Studentsxe2x80x99 guided inquiry with simulation and its relation to school science achievement and scientific literacy Abstract   The study investigates the effect of an interactive simulation with embedded inquiry support, which was seamlessly embedded within the simulation, on studentsu0027 scientific literacy and school science achievement, and explores the relationships among prior school science achievement, inquiry processes and scientific literacy. A total of 49 eighthgrade students at a public junior high school in northern Taiwan participated. Data collected include the studentsu0027 pre and postscores for school science achievement, logging data that indicate their inquiry processes, and pretest, posttest, and delayedtest data that measure their scientific literacy. The results provide evidence that the designed simulation and inquiry support had a longterm effect on the studentsu0027 scientific literacy. Replacing conventional teaching with inquiry activities did not harm the studentsu0027 school science achievement performances. Moreover, compared to school science achievement, studentsu0027 scientific literacy seems a better predictor of their inquiry behavior, especially in the aspect of making conclusions. Analyses of the studentsxe2x80x99 inquiry processes indicate that the socalled low science achieving students conducted more data analyses than the other students, and demonstrated adequate inquiry engagement. The students with middle level school science achievement demonstrated the most active engagement in inquiry and showed good gains of scientific literacy after the learning. These results indicate that a guided inquiry learning environment can support students with different levels of school science achievement to highly engage in science inquiry. Implications and future studies are discussed.

Active Learning is About More Than HandsOn: A MixedReality AI System to Support STEM Education Along with substantial consensus around the power of active learning, comes some lack of precision in what its essential ingredients are. New educational technologies offer vehicles for systematically exploring benefits of alternative techniques for supporting active learning. We introduce a new genre of Intelligent Science Station technology that uses Artificial Intelligence (AI) to support children in learning science by doing science in the real world. We use this system in a randomized controlled trial that investigates whether active learning is best when it is implemented as guided deliberate practice, as constructive xe2x80x9chandsonxe2x80x9d activity, or as a combination of both. Automated, reactive guidance is made possible by a specialized AI computer vision algorithm we developed to track what children are doing in the physical environment as they do experiments and discoveries with physical objects. The results support deliberate practice and indicate that having some guided discovery based on effective learning mechanism such as selfexplanation, contrasting cases and personalized interactive feedback produces more robust learning compared to exploratory construction alone. Children learning through guided discovery achieve greater understanding of the scientific principles than children learning through handson construction alone (4 times more pretopost test improvement). Importantly, a combined guided discovery and handson construction condition leads to better learning of the very handson construction skills that are the sole focus of the handson constructive learning condition (u003e10 times more pretopost improvement). These results suggest ways to achieve powerful active learning of science and engineering that go beyond the widespread temptation to equate handson activity with effective learning.