Fiqh Learning at Madrasah Ibtidaiyah: Enhancing Students’ Academic Performance through a Collaborative Learning Model

Hikmat Kamal; Mahyudin Ritonga; Adam Mudinillah

doi:10.35445/alishlah.v16i2.5055

Fiqh Learning at Madrasah Ibtidaiyah: Enhancing Students’ Academic Performance through a Collaborative Learning Model

Hikmat Kamal, Mahyudin Ritonga, Adam Mudinillah

Abstract

In the context of Madrasah Ibtidaiyah, Fiqh is a mandatory subject facing challenges due to ineffective teaching methodologies, leading to suboptimal learning outcomes. This study explores the potential of collaborative learning models to enhance these outcomes, hypothesizing that such methods could improve student engagement and comprehension. Utilizing a quantitative approach, data were collected through a structured questionnaire administered to Fiqh teachers via Google Forms, focusing on the effectiveness of current teaching methods and their impact on student performance. The analysis indicated that collaborative learning significantly improves learning outcomes by fostering greater student cooperation in understanding Fiqh concepts. This enhancement in learning is evident in both the quality of student engagement and academic achievements. While the findings affirm the benefits of collaborative methods, the study is limited to these strategies and suggests further research into a variety of instructional techniques to broaden educational effectiveness in religious studies. The success of collaborative models in this setting supports their potential utility in similar educational contexts, recommending an expansion of research to include diverse pedagogical approaches.

Keywords

Learning outcomes; collaborative model; fiqh

Full Text:

PDF

References

Adadi, A., & Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052

Baltz, V., Manchon, A., Tsoi, M., Moriyama, T., Ono, T., & Tserkovnyak, Y. (2018). Antiferromagnetic spintronics. Reviews of Modern Physics, 90(1), 015005. https://doi.org/10.1103/RevModPhys.90.015005

Barua, M. (2019). Animating capital: Work, commodities, circulation. Progress in Human Geography, 43(4), 650–669. https://doi.org/10.1177/0309132518819057

Benjamin, E. J., Virani, S. S., Callaway, C. W., Chamberlain, A. M., Chang, A. R., Cheng, S., Chiuve, S. E., Cushman, M., Delling, F. N., Deo, R., de Ferranti, S. D., Ferguson, J. F., Fornage, M., Gillespie, C., Isasi, C. R., Jiménez, M. C., Jordan, L. C., Judd, S. E., Lackland, D., … Muntner, P. (2018). Heart Disease and Stroke Statistics—2018 Update: A Report From the American Heart Association. Circulation, 137(12). https://doi.org/10.1161/CIR.0000000000000558

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 68(6), 394–424. https://doi.org/10.3322/caac.21492

Cai, J., Luo, J., Wang, S., & Yang, S. (2018). Feature selection in machine learning: A new perspective. Neurocomputing, 300, 70–79. https://doi.org/10.1016/j.neucom.2017.11.077

Chen, C., Chen, H., Zhang, Y., Thomas, H. R., Frank, M. H., He, Y., & Xia, R. (2020). TBtools: An Integrative Toolkit Developed for Interactive Analyses of Big Biological Data. Molecular Plant, 13(8), 1194–1202. https://doi.org/10.1016/j.molp.2020.06.009

Corcoran, C. M., Carrillo, F., Fernández-Slezak, D., Bedi, G., Klim, C., Javitt, D. C., Bearden, C. E., & Cecchi, G. A. (2018). Prediction of psychosis across protocols and risk cohorts using automated language analysis. World Psychiatry, 17(1), 67–75. https://doi.org/10.1002/wps.20491

Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018). Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nature Medicine, 24(10), 1559–1567. https://doi.org/10.1038/s41591-018-0177-5

