Recently, artificial intelligence has become more prevalent due to the combination of more data, faster processing power, and more powerful algorithms. AI technology has been introduced into almost all industries and is also affecting the education sector. The objective of this study was to explore AI topics through an analysis of literature related to AI education for grades K-12 and provide implications for the composition of a system for AI education. For this purpose, 27 materials released at the 2018 and 2019 AI4K12 Symposiums were collected. Besides, artificial intelligence integration across subjects and artificial intelligence curriculum published by CBSE of India were collected for analysis. The frequency of words, word cloud, and topic modeling was performed for each collected document. According to the analysis, content on the necessary future direction for AI education and introductions to educational tools were extracted from the 2018 symposium, whereas the 2019 symposium contained more concrete discussions on how to conduct AI education in schools. Meanwhile, content involving the principles of integration for how to integrate AI with other subjects and AI-based teaching and learning methods were extracted from Artificial Intelligence Integration Across Subjects. Finally, Artificial Intelligence Curriculum covered the theories and principles of AI. This study has significance in that it analyzed how much discussion about AI education is being conducted in K-12 based on topic modelling and suggested future directions for AI education.
Many artificial intelligence studies focus on designing new neural network models or optimizing hyperparameters to improve model accuracy. To develop a reliable model, appropriate data are required, and data preprocessing is an essential part of acquiring the data. Although various studies regard data preprocessing as part of the data exploration process, those studies lack awareness about the need for separate technologies and solutions for preprocessing. Therefore, this study evaluated combinations of preprocessing types in a text-processing neural network model. Better performance was observed when two preprocessing types were used than when three or more preprocessing types were used for data purification. More specifically, using lemmatization and punctuation splitting together, lemmatization and lowering together, and lowering and punctuation splitting together showed positive effects on accuracy. This study is significant because the results allow better decisions to be made about the selection of the preprocessing types in various research fields, including neural network research.
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