<![CDATA[This study aims to reduce the level of misconceptions experienced by eleventh-grade high school students on Heat material through the application of a deep learning approach combined with the use of the Wave and Thermodynamics KIT. The research method used was a quasi-experimental one-group pretest–posttest design, involving one class as the experimental class and two classes as replication classes. The research instrument was a three-tier multiple-choice diagnostic test used to map students' misconceptions before and after the learning treatment. Data were analysed using descriptive statistics, two-proportion tests, and normalised gain (n-gain) calculations. The research findings showed a very significant decrease in the frequency of misconceptions, namely from 6.10 to 1.00 in the experimental class; from 5.53 to 1.40 in the first replication class; and from 6.33 to 1.33 in the second replication class. This decrease was also accompanied by an increase in the conception score in each class, as reflected in average n-gain values of 0.69 in the experimental class, 0.72 in the first replication class, and 0.76 in the second replication class. Thus, it can be concluded that the application of deep learning supported by the Wave and Thermodynamics KIT is effective in reducing high school students' misconceptions about Heat material while simultaneously increasing their conceptual understanding to a medium to high level.]]>
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