Utilisation of Kinemaster Application as Thematic Learning Media Development in Elementary School
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Background. Thematic learning at Al-Bina 01 Koto Baru IT Elementary School during the co-19 pandemic has decreased in terms of quality and in the learning process. After research, this happened because of the lack of effectiveness of the learning process and the teacher's lack of optimization in conveying information related to learning material.
Purpose. The purpose of this research is to create learning media for students so that teachers are easy to teach lessons and lessons become more interesting so that students are more enthusiastic in learning.
Method. The method applied in this research is based on the use of descriptive research methods. By using data analysis on the comparison of student grades carried out through collecting samples of thematic learning scores of a group of students at Al-Bina 01 Koto Baru Integrated Islamic Elementary School during face-to-face learning before the covid-19 pandemic and online learning after the covid-19 pandemic took place.
Results. The results of this study After the operation of this thematic learning video media to students, the survey proved that with the learning media video, the quality of student learning outcomes at Al-Bina 01 Koto Baru IT Elementary School has increased.
Conclusion. Based on the research and explanations above, it can be concluded that the positive and negative impacts of the covid-19 pandemic are very influential on the world of education. Especially the impact on the quality of education in Indonesia which occurs due to the forced change in the learning system from face-to-face to online learning throughout Indonesia.
Abbas, J., Aman, J., Nurunnabi, M., & Bano, S. (2019). The Impact of Social Media on Learning Behavior for Sustainable Education: Evidence of Students from Selected Universities in Pakistan. Sustainability, 11(6), 1683. https://doi.org/10.3390/su11061683
Baber, H. (2020). Determinants of Students’ Perceived Learning Outcome and Satisfaction in Online Learning during the Pandemic of COVID19. Journal of Education and e-Learning Research, 7(3), 285–292. https://doi.org/10.20448/journal.509.2020.73.285.292
Beijaard, D. (2019). Teacher learning as identity learning: Models, practices, and topics. Teachers and Teaching, 25(1), 1–6. https://doi.org/10.1080/13540602.2019.1542871
Blanco Cano, E., & García-Martín, J. (2021). El impacto del aprendizaje-servicio (ApS) en diversas variables psicoeducativas del alumnado universitario: Las actitudes cívicas, el pensamiento crítico, las habilidades de trabajo en grupo, la empatía y el autoconcepto. Una revisión sistemática. Revista Complutense de Educación, 32(4), 639–649. https://doi.org/10.5209/rced.70939
Cabero-Almenara, J., & Palacios-Rodríguez, A. (2021). La evaluación de la educación virtual: Las e-actividades. RIED. Revista Iberoamericana de Educación a Distancia, 24(2), 169. https://doi.org/10.5944/ried.24.2.28994
Chan, S. C. H., Wan, C. L. J., & Ko, S. (2019). Interactivity, active collaborative learning, and learning performance: The moderating role of perceived fun by using personal response systems. The International Journal of Management Education, 17(1), 94–102. https://doi.org/10.1016/j.ijme.2018.12.004
Chao, S.-H., Jiang, J., Hsu, C.-H., Chiang, Y.-T., Ng, E., & Fang, W.-T. (2020). Technology-Enhanced Learning for Graduate Students: Exploring the Correlation of Media Richness and Creativity of Computer-Mediated Communication and Face-to-Face Communication. Applied Sciences, 10(5), 1602. https://doi.org/10.3390/app10051602
Clarke, A. K., & Unsworth, W. P. (2020). A happy medium: The synthesis of medicinally important medium-sized rings via ring expansion. Chemical Science, 11(11), 2876–2881. https://doi.org/10.1039/D0SC00568A
Dewantara, I. P. M. (2020). Curriculum changes in Indonesia: Teacher constraints and students of prospective teachers’ readiness in the implementation of thematic learning at low grade primary school. ?lkö?retim Online, 1047–1060. https://doi.org/10.17051/ilkonline.2020.696686
Dos Santos, P. H., Neves, S. M., Sant’Anna, D. O., Oliveira, C. H. D., & Carvalho, H. D. (2019). The analytic hierarchy process supporting decision making for sustainable development: An overview of applications. Journal of Cleaner Production, 212, 119–138. https://doi.org/10.1016/j.jclepro.2018.11.270
Echevarría, Y., Blanco, C., & Sánchez, L. (2019). Learning human-understandable models for the health assessment of Li-ion batteries via Multi-Objective Genetic Programming. Engineering Applications of Artificial Intelligence, 86, 1–10. https://doi.org/10.1016/j.engappai.2019.08.013
