Akademik Veri Yönetim Sistemi
6th International Conference on Computational Intelligence and Digital Technology, ICCIDT2K 2024, Kottayam, Hindistan, 3 - 04 Mayıs 2024, cilt.3260, (Tam Metin Bildiri)
The purpose of this study was to examine the effects of various forms of collaborative training on embedded students' metacognitive skills and their capacity to make sociotechnical decisions within an artificial intelligence classroom environment. Part of making decisions in the social science involves both identifying problems and developing, testing, and ultimately settling on solutions. Using the IMPROVE methodology, we compared two distinct collaborative training approaches, each with its own set of embedded metacognitive instructions. Two hundred and fifty-Three diploma students from three separate colleges took part: one in a conventional setting of collaborative learning (COLED), another with embedded metacognitive questions (COLED+EMB), and a control group that did not undergo any intervention. In comparison to the control group, students in the two training situations performed better on both measures of sociotechnical decision making. Regardless, there was no difference in performance between the COLED and COLED+EMB conditions for the students. The learning outcomes on the regulatory component of metacognition improved with time in all settings. Students in the COLED+EMB condition had the best average performance on the posttest, although this finding was not statistically significant. The possible outcomes of introducing metacognitive training into science classes are the focus of the conversation.