Improving Student Learning Outcomes in the Human Digestive System through the Application of Problem-Based Learning Model
DOI:
https://doi.org/10.59535/care.v3i1.435Keywords:
Digestive System, Learning Outcomes, Problem Based LearningAbstract
Learning is a process of interaction between learning resources between students and teachers in a learning environment. The application of the Problem Based Learning method can be a solution for teachers to improve student learning outcomes. This study focuses on the impact of the Problem-Based Learning (PBL) model on student learning outcomes in the topic of the digestive system in Class Mathematics and Natural Sciences (MIA) 2 at State High School (SMAN) 9 Mataram. The research involved 33 students from Class XI MIA 2 at SMAN 9 Mataram, and the learning improvement actions were implemented over three cycles. The study aimed to explore the effectiveness of the PBL method in enhancing student learning achievements, expanding teachers' professional perspectives, and contributing to innovative teaching strategies within the institution. The findings indicate a significant improvement in student learning outcomes, as reflected in the evaluation scores: 35% of students (7 students) achieved scores above the minimum completion criteria in the pre-cycle, 50% (10 students) in Cycle 1, and 85% (17 students) in Cycle 2. This study demonstrates that the application of the PBL model has a positive impact on student learning achievements in Biology, specifically in the digestive system topic.
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M. Taghizadeh and F. Hajhosseini, “Investigating a Blended Learning Environment: Contribution of Attitude, Interaction, and Quality of Teaching to Satisfaction of Graduate Students of TEFL,” Asia-Pac. Educ. Res., vol. 30, no. 5, pp. 459–469, Oct. 2021, doi: 10.1007/s40299-020-00531-z.
L. Closs, M. Mahat, and W. Imms, “Learning environments’ influence on students’ learning experience in an Australian Faculty of Business and Economics,” Learn. Environ. Res., vol. 25, no. 1, pp. 271–285, Apr. 2022, doi: 10.1007/s10984-021-09361-2.
K. Lu, H. H. Yang, Y. Shi, and X. Wang, “Examining the key influencing factors on college students’ higher-order thinking skills in the smart classroom environment,” Int. J. Educ. Technol. High. Educ., vol. 18, no. 1, p. 1, Jan. 2021, doi: 10.1186/s41239-020-00238-7.
K. Aldrup, B. Carstensen, and U. Klusmann, “Is Empathy the Key to Effective Teaching? A Systematic Review of Its Association with Teacher-Student Interactions and Student Outcomes,” Educ. Psychol. Rev., vol. 34, no. 3, pp. 1177–1216, Sep. 2022, doi: 10.1007/s10648-021-09649-y.
L. Ma, “An Immersive Context Teaching Method for College English Based on Artificial Intelligence and Machine Learning in Virtual Reality Technology,” Mob. Inf. Syst., vol. 2021, no. 1, p. 2637439, 2021, doi: 10.1155/2021/2637439.
W. H. Wong and E. Chapman, “Student satisfaction and interaction in higher education,” High. Educ., vol. 85, no. 5, pp. 957–978, May 2023, doi: 10.1007/s10734-022-00874-0.
I. V. Rossi, J. D. de Lima, B. Sabatke, M. A. F. Nunes, G. E. Ramirez, and M. I. Ramirez, “Active learning tools improve the learning outcomes, scientific attitude, and critical thinking in higher education: Experiences in an online course during the COVID-19 pandemic,” Biochem. Mol. Biol. Educ., vol. 49, no. 6, pp. 888–903, 2021, doi: 10.1002/bmb.21574.
N. Rehman, W. Zhang, A. Mahmood, M. Z. Fareed, and S. Batool, “Fostering twenty-first century skills among primary school students through math project-based learning,” Humanit. Soc. Sci. Commun., vol. 10, no. 1, pp. 1–12, Jul. 2023, doi: 10.1057/s41599-023-01914-5.
A. Purwanto, “Education Management Research Data Analysis: Comparison of Results between Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS For Small Samples,” Aug. 11, 2021, Social Science Research Network, Rochester, NY: 3982753. Accessed: Jan. 18, 2025. [Online]. Available: https://papers.ssrn.com/abstract=3982753
U. K. Lilhore et al., “Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques,” Comput. Math. Methods Med., vol. 2022, no. 1, p. 4688327, 2022, doi: 10.1155/2022/4688327.
G. Nalli, D. Amendola, A. Perali, and L. Mostarda, “Comparative Analysis of Clustering Algorithms and Moodle Plugin for Creation of Student Heterogeneous Groups in Online University Courses,” Appl. Sci., vol. 11, no. 13, Art. no. 13, Jan. 2021, doi: 10.3390/app11135800.
S. Dolapcioglu and A. Doğanay, “Development of critical thinking in mathematics classes via authentic learning: an action research,” Int. J. Math. Educ. Sci. Technol., vol. 53, no. 6, pp. 1363–1386, Jun. 2022, doi: 10.1080/0020739X.2020.1819573.
M. Akour and M. Alenezi, “Higher Education Future in the Era of Digital Transformation,” Educ. Sci., vol. 12, no. 11, Art. no. 11, Nov. 2022, doi: 10.3390/educsci12110784.
L. Castañeda, F. M. Esteve-Mon, J. Adell, and S. Prestridge, “International insights about a holistic model of teaching competence for a digital era: the digital teacher framework reviewed,” Eur. J. Teach. Educ., vol. 45, no. 4, pp. 493–512, Aug. 2022, doi: 10.1080/02619768.2021.1991304.
S. Caskurlu, Y. Maeda, J. C. Richardson, and J. Lv, “A meta-analysis addressing the relationship between teaching presence and students’ satisfaction and learning,” Comput. Educ., vol. 157, p. 103966, 2020.
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Copyright (c) 2025 Tetii Apriati, Masra Latjompoh, Zhi-Guo Xiao

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