatrics, 10, 881515. https://doi.org/10.3389/fped.2022.881515
Edwards, O. W., McKinzie Bennett, C., & Johnson, B. (2019). School Consultation to Counter Stereotype Threat. Journal of Educational and Psychological Consultation, 29(2), 188–205. https://doi.org/10.1080/10474412.2018.1482218
Eldridge, A., Piernas, C., Illner, A.-K., Gibney, M., Gurinovi?, M., de Vries, J., & Cade, J. (2018). Evaluation of New Technology-Based Tools for Dietary Intake Assessment—An ILSI Europe Dietary Intake and Exposure Task Force Evaluation. Nutrients, 11(1), 55. https://doi.org/10.3390/nu11010055
Falloon, G. (2020). From digital literacy to digital competence: The teacher digital competency (TDC) framework. Educational Technology Research and Development, 68(5), 2449–2472. https://doi.org/10.1007/s11423-020-09767-4
Fernandez-Cassi, X., Timoneda, N., Martínez-Puchol, S., Rusiñol, M., Rodriguez-Manzano, J., Figuerola, N., Bofill-Mas, S., Abril, J. F., & Girones, R. (2018). Metagenomics for the study of viruses in urban sewage as a tool for public health surveillance. Science of The Total Environment, 618, 870–880. https://doi.org/10.1016/j.scitotenv.2017.08.249
Gallego-Arrufat, M.-J., Torres-Hernández, N., & Pessoa, T. (2019). Competence of future teachers in the digital security area. Comunicar, 27(61), 57–67. https://doi.org/10.3916/C61-2019-05
Hidayat, M. S., Kadir, A., & Herman. (2018). Comparison Results of Islamic Education Learning Using Teams Games Tournament (TGT) in Indonesia and Thailand. IOP Conference Series: Earth and Environmental Science, 175, 012150. https://doi.org/10.1088/1755-1315/175/1/012150
Huang, C. L., Zhang, S., & Yang, S. C. (2020). How students react to different cyberbullying events: Past experience, judgment, perceived seriousness, helping behavior and the effect of online disinhibition. Computers in Human Behavior, 110, 106338. https://doi.org/10.1016/j.chb.2020.106338
Jeong, D., Presseau, J., ElChamaa, R., Naumann, D. N., Mascaro, C., Luconi, F., Smith, K. M., & Kitto, S. (2018). Barriers and Facilitators to Self-Directed Learning in Continuing Professional Development for Physicians in Canada: A Scoping Review. Academic Medicine, 93(8), 1245–1254. https://doi.org/10.1097/ACM.0000000000002237
Johnson, J. M., & Khoshgoftaar, T. M. (2019). Survey on deep learning with class imbalance. Journal of Big Data, 6(1), 27. https://doi.org/10.1186/s40537-019-0192-5
Kulin, M., Kazaz, T., Moerman, I., & De Poorter, E. (2018). End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications. IEEE Access, 6, 18484–18501. https://doi.org/10.1109/ACCESS.2018.2818794
Liu, F., Nair, C., Khurana, G., Watanabe, A., DeProspo, B. H., Kubo, A., Lin, C. P., Makita, T., Watanabe, N., & Tummala, R. R. (2019). Next Generation of 2-7 Micron Ultra-Small Microvias for 2.5D Panel Redistribution Layer by Using Laser and Photolithography Technologies. 2019 IEEE 69th Electronic Components and Technology Conference (ECTC), 924–930. https://doi.org/10.1109/ECTC.2019.00144
Lobovikov-Katz, A. (2019). Methodology for Spatial-Visual Literacy (MSVL) in Heritage Education. Revista Electrónica Interuniversitaria de Formación Del Profesorado, 22(1). https://doi.org/10.6018/reifop.22.1.358671
Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018). Machine learning for internet of things data analysis: A survey. Digital Communications and Networks, 4(3), 161–175. https://doi.org/10.1016/j.dcan.2017.10.002
Malka, R., Villwock, J., Faucett, E. A., & Bowe, S. (2021). Podcast?Based Learning in Otolaryngology: Availability, Breadth, and Comparison with Other Specialties. The Laryngoscope, 131(7). https://doi.org/10.1002/lary.29349
Mayer, R. E. (2019). Computer Games in Education. Annual Review of Psychology, 70(1), 531–549. https://doi.org/10.1146/annurev-psych-010418-102744