Eslam, M., Sanyal, A. J., George, J., Sanyal, A., Neuschwander-Tetri, B., Tiribelli, C., Kleiner, D. E., Brunt, E., Bugianesi, E., Yki-Järvinen, H., Grønbæk, H., Cortez-Pinto, H., George, J., Fan, J., Valenti, L., Abdelmalek, M., Romero-Gomez, M., Rinella, M., Arrese, M., … Younossi, Z. (2020). MAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease. Gastroenterology, 158(7), 1999-2014.e1. https://doi.org/10.1053/j.gastro.2019.11.312

Gao, J., Zheng, P., Jia, Y., Chen, H., Mao, Y., Chen, S., Wang, Y., Fu, H., & Dai, J. (2020). Mental health problems and social media exposure during COVID-19 outbreak. PLOS ONE, 15(4), e0231924. https://doi.org/10.1371/journal.pone.0231924

Ge, X., Han, Q.-L., Ding, L., Wang, Y.-L., & Zhang, X.-M. (2020). Dynamic Event-Triggered Distributed Coordination Control and its Applications: A Survey of Trends and Techniques. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(9), 3112–3125. https://doi.org/10.1109/TSMC.2020.3010825

Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354–377. https://doi.org/10.1016/j.patcog.2017.10.013

Guan, W., Ni, Z., Hu, Y., Liang, W., Ou, C., He, J., Liu, L., Shan, H., Lei, C., Hui, D. S. C., Du, B., Li, L., Zeng, G., Yuen, K.-Y., Chen, R., Tang, C., Wang, T., Chen, P., Xiang, J., … Zhong, N. (2020). Clinical Characteristics of Coronavirus Disease 2019 in China. New England Journal of Medicine, 382(18), 1708–1720. https://doi.org/10.1056/NEJMoa2002032

Guo, L., Ren, L., Yang, S., Xiao, M., Chang, D., Yang, F., Dela Cruz, C. S., Wang, Y., Wu, C., Xiao, Y., Zhang, L., Han, L., Dang, S., Xu, Y., Yang, Q.-W., Xu, S.-Y., Zhu, H.-D., Xu, Y.-C., Jin, Q., … Wang, J. (2020). Profiling Early Humoral Response to Diagnose Novel Coronavirus Disease (COVID-19). Clinical Infectious Diseases, 71(15), 778–785. https://doi.org/10.1093/cid/ciaa310

Hoffmann, M., Kleine-Weber, H., Schroeder, S., Krüger, N., Herrler, T., Erichsen, S., Schiergens, T. S., Herrler, G., Wu, N.-H., Nitsche, A., Müller, M. A., Drosten, C., & Pöhlmann, S. (2020). SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. Cell, 181(2), 271-280.e8. https://doi.org/10.1016/j.cell.2020.02.052

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

Huang, Y., & Zhao, N. (2020). Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: A web-based cross-sectional survey. Psychiatry Research, 288, 112954. https://doi.org/10.1016/j.psychres.2020.112954

Jin, Z., Du, X., Xu, Y., Deng, Y., Liu, M., Zhao, Y., Zhang, B., Li, X., Zhang, L., Peng, C., Duan, Y., Yu, J., Wang, L., Yang, K., Liu, F., Jiang, R., Yang, X., You, T., Liu, X., … Yang, H. (2020). Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature, 582(7811), 289–293. https://doi.org/10.1038/s41586-020-2223-y

Katoh, K., Rozewicki, J., & Yamada, K. D. (2019). MAFFT online service: Multiple sequence alignment, interactive sequence choice and visualization. Briefings in Bioinformatics, 20(4), 1160–1166. https://doi.org/10.1093/bib/bbx108

Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Medical Research Methodology, 18(1), 24. https://doi.org/10.1186/s12874-018-0482-1

Kim, M., Kim, G.-H., Lee, T. K., Choi, I. W., Choi, H. W., Jo, Y., Yoon, Y. J., Kim, J. W., Lee, J., Huh, D., Lee, H., Kwak, S. K., Kim, J. Y., & Kim, D. S. (2019). Methylammonium Chloride Induces Intermediate Phase Stabilization for Efficient Perovskite Solar Cells. Joule, 3(9), 2179–2192. https://doi.org/10.1016/j.joule.2019.06.014

