Building on the success in a previous study in engaging the underserved middle-school population in the practice of science through individualized scaffolding, the current study sought to examine an automated-agent, Astro-world, developed to provide real-time guidance to students in order to increase the scalability of the intervention while maintaining the benefits of the individualized format. Through practices of argument and counterargument in advancing and challenging claims, the agent focused on coordination of multiple variables affecting an outcome, rather than only the foundational and more extensively studied strategy of controlled experimentation, in the context of a scenario in which students had to investigate multiple factors affecting the performance of potential astronauts in a space simulator. The intervention sought to help students see the purpose and value of scientific practices using social science content rather than traditional science topics. In addition to adapting the technology into a regular classroom setting in which the teacher is actively engaged (teacher-involved condition), the study included a second condition to determine if the technology could be used effectively without active teacher involvement (tech-only condition). Delayed far-transfer assessments showed that only students in the teacher-involved condition (but not the tech-only condition) outperformed those in a non-participating control group in recognizing the need for evidence and considering all contributing factors in making predictions. Furthermore, post-hoc analysis showed that these significant differences occurred predominantly among those who mastered the foundational control of variable skills. Possibilities are considered as to why teacher involvement was critical to effectiveness, and implications for classroom practice are addressed.
Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8BP1K7K |
Date | January 2018 |
Creators | Arvidsson, Toi Sin |
Source Sets | Columbia University |
Language | English |
Detected Language | English |
Type | Theses |
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