Elghaish, F., Matarneh, S. T., & Alhusban, M. (2022). The application of “deep learning” in construction site management: Scientometric, thematic and critical analysis. Construction Innovation, 22(3), 580–603. https://doi.org/10.1108/CI-10-2021-0195
Fischer, B. M. (2020). Developing and sustaining creativity: Creative processes in Canadian junior college teachers. Thinking Skills and Creativity, 38, 100754. https://doi.org/10.1016/j.tsc.2020.100754
Forcael, E., Garces, G., & Orozco, F. (2022). Relationship Between Professional Competencies Required by Engineering Students According to ABET and CDIO and Teaching–Learning Techniques. IEEE Transactions on Education, 65(1), 46–55. https://doi.org/10.1109/TE.2021.3086766
Guillén-Gámez, F. D., Mayorga-Fernández, M. J., & Contreras-Rosado, J. A. (2021). Incidence of Gender in the Digital Competence of Higher Education Teachers in Research Work: Analysis with Descriptive and Comparative Methods. Education Sciences, 11(3), 98. https://doi.org/10.3390/educsci11030098
Guo, Q., Liu, Y., Zhao, Y., Li, B., & Yao, Q. (2019). Improved resonance reliability and global sensitivity analysis of multi-span pipes conveying fluid based on active learning Kriging model. International Journal of Pressure Vessels and Piping, 170, 92–101. https://doi.org/10.1016/j.ijpvp.2019.01.016
Hamel, C., Michaud, A., Thuku, M., Skidmore, B., Stevens, A., Nussbaumer-Streit, B., & Garritty, C. (2021). Defining Rapid Reviews: A systematic scoping review and thematic analysis of definitions and defining characteristics of rapid reviews. Journal of Clinical Epidemiology, 129, 74–85. https://doi.org/10.1016/j.jclinepi.2020.09.041
Hassan, M. M., Gumaei, A., Alsanad, A., Alrubaian, M., & Fortino, G. (2020). A hybrid deep learning model for efficient intrusion detection in big data environment. Information Sciences, 513, 386–396. https://doi.org/10.1016/j.ins.2019.10.069
Hermansson, C., Jonsson, S., & Liu, L. (2022). The medium is the message: Learning channels, financial literacy, and stock market participation. International Review of Financial Analysis, 79, 101996. https://doi.org/10.1016/j.irfa.2021.101996
Howitz, W. J., Thane, T. A., Frey, T. L., Wang, X. S., Gonzales, J. C., Tretbar, C. A., Seith, D. D., Saluga, S. J., Lam, S., Nguyen, M. M., Tieu, P., Link, R. D., & Edwards, K. D. (2020). Online in No Time: Design and Implementation of a Remote Learning First Quarter General Chemistry Laboratory and Second Quarter Organic Chemistry Laboratory. Journal of Chemical Education, 97(9), 2624–2634. https://doi.org/10.1021/acs.jchemed.0c00895
Hudon, A., Beaudoin, M., Phraxayavong, K., Dellazizzo, L., Potvin, S., & Dumais, A. (2022). Implementation of a machine learning algorithm for automated thematic annotations in avatar: A linear support vector classifier approach. Health Informatics Journal, 28(4), 146045822211424. https://doi.org/10.1177/14604582221142442
Imran, Ghaffar, Z., Alshahrani, A., Fayaz, M., Alghamdi, A. M., & Gwak, J. (2021). A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges. Electronics, 10(8), 880. https://doi.org/10.3390/electronics10080880
Jain, P. K., Pamula, R., & Srivastava, G. (2021). A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews. Computer Science Review, 41, 100413. https://doi.org/10.1016/j.cosrev.2021.100413
Kim, E., Park, H., & Jang, J. (2019). Development of a Class Model for Improving Creative Collaboration Based on the Online Learning System (Moodle) in Korea. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), 67. https://doi.org/10.3390/joitmc5030067
Kim, J. (2021). The Meaning of Numbers: Effect of Social Media Engagement Metrics in Risk Communication. Communication Studies, 72(2), 195–213. https://doi.org/10.1080/10510974.2020.1819842
Lauriola, I., Lavelli, A., & Aiolli, F. (2022). An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools. Neurocomputing, 470, 443–456. https://doi.org/10.1016/j.neucom.2021.05.103
Li, D., Zhi, B., Schoenherr, T., & Wang, X. (2023). Developing capabilities for supply chain resilience in a post-COVID world: A machine learning-based thematic analysis. IISE Transactions, 1–21. https://doi.org/10.1080/24725854.2023.2176951
Loey, M., Manogaran, G., Taha, M. H. N., & Khalifa, N. E. M. (2021). A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement, 167, 108288. https://doi.org/10.1016/j.measurement.2020.108288
Mailizar, M., Almanthari, A., & Maulina, S. (2021). Examining Teachers’ Behavioral Intention to Use E-learning in Teaching of Mathematics: An Extended TAM Model. Contemporary Educational Technology, 13(2), ep298. https://doi.org/10.30935/cedtech/9709
