Development of A Deep Learning Model Based on Augmented Reality to Improve Self-Efficacy and Physics Learning Outcomes of Senior High School Students in Makassar City
DOI:
https://doi.org/10.55927/jiph.v5i3.28Keywords:
Deep Learning, Augmented Reality, Self-Efficacy, Physics Learning Outcomes, AR-Kolb ModelAbstract
This study aims to develop a deep learning model based on Augmented Reality integrated with Kolb’s experiential learning cycle (AR-Kolb) to improve self-efficacy and physics learning outcomes of senior high school students in Makassar. The Research and Development method employing a hybrid approach of Borg and Gall, ADDIE, and Bergman and Moore were applied. The subjects involved 144 eleventh-grade science students from four senior high schools (two public and two private) in Makassar, assigned to experimental and control groups. Instruments included a Bandura-adapted self-efficacy questionnaire, learning outcome tests, expert validation sheets, and observation. Validation results indicated the model was highly valid (average ≥91%). Practicality reached the highly practical category. N-Gain scores for self-efficacy and learning outcomes in the experimental group were in the high category and significantly higher than those of the control group. The maritime-contextual AR-Kolb model proved effective in supporting deep physics learning in Eastern Indonesia.
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