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Abstract
Early studies show that learning with mobile devices, also known as mobile learning, improves students’ learning in authentic contextual learning–i.e., learning connected to the real world. However, no empirical evidence has yet to firmly prove the effects of mobile technology on specific student skillsets such as learning scalability which means learning can be applied in various scenarios and learning sustainability which means learning can be sustained in real-world environments. Therefore, this study aims to explore the effect of learning using a mobile app called mobile Smart-Physics on learning cognitive levels, learning scalability (e.g., number of learning locations and number of experimental data), and learning sustainability (e.g., number of completed assignments). Eleventh-grade vocational high school students volunteered for this quasi-experiment and were divided into an experimental group (EG), which used Smart-Physics, and a control group (CG), which used a mobile Ubiquitous-Physics (U-Physics) app. The findings show that the EG significantly outperformed the CG concerning learning cognitive levels, learning scalability and learning sustainability. Smart-Physics features enabled the students to tackle technical and pedagogical difficulties during physical investigations in real-world environments and, in some cases, improved their task accomplishment and sustained their motivation to learn. Location awareness promoted the students’ authentic experiential learning, which sharpened their ability to apply learning in real-world environments and upload more experimental data. Feedback helped the students consolidate their physics theories and practical experiences, thereby generating more learning records with meaningful multimedia content like experimental graphs, tables, and notes in various learning locations. Therefore, we encourage practitioners to use smart learning environment features in their learning tools and activity designs.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Research Article
EURASIA J Math Sci Tech Ed, Volume 20, Issue 8, August 2024, Article No: em2491
https://doi.org/10.29333/ejmste/14917
Publication date: 06 Aug 2024
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