Moto, S., Ratanaolarn, T., Tuntiwongwanich, S., & Pimdee, P. (2018). A Thai Junior High School Students’ 21st Century Information Literacy, Media Literacy, and ICT Literacy Skills Factor Analysis. International Journal of Emerging Technologies in Learning (IJET), 13(09), 87. https://doi.org/10.3991/ijet.v13i09.8355
Narvaez Rojas, C., Alomia Peñafiel, G. A., Loaiza Buitrago, D. F., & Tavera Romero, C. A. (2021). Society 5.0: A Japanese Concept for a Superintelligent Society. Sustainability, 13(12), 6567. https://doi.org/10.3390/su13126567
Pei, L., & Wu, H. (2019). Does online learning work better than offline learning in undergraduate medical education? A systematic review and meta-analysis. Medical Education Online, 24(1), 1666538. https://doi.org/10.1080/10872981.2019.1666538
Perifanou, M., Economides, A. A., & Tzafilkou, K. (2021). Teachers’ Digital Skills Readiness During COVID-19 Pandemic. International Journal of Emerging Technologies in Learning (IJET), 16(08), 238. https://doi.org/10.3991/ijet.v16i08.21011
Poto?an, V., Mulej, M., & Nedelko, Z. (2021). Society 5.0: Balancing of Industry 4.0, economic advancement and social problems. Kybernetes, 50(3), 794–811. https://doi.org/10.1108/K-12-2019-0858
Prakash, C., Kumar, R., & Mittal, N. (2018). Recent developments in human gait research: Parameters, approaches, applications, machine learning techniques, datasets and challenges. Artificial Intelligence Review, 49(1), 1–40. https://doi.org/10.1007/s10462-016-9514-6
Puspitarini, Y. D., & Hanif, M. (2019). Using Learning Media to Increase Learning Motivation in Elementary School. Anatolian Journal of Education, 4(2), 53–60. https://doi.org/10.29333/aje.2019.426a
Rafiola, R. H., Setyosari, P., Radjah, C. L., & Ramli, M. (2020). The Effect of Learning Motivation, Self-Efficacy, and Blended Learning on Students’ Achievement in The Industrial Revolution 4.0. International Journal of Emerging Technologies in Learning (IJET), 15(08), 71. https://doi.org/10.3991/ijet.v15i08.12525
Rahim Taleqani, A., Hough, J., & Nygard, K. E. (2019). Public Opinion on Dockless Bike Sharing: A Machine Learning Approach. Transportation Research Record: Journal of the Transportation Research Board, 2673(4), 195–204. https://doi.org/10.1177/0361198119838982
Ridhwan, Nurdin, A., & Samad, S. A. A. (2018). Dynamics of Islamic Education in The Land of Bugis: Growth, Development and Typology Pesantren in Bone. IOP Conference Series: Earth and Environmental Science, 175, 012158. https://doi.org/10.1088/1755-1315/175/1/012158
Sahin, D., & Yilmaz, R. M. (2020). The effect of Augmented Reality Technology on middle school students’ achievements and attitudes towards science education. Computers & Education, 144, 103710. https://doi.org/10.1016/j.compedu.2019.103710
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Sclove, R. (2020). Democracy and Technology: An Interview with Richard Sclove from Beth Simone Noveck. Digital Government: Research and Practice, 1(1), 1–6. https://doi.org/10.1145/3368273
Shakarami, A., Ghobaei-Arani, M., & Shahidinejad, A. (2020). A survey on the computation offloading approaches in mobile edge computing: A machine learning-based perspective. Computer Networks, 182, 107496. https://doi.org/10.1016/j.comnet.2020.107496
Silber?Varod, V., Eshet?Alkalai, Y., & Geri, N. (2019). Tracing research trends of 21st?century learning skills. British Journal of Educational Technology, 50(6), 3099–3118. https://doi.org/10.1111/bjet.12753
So?tysik-Piorunkiewicz, A., & Zdonek, I. (2021). How Society 5.0 and Industry 4.0 Ideas Shape the Open Data Performance Expectancy. Sustainability, 13(2), 917. https://doi.org/10.3390/su13020917
Suharno, Pambudi, N. A., & Harjanto, B. (2020). Vocational education in Indonesia: History, development, opportunities, and challenges. Children and Youth Services Review, 115, 105092. https://doi.org/10.1016/j.childyouth.2020.105092
