The Role of Technology in Indonesian Education at Present

Salma Rabani (1), Annisaul Khairat (2), Xie Guilin (3), Deng Jiao (4)
(1) Universitas Islam Negeri Mahmud Yunus Batusangkar, Indonesia,
(2) Universitas Islam Negeri Mahmud Yunus Batusangkar, Indonesia,
(3) University of Science and Technology of Hanoi, Viet Nam,
(4) University Sains Malaysia, Malaysia

Abstract

Technology has become increasingly important in education, including in Indonesia. The implementation of technology in education is expected to improve the quality of education and help reduce the gap in education access across regions. However, several factors have hindered the implementation of technology in education in Indonesia, such as limited infrastructure, inadequate training and development for teachers and lecturers, and digital divides between regions. Despite these challenges, the use of technology in education has several benefits, including improving the quality of learning and motivation of students and increasing the efficiency of administrative tasks for teachers and lecturers. To improve the implementation of technology in education in Indonesia, efforts are needed to develop supporting infrastructure, provide training and development for educators, and establish clear policies and regulations. Collaboration between the government, educational institutions, and the private sector is also necessary. This article discusses the role of technology in education in Indonesia and the factors that influence its implementation, drawing on various studies and research.

Full text article

Generated from XML file

References

Albrecht, E., & Chin, K. J. (2020). Advances in regional anaesthesia and acute pain management: A narrative review. Anaesthesia, 75(S1). https://doi.org/10.1111/anae.14868

Arora, S., Singh, H., Sharma, M., Sharma, S., & Anand, P. (2019). A New Hybrid Algorithm Based on Grey Wolf Optimization and Crow Search Algorithm for Unconstrained Function Optimization and Feature Selection. IEEE Access, 7, 26343–26361. https://doi.org/10.1109/ACCESS.2019.2897325

Bai, B., Guo, Z., Zhou, C., Zhang, W., & Zhang, J. (2021). Application of adaptive reliability importance sampling-based extended domain PSO on single mode failure in reliability engineering. Information Sciences, 546, 42–59. https://doi.org/10.1016/j.ins.2020.07.069

Caniëls, M. C. J., Chiocchio, F., & Van Loon, N. P. A. A. (2019). Collaboration in project teams: The role of mastery and performance climates. International Journal of Project Management, 37(1), 1–13. https://doi.org/10.1016/j.ijproman.2018.09.006

Chen, Y., Zhong, H., Wang, J., Wan, X., Li, Y., Pan, W., Li, N., & Tang, B. (2019). Catalase-like metal–organic framework nanoparticles to enhance radiotherapy in hypoxic cancer and prevent cancer recurrence. Chemical Science, 10(22), 5773–5778. https://doi.org/10.1039/C9SC00747D

Gao, Z., Dang, W., Wang, X., Hong, X., Hou, L., Ma, K., & Perc, M. (2021). Complex networks and deep learning for EEG signal analysis. Cognitive Neurodynamics, 15(3), 369–388. https://doi.org/10.1007/s11571-020-09626-1

Golden, T. D., & Gajendran, R. S. (2019). Unpacking the Role of a Telecommuter’s Job in Their Performance: Examining Job Complexity, Problem Solving, Interdependence, and Social Support. Journal of Business and Psychology, 34(1), 55–69. https://doi.org/10.1007/s10869-018-9530-4

Hassan, M. H., Houssein, E. H., Mahdy, M. A., & Kamel, S. (2021). An improved Manta ray foraging optimizer for cost-effective emission dispatch problems. Engineering Applications of Artificial Intelligence, 100, 104155. https://doi.org/10.1016/j.engappai.2021.104155

He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30–36. https://doi.org/10.1038/s41591-018-0307-0

Hu, L., He, S., Han, Z., Xiao, H., Su, S., Weng, M., & Cai, Z. (2019). Monitoring housing rental prices based on social media:An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies. Land Use Policy, 82, 657–673. https://doi.org/10.1016/j.landusepol.2018.12.030

Huseien, G. F., & Shah, K. W. (2020). Durability and life cycle evaluation of self-compacting concrete containing fly ash as GBFS replacement with alkali activation. Construction and Building Materials, 235, 117458. https://doi.org/10.1016/j.conbuildmat.2019.117458

