ABSTRACT:
This study aims to explore the learning experiences of pre-service teachers in utilizing generative AI-based data-driven decision-making(DDDM) tools and to investigate whether the experience was effective. To verify the effectiveness, a survey on the efficacy of DDDM was conducted before and after the class, and the responses were analyzed using paired sample t-tests. In addition, students were asked to write a reflection journal at the end of the class, which was later analyzed by text mining. As a result, pre-service teachers’ DDDM efficacy significantly improved after utilizing the generative AI-based tools. In addition, the topic modeling from reflection journals was extracted as follows: ‘Data literacy learning using gen AI to support DDDM that pre-service teachers can use in the field,’ ‘Deriving teaching strategies by groups using generative AI and recognizing ethical issues regarding educational data,’ ‘Improving pre-service teachers’ problem-solving skills through data literacy competencies,’ ‘Reflecting on the ability and method of asking questions through gen AI tools for interpreting performance data,’ and ‘Supporting pre-service teachers’ efficient instructional decision-making through data visualization-based materials.’ Finally, the analysis of the difficulties revealed in the pre-service teachers’ reflective journals, the output, the learning materials generated by the gen AI, asking questions to the gen AI, data analysis tasks with the gen AI, and the time it takes to use the gen AI. Therefore, this study is significant for its dual implications for pre-service teachers’ learning experiences in DDDM using generative AI.
Keywords:
Generative AI, Data-driven Decision-making, Reflection Analysis, Text Mining