The Effect of Deep Learning-Based Contextual Problem on Science Concept Understanding in Slow Learners at Inclusive Schools
DOI:
https://doi.org/10.22373/9tr6vz39Keywords:
Sciences, Contextual issues, Conceptual understanding, Deep learning, Inclusive schools, Slow learnersAbstract
Exploring deep learning in inclusive schools is urgent, as it plays a crucial role in fostering meaningful learning experiences for students with diverse abilities. This study aims to determine the effect of deep learning based contextual problem on science concept understanding among slow learners in inclusive schools. The research method used is a quasi-experimental design with a pretest-posttest control group design, involving two inclusive classes in the Yogyakarta region. One class served as the experimental group and received deep learning based contextual problem treatment for three weeks, while the control class received conventional instruction. The main instruments were multiple-choice tests to measure understanding of ecosystem concepts, as well as observation sheets of learning activities. The results of the analysis using the Mann-Whitney test showed a significant difference between the experimental and control groups (p = 0.000), indicating that this approach is effective in improving science concept understanding among slow learners. Learning was conducted through stages of understanding, application, and reflection, utilizing contextual media, field trips, and environmental management projects that involved the local community. These findings reinforce the relevance of constructivism, situated learning, and experiential learning theories in inclusive education. In addition to supporting cognitive aspects, this model also contributes to students' social skills and learning motivation. The practical implications of this study encourage teachers to apply adaptive and contextual learning strategies, as well as the importance of developing modules and teacher training.
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