Effectivity of Artificial Intelligence-Assisted Learning on Elementary School Students' Mathematical Problem-Solving Ability
DOI:
https://doi.org/10.22373/jppg.v4i2.10351Keywords:
Artificial intelligence-assisted learning, problem-solving ability, mathematics learningAbstract
This study aims to test the effect of artificial intelligence-assisted learning on elementary school students' problem-solving abilities in mathematics learning. This study is a quantitative study with an experimental research type. The experimental design used is a quasi-experiment involving students of Blokang State Elementary School as an experimental class (totaling 30 students) and students of Batucina State Elementary School (totaling 30 students) as a control class. The data of this study is quantitative data collected using mathematical problem-solving ability test techniques. The collected data were then analyzed using descriptive statistical test techniques by testing the average value, standard deviation and percentage of completeness, as well as inferential statistical tests using independent t-tests and paired t-tests. The results of the study indicate that artificial intelligence-assisted learning has a positive and significant effect on elementary school students' problem-solving abilities in mathematics learning. This is evident from the research data which shows that the average value of students' mathematical problem-solving abilities in the experimental class is higher (92.16) than the control class (69.13). In addition, the standard deviation value also shows that the standard deviation value of the experimental class is smaller than the control class, which means that the mathematical problem-solving abilities The distribution of students in the experimental class was more even. Furthermore, the percentage of student completion showed that the experimental class achieved 95.61%, while the control class only achieved 70.33%. Inferential statistical tests were conducted in this study to test the research hypothesis. The results of the prerequisite test showed that the data were normally distributed (Sig. > 0.05) and the sample came from a homogeneous population (Sig. > 0.05). The results of the prerequisite test were used as the basis for continuing the hypothesis test using parametric statistical techniques with the t-test type. The results of the independent t-test and paired t-test showed that the significance value was smaller than the alpha value, which was 0.000 (<0.005). Based on these results, artificial intelligence-assisted learning can be used as an alternative to address the low problem-solving abilities of elementary school students in mathematics learning.
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