In this study, we analyzed students’ reasoning and explanations of friction concepts before and after engaging in guided experimentation with visuohaptic (VH) simulations. The VH experimentation included two affordances: visual cues and haptic feedback. Specifically, we analyzed the outcomes of two treatment groups with different sequences of affordance introduction. The first treatment group started with visual cues, with haptic feedback added later, while the second treatment group started with haptic feedback and added the visual cues later. We recruited 48 students who had previously taken at least one physics course. Participants completed a pre‐ and posttest assessment, which included both procedural and conceptual questions about friction before and after the guided experimentation task. The results show that the participants from both treatment groups benefited from using VH simulations. Both treatment groups showed statistically significant pre/post improvements in their understanding of friction. Moreover, both treatment groups showed a statistically significant increase in the conceptual understanding of friction concepts from pretest to posttest with moderate to strong effect sizes. Implications for laboratory instruction are also discussed.
Tugba Yuksel is a Ph.D. candidate in curriculum and Instruction department at Purdue University. She has received her B.S and M.S degrees in physics education program from Hacettepe University in Turkey. She registered to a master program in department of physics at Ankara University in Turkey. At the end of the second semester, she leave her program and came to Purdue university. She holds another M.S degree in science education from Purdue University. Her research interest is mainly on examining how undergraduate level students use their model-based reasoning in the process of learning quantum mechanics and identifying new instructional strategies which helps to support visualization and modelbased reasoning.
Engineering and Physics Students' Perceptions about Learning Quantum Mechanics via Computer Simulations AbstractQuantum mechanics (QM) is an important topic in engineering and physics, necessary for both the mathematical and physical prediction and explanation of a particle's behavior at atomic and subatomic levels. Computer simulations provide an advantage for helping students make sense of abstract concepts and visualize complex phenomena in the process of developing a conceptual understanding of quantum knowledge. Students' experiences and attitudes about learning via computer simulations can inform educational design and improve content delivery. In this paper, we studied students' perspective about how simulations influenced their QM learning. Results of this study showed that most students agreed that simulations are helpful for their QM learning. The positive feedback from freshman and sophomore students focused mainly on basic interactive functions of simulations. Such functionality included: ease of operation, direct visualization, and animated demonstration. Senior level students were more critical of simulations in their descriptions, pointing out the limitations of the models behind the physical explanations and the authenticity of the computational representations. Based on these research findings, we provided recommendations to improve simulation-based instructional design.
Background and Motivation
Research suggests that students' conceptual models play an essential role in their understanding. Therefore, model-based inquiry has been considered as an instructional method in which learners have the opportunity to actively build and use their models. In this study, we investigated students' model evolution during their learning experience with model-based instruction. We analyzed students' model transition process as they engaged with a sequence of activities supported with physical, computer-based, and mathematical models. We compared the results with students' who received traditional computer-based instruction. Results show that students who received model-based inquiry instruction increased the sophistication of their explanation and gained more accurate understanding compared to traditional compute-based instruction group.
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