Jiang, L., Zhang, L. J., & May, S. (2019). Implementing English-medium instruction (EMI) in China: Teachers’ practices and perceptions, and students’ learning motivation and needs. International Journal of Bilingual Education and Bilingualism, 22(2), 107–119. https://doi.org/10.1080/13670050.2016.1231166

Low, E. S., Ong, P., & Cheah, K. C. (2019). Solving the optimal path planning of a mobile robot using improved Q-learning. Robotics and Autonomous Systems, 115, 143–161. https://doi.org/10.1016/j.robot.2019.02.013

Penconek, T., Tate, K., Bernardes, A., Lee, S., Micaroni, S. P. M., Balsanelli, A. P., De Moura, A. A., & Cummings, G. G. (2021). Determinants of nurse manager job satisfaction: A systematic review. International Journal of Nursing Studies, 118, 103906. https://doi.org/10.1016/j.ijnurstu.2021.103906

Peng, H., Wang, H., Du, B., Bhuiyan, M. Z. A., Ma, H., Liu, J., Wang, L., Yang, Z., Du, L., Wang, S., & Yu, P. S. (2020). Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting. Information Sciences, 521, 277–290. https://doi.org/10.1016/j.ins.2020.01.043

Pfattheicher, S., Nielsen, Y. A., & Thielmann, I. (2022). Prosocial behavior and altruism: A review of concepts and definitions. Current Opinion in Psychology, 44, 124–129. https://doi.org/10.1016/j.copsyc.2021.08.021

Salminen, J., Hopf, M., Chowdhury, S. A., Jung, S., Almerekhi, H., & Jansen, B. J. (2020). Developing an online hate classifier for multiple social media platforms. Human-Centric Computing and Information Sciences, 10(1), 1. https://doi.org/10.1186/s13673-019-0205-6

Song, J., She, J., Chen, D., & Pan, F. (2020). Latest research advances on magnesium and magnesium alloys worldwide. Journal of Magnesium and Alloys, 8(1), 1–41. https://doi.org/10.1016/j.jma.2020.02.003

Van Doren, J., Arns, M., Heinrich, H., Vollebregt, M. A., Strehl, U., & K. Loo, S. (2019). Sustained effects of neurofeedback in ADHD: A systematic review and meta-analysis. European Child & Adolescent Psychiatry, 28(3), 293–305. https://doi.org/10.1007/s00787-018-1121-4

Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003

Wang, F., Wang, H., Wang, H., Li, G., & Situ, G. (2019). Learning from simulation: An end-to-end deep-learning approach for computational ghost imaging. Optics Express, 27(18), 25560. https://doi.org/10.1364/OE.27.025560

Wang, S., Chen, X., & Szolnoki, A. (2019). Exploring optimal institutional incentives for public cooperation. Communications in Nonlinear Science and Numerical Simulation, 79, 104914. https://doi.org/10.1016/j.cnsns.2019.104914

Wu, M., Chen, Y., Lin, H., Zhao, L., Shen, L., Li, R., Xu, Y., Hong, H., & He, Y. (2020). Membrane fouling caused by biological foams in a submerged membrane bioreactor: Mechanism insights. Water Research, 181, 115932. https://doi.org/10.1016/j.watres.2020.115932

Yang, Z., Yu, W., Liang, P., Guo, H., Xia, L., Zhang, F., Ma, Y., & Ma, J. (2019). Deep transfer learning for military object recognition under small training set condition. Neural Computing and Applications, 31(10), 6469–6478. https://doi.org/10.1007/s00521-018-3468-3

Zhang, Y., & Jin, Z. (2020). Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems. Expert Systems with Applications, 148, 113246. https://doi.org/10.1016/j.eswa.2020.113246

Authors

Salma Rabani
salmarabani@gmail.com (Primary Contact)
Annisaul Khairat
Xie Guilin
Deng Jiao
Rabani, S., Khairat, A., Guilin, X., & Jiao, D. (2023). The Role of Technology in Indonesian Education at Present. Journal of Computer Science Advancements, 1(2), 85–91. https://doi.org/10.70177/jsca.v1i2.403

Article Details

Implementation of a Cloud-Based E-Learning System for Integrated Learning in Higher Education

Parini Parini, Sri Nur Rahmi, Fransiskus Ghunu Bili, Ahmya Ayaka
Abstract View : 39
Download :17

Use of Virtual Reality Technology for Learning Mechanical Skills

Anto Susilo, Ling Barra, Yuanyuan Wang
Abstract View : 93
Download :38