Maloy, J., Fries, L., Laski, F., & Ramirez, G. (2019). Seductive Details in the Flipped Classroom: The Impact of Interesting but Educationally Irrelevant Information on Student Learning and Motivation. CBE—Life Sciences Education, 18(3), ar42. https://doi.org/10.1187/cbe.19-01-0004
Meyer, O. A., Omdahl, M. K., & Makransky, G. (2019). Investigating the effect of pre-training when learning through immersive virtual reality and video: A media and methods experiment. Computers & Education, 140, 103603. https://doi.org/10.1016/j.compedu.2019.103603
Nakajima, T. M., & Goode, J. (2020). Lighting Up Learning: Teachers’ Pedagogical Approaches for Mak(e)ing Computing Culturally Responsive in Electronic-Textiles Classrooms. Computing in Science & Engineering, 22(5), 41–50. https://doi.org/10.1109/MCSE.2020.3007164
Oyebode, O., Alqahtani, F., & Orji, R. (2020). Using Machine Learning and Thematic Analysis Methods to Evaluate Mental Health Apps Based on User Reviews. IEEE Access, 8, 111141–111158. https://doi.org/10.1109/ACCESS.2020.3002176
Pal, D., & Vanijja, V. (2020). Perceived usability evaluation of Microsoft Teams as an online learning platform during COVID-19 using system usability scale and technology acceptance model in India. Children and Youth Services Review, 119, 105535. https://doi.org/10.1016/j.childyouth.2020.105535
Rahma, R. A., Sucipto, S., Affriyenni, Y., & Widyaswari, M. (2021). Cybergogy as a digital media to facilitate the learning style of millennial college students. World Journal on Educational Technology: Current Issues, 13(2), 223–235. https://doi.org/10.18844/wjet.v13i2.5691
Salehudin, M., Nasir, M., Hawib, S., Toba, R., Hayati, N., & Safiah, I. (2021). The Users’ Experiences in Processing Visual Media for Creative and Online Learning Using Instagram. European Journal of Educational Research, 10(4), 1669–1682. https://doi.org/10.12973/eu-jer.10.4.1669
Sarugaku, T., Kobayashi, Y., Koyama, R., Shinya, M., & Yamada, M. (2019). Proposal of a New Application Method of 4K Editing Technology to Sports Video. 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE), 1060–1063. https://doi.org/10.1109/GCCE46687.2019.9015260
Sensors, J. O. (2023). Retracted: Teacher Education and Management: Innovative Application of Ecological Management System of Big Data Management System. Journal of Sensors, 2023, 1–1. https://doi.org/10.1155/2023/9835307
Smith, T. L., & Moore, E. B. (2020). Storytelling to Sensemaking: A Systematic Framework for Designing Auditory Description Display for Interactives. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–12. https://doi.org/10.1145/3313831.3376460
Syamsuddin, A., Babo, R., Sulfasyah, S., & Rahman, S. (2021). Mathematics Learning Interest of Students Based on the Difference in the Implementation of Model of Thematic Learning and Character-Integrated Thematic Learning. European Journal of Educational Research, 10(2), 581–591. https://doi.org/10.12973/eu-jer.10.2.581
Tang, Y. M., Chen, P. C., Law, K. M. Y., Wu, C. H., Lau, Y., Guan, J., He, D., & Ho, G. T. S. (2021). Comparative analysis of Student’s live online learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector. Computers & Education, 168, 104211. https://doi.org/10.1016/j.compedu.2021.104211
Ulucak, R., Danish, & Khan, S. U. (2020). Does information and communication technology affect CO 2 mitigation under the pathway of sustainable development during the mode of globalization? Sustainable Development, 28(4), 857–867. https://doi.org/10.1002/sd.2041
Wang, M., Wang, H., Yin, Y., Rahardja, S., & Qu, Z. (2022). Temperature field prediction for various porous media considering variable boundary conditions using deep learning method. International Communications in Heat and Mass Transfer, 132, 105916. https://doi.org/10.1016/j.icheatmasstransfer.2022.105916
Wang, Z. (2021). Analysis on the Application of Video Editing Skills Based on Image Mosaic in Film and Television Works. 2021 2nd International Conference on Computers, Information Processing and Advanced Education, 1446–1449. https://doi.org/10.1145/3456887.3459697
Zhu, L., Zhang, C., Zhang, C., Zhang, Z., Zhou, X., Liu, W., & Zhu, B. (2020). A new and reliable dual model- and data-driven TOC prediction concept: A TOC logging evaluation method using multiple overlapping methods integrated with semi-supervised deep learning. Journal of Petroleum Science and Engineering, 188, 106944. https://doi.org/10.1016/j.petrol.2020.106944
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