Use of Learning Media to Increase Student Learning Motivation in Junior High Schools
Downloads
The development of the times greatly influences life, one of which is in the field of education, there are many innovations related to education, one example is the use of learning media in teaching and learning processes and motivation. Lots of learning innovations that produce technology that makes it easier for an educator to provide learning material so that students get motivation to learn. Learning media is very useful in every lesson at the junior high school level. This study aims to determine the application of the use of any media that can influence and encourage students' learning motivation at the junior high school level. This type of research is descriptive qualitative research. Data obtained through observation, interviews and documentation. The results of the study show that the use of learning media on students' learning motivation greatly supports motivation in learning activities. The students were highly motivated when the learning used projector and speaker media. In addition, the use of learning media at the junior high school level has an important role in the interest and motivation of students' learning, including feelings of pleasure and interest in the material increasing. The implication of this research is that educators in delivering learning material in class must be good at choosing and using the right media, educators must also have creative and innovative ideas so that the learning atmosphere of students is more enthusiastic and motivated. The limitation of this research is that the researcher only observed one school, therefore the researcher hopes that future researchers will be able to observe more schools and in a better way.
Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/j.chb.2019.08.004
Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D. (2019). Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements. Psychological Science in the Public Interest, 20(1), 1–68. https://doi.org/10.1177/1529100619832930
Bernheim, A., Mei, X., Huang, M., Yang, Y., Fayad, Z. A., Zhang, N., Diao, K., Lin, B., Zhu, X., Li, K., Li, S., Shan, H., Jacobi, A., & Chung, M. (2020). Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection. Radiology, 295(3), 200463. https://doi.org/10.1148/radiol.2020200463
Champion, K. E., Gardner, L. A., McGowan, C., Chapman, C., Thornton, L., Parmenter, B., McBride, N., Lubans, D. R., McCann, K., Spring, B., Teesson, M., The Health4Life Team, & Newton, N. C. (2020). A Web-Based Intervention to Prevent Multiple Chronic Disease Risk Factors Among Adolescents: Co-Design and User Testing of the Health4Life School-Based Program. JMIR Formative Research, 4(7), e19485. https://doi.org/10.2196/19485
Cheng, G., Yang, C., Yao, X., Guo, L., & Han, J. (2018). When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs. IEEE Transactions on Geoscience and Remote Sensing, 56(5), 2811–2821. https://doi.org/10.1109/TGRS.2017.2783902
Chick, R. C., Clifton, G. T., Peace, K. M., Propper, B. W., Hale, D. F., Alseidi, A. A., & Vreeland, T. J. (2020). Using Technology to Maintain the Education of Residents During the COVID-19 Pandemic. Journal of Surgical Education, 77(4), 729–732. https://doi.org/10.1016/j.jsurg.2020.03.018
De’, R., Pandey, N., & Pal, A. (2020). Impact of digital surge during Covid-19 pandemic: A viewpoint on research and practice. International Journal of Information Management, 55, 102171. https://doi.org/10.1016/j.ijinfomgt.2020.102171
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318. https://doi.org/10.1016/j.compag.2018.01.009
Forrester, S. J., Kikuchi, D. S., Hernandes, M. S., Xu, Q., & Griendling, K. K. (2018). Reactive Oxygen Species in Metabolic and Inflammatory Signaling. Circulation Research, 122(6), 877–902. https://doi.org/10.1161/CIRCRESAHA.117.311401
Güne?, F. E., Bekiroglu, N., Imeryuz, N., & Agirbasli, M. (2019). Awareness of cardiovascular risk factors among university students in Turkey. Primary Health Care Research & Development, 20, e127. https://doi.org/10.1017/S146342361900063X
Hermawan, B., & Rahyono, F. X. (2019). Ideational meanings of science and interpersonal position of readers in science textbooks for basic level in Indonesia. Indonesian Journal of Applied Linguistics, 9(1). https://doi.org/10.17509/ijal.v9i1.15932