Lai, J., Ma, S., Wang, Y., Cai, Z., Hu, J., Wei, N., Wu, J., Du, H., Chen, T., Li, R., Tan, H., Kang, L., Yao, L., Huang, M., Wang, H., Wang, G., Liu, Z., & Hu, S. (2020). Factors Associated With Mental Health Outcomes Among Health Care Workers Exposed to Coronavirus Disease 2019. JAMA Network Open, 3(3), e203976. https://doi.org/10.1001/jamanetworkopen.2020.3976

Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., & Quillen, D. (2018). Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. The International Journal of Robotics Research, 37(4–5), 421–436. https://doi.org/10.1177/0278364917710318

Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Processing Magazine, 37(3), 50–60. https://doi.org/10.1109/MSP.2020.2975749

Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.-W., & Heng, P.-A. (2018). H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes. IEEE Transactions on Medical Imaging, 37(12), 2663–2674. https://doi.org/10.1109/TMI.2018.2845918

Lindegren, L., Hernández, J., Bombrun, A., Klioner, S., Bastian, U., Ramos-Lerate, M., de Torres, A., Steidelmüller, H., Stephenson, C., Hobbs, D., Lammers, U., Biermann, M., Geyer, R., Hilger, T., Michalik, D., Stampa, U., McMillan, P. J., Castañeda, J., Clotet, M., … Vecchiato, A. (2018). Gaia Data Release 2: The astrometric solution. Astronomy & Astrophysics, 616, A2. https://doi.org/10.1051/0004-6361/201832727

Lodigiani, C., Iapichino, G., Carenzo, L., Cecconi, M., Ferrazzi, P., Sebastian, T., Kucher, N., Studt, J.-D., Sacco, C., Bertuzzi, A., Sandri, M. T., & Barco, S. (2020). Venous and arterial thromboembolic complications in COVID-19 patients admitted to an academic hospital in Milan, Italy. Thrombosis Research, 191, 9–14. https://doi.org/10.1016/j.thromres.2020.04.024

Low, D. M., Rumker, L., Talkar, T., Torous, J., Cecchi, G., & Ghosh, S. S. (2020). Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study. Journal of Medical Internet Research, 22(10), e22635. https://doi.org/10.2196/22635

Martí, P., García-Mayor, C., & Serrano-Estrada, L. (2019). Identifying opportunity places for urban regeneration through LBSNs. Cities, 90, 191–206. https://doi.org/10.1016/j.cities.2019.02.001

Munn, Z., Peters, M. D. J., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medical Research Methodology, 18(1), 143. https://doi.org/10.1186/s12874-018-0611-x

Petrilli, C. M., Jones, S. A., Yang, J., Rajagopalan, H., O’Donnell, L., Chernyak, Y., Tobin, K. A., Cerfolio, R. J., Francois, F., & Horwitz, L. I. (2020). Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: Prospective cohort study. BMJ, m1966. https://doi.org/10.1136/bmj.m1966

Po, H. C., Zou, L., Vishwanath, A., & Senthil, T. (2018). Origin of Mott Insulating Behavior and Superconductivity in Twisted Bilayer Graphene. Physical Review X, 8(3), 031089. https://doi.org/10.1103/PhysRevX.8.031089

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

Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860

Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4510–4520. https://doi.org/10.1109/CVPR.2018.00474

Shang, J., Wan, Y., Luo, C., Ye, G., Geng, Q., Auerbach, A., & Li, F. (2020). Cell entry mechanisms of SARS-CoV-2. Proceedings of the National Academy of Sciences, 117(21), 11727–11734. https://doi.org/10.1073/pnas.2003138117

Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019). Comprehensive Integration of Single-Cell Data. Cell, 177(7), 1888-1902.e21. https://doi.org/10.1016/j.cell.2019.05.031

