Improving students' mathematics HOTS ability through the integration of deep learning and SOLO taxonomy
DOI:
https://doi.org/10.29407/jmen.v11i2.27111Keywords:
Deep learning, SOLO Taxonomy, mathematical HOTS, high-level thinking, quasi-experimentalAbstract
Education in the 21st century requires students to have high-level thinking skills (HOTS), but the low HOTS of students' mathematics is still a significant challenge, as reflected in the results of international evaluations. This study aims to investigate the effectiveness of the integration of deep learning and SOLO Taxonomy in optimizing students' mathematics HOTS. Using a quasi-experimental design, the study involved 60 of students in grade VIII who were divided into an experimental group (n = 30) and a control group (n = 30). The experimental group received a learning intervention based on deep learning principles with the guidance of the SOLO Taxonomy, while the control group received conventional learning. Data were collected through a math HOTS pre-test and post-test and analyzed using ANCOVA to control students' initial scores. The results showed a significant increase in students' math HOTS in both groups, but the increase in the experimental group was much higher (p<0.01). The qualitative analysis of the students' responses also shows a clear development at the level of SOLO Taxonomy, from Unistructural/Multistructural to Relational and Extended Abstract. These findings indicate that the integration of deep learning and SOLO Taxonomy creates an effective synergistic effect in facilitating deep thinking. This research contributes to the educational literature by providing a measurable and practical learning model to improve HOTS, as well as providing important implications for teacher practice and curriculum development in the future.
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