Indrawan, G., Paramarta, I. K., Agustini, K., & Sariyasa, S. (2018). Latin-to-Balinese Script Transliteration Method on Mobile Application: A Comparison. Indonesian Journal of Electrical Engineering and Computer Science, 10(3), 1331. https://doi.org/10.11591/ijeecs.v10.i3.pp1331-1342
Iyer, P., Aziz, K., & Ojcius, D. M. (2020). Impact of COVID-19 on dental education in the United States. Journal of Dental Education, 84(6), 718–722. https://doi.org/10.1002/jdd.12163
Jing, L., & Tian, Y. (2021). Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(11), 4037–4058. https://doi.org/10.1109/TPAMI.2020.2992393
Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., Zaslavsky, L., Zhang, J., & Bolton, E. E. (2019). PubChem 2019 update: Improved access to chemical data. Nucleic Acids Research, 47(D1), D1102–D1109. https://doi.org/10.1093/nar/gky1033
Lawrence, J. E., & Tar, U. A. (2018). Factors that influence teachers’ adoption and integration of ICT in teaching/learning process. Educational Media International, 55(1), 79–105. https://doi.org/10.1080/09523987.2018.1439712
Ma, Y., Hao, Y., Chen, M., Chen, J., Lu, P., & Košir, A. (2019). Audio-visual emotion fusion (AVEF): A deep efficient weighted approach. Information Fusion, 46, 184–192. https://doi.org/10.1016/j.inffus.2018.06.003
Mischler, Æ. (2021). Indexing the translation of Fath al-Bari, a multi-volume Islamic classic. The Indexer: The International Journal of Indexing, 39(2), 165–182. https://doi.org/10.3828/indexer.2021.17
Miskiah, M., Suryono, Y., & Sudrajat, A. (2019). INTEGRATION OF INFORMATION AND COMUNICATION TECHNOLOGY INTO ISLAMIC RELIGIOUS EDUCATION TEACHER TRAINING. Jurnal Cakrawala Pendidikan, 38(1), 130–140. https://doi.org/10.21831/cp.v38i1.23439
M.J, F. R., & Hanggara, Y. (2019). PENERAPAN MODEL PEMBELAJARAN JIGSAW DAN KANCING GEMERINCING DITINJAU DARI MINAT BELAJAR TERHADAP HASIL BELAJAR SISWA KELAS VIII SMP MONDIAL BATAM. PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika, 8(2). https://doi.org/10.33373/pythagoras.v8i2.1904
Molinillo, S., Aguilar-Illescas, R., Anaya-Sánchez, R., & Vallespín-Arán, M. (2018). Exploring the impacts of interactions, social presence and emotional engagement on active collaborative learning in a social web-based environment. Computers & Education, 123, 41–52. https://doi.org/10.1016/j.compedu.2018.04.012
Murwantini, S. M., Soenarto, S., & Sukardi, T. (2021). Evaluation of Curriculum Implementation at Vocational High Schools in Palangkaraya. The International Journal of Assessment and Evaluation, 29(1), 17–25. https://doi.org/10.18848/2327-7920/CGP/v29i01/17-25
Pahayahay, A., & Khalili-Mahani, N. (2020). What Media Helps, What Media Hurts: A Mixed Methods Survey Study of Coping with COVID-19 Using the Media Repertoire Framework and the Appraisal Theory of Stress. Journal of Medical Internet Research, 22(8), e20186. https://doi.org/10.2196/20186
Pakatchi, A. (2020). Shi?ism. In D. W. Brown (Ed.), The Wiley Blackwell Concise Companion to the Hadith (1st ed., pp. 281–294). Wiley. https://doi.org/10.1002/9781118638477.ch14
Parikhin, P. (2018). Pembelajaran Kooperatif STAD untuk Meningkatkan Kualitas Pembelajaran Praktik Kelistrikan SMK (Studi pada SMK Sriwijaya Karangpucung). Automotive Experiences, 1(02), 53–57. https://doi.org/10.31603/ae.v1i02.2249
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
Rinninella, E., Raoul, P., Cintoni, M., Franceschi, F., Miggiano, G., Gasbarrini, A., & Mele, M. (2019). What is the Healthy Gut Microbiota Composition? A Changing Ecosystem across Age, Environment, Diet, and Diseases. Microorganisms, 7(1), 14. https://doi.org/10.3390/microorganisms7010014
Rohmansyah, R. (2020). Understanding of Polygamy in the Qur’anic Text, Hadith Text, and Biblical Text. AL-IHKAM: Jurnal Hukum & Pranata Sosial, 14(2), 317–336. https://doi.org/10.19105/al-lhkam.v14i2.2529
Rusydiyah, E. F., Purwati, E., & Prabowo, A. (2020). HOW TO USE DIGITAL LITERACY AS A LEARNING RESOURCE FOR TEACHER CANDIDATES IN INDONESIA. Jurnal Cakrawala Pendidikan, 39(2), 305–318. https://doi.org/10.21831/cp.v39i2.30551
Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. WIREs Data Mining and Knowledge Discovery, 8(4). https://doi.org/10.1002/widm.1249
Scott Kruse, C., Karem, P., Shifflett, K., Vegi, L., Ravi, K., & Brooks, M. (2018). Evaluating barriers to adopting telemedicine worldwide: A systematic review. Journal of Telemedicine and Telecare, 24(1), 4–12. https://doi.org/10.1177/1357633X16674087