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 71(3), 209–249. https://doi.org/10.3322/caac.21660

Thakur, P., Kool, A., Hoque, N. A., Bagchi, B., Khatun, F., Biswas, P., Brahma, D., Roy, S., Banerjee, S., & Das, S. (2018). Superior performances of in situ synthesized ZnO/PVDF thin film based self-poled piezoelectric nanogenerator and self-charged photo-power bank with high durability. Nano Energy, 44, 456–467. https://doi.org/10.1016/j.nanoen.2017.11.065

Théry, C., Witwer, K. W., Aikawa, E., Alcaraz, M. J., Anderson, J. D., Andriantsitohaina, R., Antoniou, A., Arab, T., Archer, F., Atkin-Smith, G. K., Ayre, D. C., Bach, J.-M., Bachurski, D., Baharvand, H., Balaj, L., Baldacchino, S., Bauer, N. N., Baxter, A. A., Bebawy, M., … Zuba-Surma, E. K. (2018). Minimal information for studies of extracellular vesicles 2018 (MISEV2018): A position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. Journal of Extracellular Vesicles, 7(1), 1535750. https://doi.org/10.1080/20013078.2018.1535750

Virani, S. S., Alonso, A., Benjamin, E. J., Bittencourt, M. S., Callaway, C. W., Carson, A. P., Chamberlain, A. M., Chang, A. R., Cheng, S., Delling, F. N., Djousse, L., Elkind, M. S. V., Ferguson, J. F., Fornage, M., Khan, S. S., Kissela, B. M., Knutson, K. L., Kwan, T. W., Lackland, D. T., … On behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. (2020). Heart Disease and Stroke Statistics—2020 Update: A Report From the American Heart Association. Circulation, 141(9). https://doi.org/10.1161/CIR.0000000000000757

Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. https://doi.org/10.1126/science.aap9559

Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., Ho, C. S., & Ho, R. C. (2020). Immediate Psychological Responses and Associated Factors during the Initial Stage of the 2019 Coronavirus Disease (COVID-19) Epidemic among the General Population in China. International Journal of Environmental Research and Public Health, 17(5), 1729. https://doi.org/10.3390/ijerph17051729

Watson, D., Stanton, K., Khoo, S., Ellickson-Larew, S., & Stasik-O’Brien, S. M. (2019). Extraversion and psychopathology: A multilevel hierarchical review. Journal of Research in Personality, 81, 1–10. https://doi.org/10.1016/j.jrp.2019.04.009

Will, C. M. (2018). Theory and Experiment in Gravitational Physics (2nd ed.). Cambridge University Press. https://doi.org/10.1017/9781316338612

Wishart, D. S., Feunang, Y. D., Marcu, A., Guo, A. C., Liang, K., Vázquez-Fresno, R., Sajed, T., Johnson, D., Li, C., Karu, N., Sayeeda, Z., Lo, E., Assempour, N., Berjanskii, M., Singhal, S., Arndt, D., Liang, Y., Badran, H., Grant, J., … Scalbert, A. (2018). HMDB 4.0: The human metabolome database for 2018. Nucleic Acids Research, 46(D1), D608–D617. https://doi.org/10.1093/nar/gkx1089

Xu, Q., Zhang, L., Cheng, B., Fan, J., & Yu, J. (2020). S-Scheme Heterojunction Photocatalyst. Chem, 6(7), 1543–1559. https://doi.org/10.1016/j.chempr.2020.06.010

Yoo, J. J., Seo, G., Chua, M. R., Park, T. G., Lu, Y., Rotermund, F., Kim, Y.-K., Moon, C. S., Jeon, N. J., Correa-Baena, J.-P., Bulović, V., Shin, S. S., Bawendi, M. G., & Seo, J. (2021). Efficient perovskite solar cells via improved carrier management. Nature, 590(7847), 587–593. https://doi.org/10.1038/s41586-021-

DOI: https://doi.org/10.35445/alishlah.v16i2.5055