Sukmana, R. (2020). Critical assessment of Islamic endowment funds (Waqf) literature: Lesson for government and future directions. Heliyon, 6(10), e05074. https://doi.org/10.1016/j.heliyon.2020.e05074
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018). A Survey on Deep Transfer Learning. In V. K?rková, Y. Manolopoulos, B. Hammer, L. Iliadis, & I. Maglogiannis (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2018 (Vol. 11141, pp. 270–279). Springer International Publishing. https://doi.org/10.1007/978-3-030-01424-7_27
Tang, X., Tang, S., Ren, Z., & Wong, D. F. K. (2019). Prevalence of depressive symptoms among adolescents in secondary school in mainland China: A systematic review and meta-analysis. Journal of Affective Disorders, 245, 498–507. https://doi.org/10.1016/j.jad.2018.11.043
Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0 – A Glimpse. Procedia Manufacturing, 20, 233–238. https://doi.org/10.1016/j.promfg.2018.02.034
van Dijk-Wesselius, J. E., van den Berg, A. E., Maas, J., & Hovinga, D. (2020). Green Schoolyards as Outdoor Learning Environments: Barriers and Solutions as Experienced by Primary School Teachers. Frontiers in Psychology, 10, 2919. https://doi.org/10.3389/fpsyg.2019.02919
Vidakis, N., & Charitakis, S. (2018). Designing the Learning Process: The IOLAOS Platform. Proceedings of the 10th International Conference on Subject-Oriented Business Process Management - S-BPM One ‘18, 1–11. https://doi.org/10.1145/3178248.3178254
Walsh, N. A., Rodriguez, N., Repa, L. M., King, E., & Garland, S. N. (2020). Associations between device use before bed, mood disturbance, and insomnia symptoms in young adults. Sleep Health, 6(6), 822–827. https://doi.org/10.1016/j.sleh.2020.04.004
Wiyono, B. B., Indreswari, H., & Prastiawan, A. (2021). The Use of Communication Technology in Establishing Community Relationships Applied by School Administration Staff, in Relation to Their Education Level and Age. 2021 3rd International Conference on Computer Communication and the Internet (ICCCI), 214–219. https://doi.org/10.1109/ICCCI51764.2021.9486784
Wrahatnolo, T. & Munoto. (2018). 21 st centuries skill implication on educational system. IOP Conference Series: Materials Science and Engineering, 296, 012036. https://doi.org/10.1088/1757-899X/296/1/012036
Yang, C., Chan, M.-K., & Ma, T.-L. (2020). School-wide social emotional learning (SEL) and bullying victimization: Moderating role of school climate in elementary, middle, and high schools. Journal of School Psychology, 82, 49–69. https://doi.org/10.1016/j.jsp.2020.08.002
Yuen, A. H. K., Cheng, M., & Chan, F. H. F. (2019). Student satisfaction with learning management systems: A growth model of belief and use. British Journal of Educational Technology, 50(5), 2520–2535. https://doi.org/10.1111/bjet.12830
Zhang, S., Yang, X., Gertsen, H., Peters, P., Salánki, T., & Geissen, V. (2018). A simple method for the extraction and identification of light density microplastics from soil. Science of The Total Environment, 616–617, 1056–1065. https://doi.org/10.1016/j.scitotenv.2017.10.213
Zhou, Z., Shek, D. T. L., Zhu, X., & Dou, D. (2020). Positive Youth Development and Adolescent Depression: A Longitudinal Study Based on Mainland Chinese High School Students. International Journal of Environmental Research and Public Health, 17(12), 4457. https://doi.org/10.3390/ijerph17124457
Zuckerbrot, R. A., Cheung, A., Jensen, P. S., Stein, R. E. K., Laraque, D., GLAD-PC STEERING GROUP, Levitt, A., Birmaher, B., Campo, J., Clarke, G., Emslie, G., Kaufman, M., Kelleher, K. J., Kutcher, S., Malus, M., Sacks, D., Waslick, B., & Sarvet, B. (2018). Guidelines for Adolescent Depression in Primary Care (GLAD-PC): Part I. Practice Preparation, Identification, Assessment, and Initial Management. Pediatrics, 141(3), e20174081. https://doi.org/10.1542/peds.2017-4081
Authors
Copyright (c) 2023 Alessandra Oldani, Forcella Chiara , Mozzi Monica
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