Shafique, K., Khawaja, B. A., Sabir, F., Qazi, S., & Mustaqim, M. (2020). Internet of Things (IoT) for Next-Generation Smart Systems: A Review of Current Challenges, Future Trends and Prospects for Emerging 5G-IoT Scenarios. IEEE Access, 8, 23022–23040. https://doi.org/10.1109/ACCESS.2020.2970118
Sheikholeslami, M., Haq, R., Shafee, A., & Li, Z. (2019). Heat transfer behavior of nanoparticle enhanced PCM solidification through an enclosure with V shaped fins. International Journal of Heat and Mass Transfer, 130, 1322–1342. https://doi.org/10.1016/j.ijheatmasstransfer.2018.11.020
Sheth, J. (2020). Impact of Covid-19 on consumer behavior: Will the old habits return or die? Journal of Business Research, 117, 280–283. https://doi.org/10.1016/j.jbusres.2020.05.059
Sidhu, G. K., Kaur, S., & Chi, L. J. (2018). CEFR-aligned school-based assessment in the Malaysian primary ESL classroom. Indonesian Journal of Applied Linguistics, 8(2). https://doi.org/10.17509/ijal.v8i2.13311
Stanaway, J. D., Afshin, A., Gakidou, E., Lim, S. S., Abate, D., Abate, K. H., Abbafati, C., Abbasi, N., Abbastabar, H., Abd-Allah, F., Abdela, J., Abdelalim, A., Abdollahpour, I., Abdulkader, R. S., Abebe, M., Abebe, Z., Abera, S. F., Abil, O. Z., Abraha, H. N., … Murray, C. J. L. (2018). Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 392(10159), 1923–1994. https://doi.org/10.1016/S0140-6736(18)32225-6
Supraptiningsih, U., & Bariyyah, K. (2019). Marriage Settlement among Minority Moslem by Datok Imam Masjid in South Thailand. AL-IHKAM: Jurnal Hukum & Pranata Sosial, 14(2), 221–236. https://doi.org/10.19105/al-lhkam.v14i2.2631
Susanti, F., Junaedi, D., & Effendy, V. (2019). Communication Learning User Interface Model for Children with Autism with the Goal-Directed Design Method. 2019 7th International Conference on Information and Communication Technology (ICoICT), 1–6. https://doi.org/10.1109/ICoICT.2019.8835395
Suswanto, H., Aziz, F. K., Ningrum, G. D. K., & Hamdan, A. (2021). The Development of A PHP-Based Instrument for Attitude Assessment of Learning Outcomes to Improve the Achievement Efficiency of Computer and Network Engineering Students in SMK Negeri 7 Malang. 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), 1–5. https://doi.org/10.1109/ICEEIE52663.2021.9616655
Tuwoso, T. (2022). Comparison of learning outcomes in metal casting subject using traditional learning model with problem-base learning through SIPEJAR. 030017. https://doi.org/10.1063/5.0093862
Xu, B., Chen, N.-S., & Chen, G. (2020). Effects of teacher role on student engagement in WeChat-Based online discussion learning. Computers & Education, 157, 103956. https://doi.org/10.1016/j.compedu.2020.103956
Yang, Y., Lu, Y., Yang, L., Gou, Z., & Zhang, X. (2020). Urban greenery, active school transport, and body weight among Hong Kong children. Travel Behaviour and Society, 20, 104–113. https://doi.org/10.1016/j.tbs.2020.03.001
Yii, J., Mahmud, A., Abd Samat, A. H., Sabardin, D. M., Mohd Isa, M. H., Mohd Saiboon, I., & Jaafar, M. J. (2021). A Qualitative Study on Emergency Healthcare Worker (EHCW) Perception towards COVID-ACLS Simulation Training in Resuscitating Suspected COVID-19 Patients. Sains Malaysiana, 50(9), 2847–2858. https://doi.org/10.17576/jsm-2021-5009-27
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent Trends in Deep Learning Based Natural Language Processing [Review Article]. IEEE Computational Intelligence Magazine, 13(3), 55–75. https://doi.org/10.1109/MCI.2018.2840738
Zhang, C., Yang, L., Liu, S., Ma, S., Wang, Y., Cai, Z., Du, H., Li, R., Kang, L., Su, M., Zhang, J., Liu, Z., & Zhang, B. (2020). Survey of Insomnia and Related Social Psychological Factors Among Medical Staff Involved in the 2019 Novel Coronavirus Disease Outbreak. Frontiers in Psychiatry, 11, 306. https://doi.org/10.3389/fpsyt.2020.00306
Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. WIREs Data Mining and Knowledge Discovery, 8(4). https://doi.org/10.1002/widm.1253
Zhang, S., Zhang, S., Huang, T., Gao, W., & Tian, Q. (2018). Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 28(10), 3030–3043. https://doi.org/10.1109/TCSVT.2017.2719043
Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018). Learning Transferable Architectures for Scalable Image Recognition. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8697–8710. https://doi.org/10.1109/CVPR.2018.00907
Copyright (c) 2023 Canti Charline
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
