Learning electric circuit principles in a simulation environment with a single representation versus xe2x80x9cconcreteness fadingxe2x80x9d through multiple representations Abstract   This study explored how learning in a simulation environment with a single representation compares to learning with concreteness fading in the domain of electric circuits in secondary school. Concreteness fading, an instructional technique where representations change from concrete to more abstract during a learning sequence, has been proposed as an approach to combine the strengths of each representation and to promote transfer, but the empirical evidence is not unequivocal. One reason may be that defining xe2x80x9cconcretexe2x80x9d and xe2x80x9cabstractxe2x80x9d for the purpose of concreteness fading is less straightforward in practice than in theory, as illustrated in the conceptualization of concreteness fading in this study. The results showed no differences in content coverage and total learning time between conditions, but the transition between representations temporarily increased learning time in the fading condition. An analysis of the learning revealed no differences between conditions on circuits similar in appearance to the simulation environment, but the single representation resulted in better application of the learned knowledge to realistic circuits. The discussion explores the results from the perspective of concreteness fading and alternative conceptualizations of the learning environment. One of these, resulting from the difficulty in positioning computer simulations within concretexe2x80x93abstract dimensions, is that computer simulations can include concrete and abstract in a single representation, which may be easier (and more effective) in classroom settings than multiple representations. This suggests that a welldesigned simulation can be a reckoned alternative to concreteness fading and to multiple representations in general in science education.

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.