Investigating the effectiveness of STEAM education on students’ conceptual understanding of force and energy topics

Özkan, Gülbin; Topsakal, Ünsal Umdu

doi:10.1080/02635143.2020.1769586

Cited by 56 publications

(37 citation statements)

References 31 publications

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“…These findings corroborate previous studies evaluating the effects of an integrated STEAM approach on learning. For instance, it has been found that STEAM pedagogies boost students' ability to conceptualize themes (Liliawati et al, 2018 ), improve the acquisition of concepts (Perignat and Katz-Buonincontro, 2019 ; Wandari et al, 2019 ; Ozkan and Topsakal, 2020 ), enhance disciplinary knowledge (Ceylan and Ozdilek, 2015 ), raise test scores (Chien and Chu, 2018 ) and benefits overall academic performance (Kim et al, 2014 ). The current study aligns with these results, finding that STEAM courses supported by Maker technology within the framework of engineering design can increase students' academic motivation and self-efficacy, thereby facilitating the acquisition of interdisciplinary knowledge.…”

Section: Discussionmentioning

confidence: 99%

A Curriculum Integrating STEAM and Maker Education Promotes Pupils' Learning Motivation, Self-Efficacy, and Interdisciplinary Knowledge Acquisition

2021

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Contemporary society expects learners to synthesize large amounts of available information and take advantage of interdisciplinary knowledge to tackle complex, real-world issues. STEAM education aims to cultivate students' ability to solve such problems through interdisciplinary thinking but is often represented by courses that are merely disjointed arrays of school subjects. On the other hand, Maker education harnesses society's enthusiasm for technological innovation and creativity but overlooks the scientific principles that underpin these processes. This research presents a novel elementary school course informed by the interdisciplinary principles of STEAM, integrated with Maker's focus on technology and creativity. The course design also utilized engineering design as a meta-thematic framework. A total of 164 third-grade pupils participated in the research, with responses analyzed using descriptive statistical methods. The findings indicated that the integrated design of the course promoted pupils' learning motivation, self-efficacy, and acquisition of interdisciplinary knowledge. These effects were not gender-specific and demonstrate the potential applicability of a STEAM/Maker integrated approach to curriculum design in other settings.

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Section: Discussionmentioning

confidence: 99%

A Curriculum Integrating STEAM and Maker Education Promotes Pupils' Learning Motivation, Self-Efficacy, and Interdisciplinary Knowledge Acquisition

2021

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“…As shown in Table 12, the majority of the studies did not utilize any software. It should be noted that most studies that did not employ software were found in the last 5 years, such as [42,50]. Next was 3D design software (such as Tinkercad, CAD), then online learning platform/website, and simulation software (such as PhET).…”

Section: What Kinds Of Rules Were Employed In Stem Activities During the Last 10 Years?mentioning

confidence: 99%

Perusing the Past to Propel the Future: A Systematic Review of STEM Learning Activity Based on Activity Theory

Gyasi¹,

Zheng²,

Zhou³

2021

Sustainability

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Education is the road to sustainability, creating the appropriate environment for learners to socialize and acquire knowledge and skills for the 21st century. This study reviews 53 studies on Science, Technology, Engineering, and Mathematics (STEM) learning activities from 2011 to 2020. In the past last 10 years, STEM education has gained attention, and little is known about how researchers designed and implemented learning activities. This systematic review based on activity theory reveals that STEM learning activities mostly involved elementary students in all STEM disciplines, with a sample size from 1 to 50. STEM learning activities emphasize mixed tasks, evaluating mixed learning outcomes with three STEM disciplines. Researchers mostly preferred project-based learning and problem-based learning methods, lasting from 9 to 24 weeks under teacher guidance with no rewards. This study revealed that most STEM activities were implemented in the classroom. Finally, the most often-used tools were mixed hardware. The quiz is the most often utilized in STEM activity. Major understudied areas that can be investigated by future studies are also revealed in depth. The results and implications for future studies are also discussed in detail.

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“…Analyzing a neuron regarding its activities is important, since understanding the way it works also helps us to construct the ANNs. An ANN may be seen as a black box which contains hierarchical sets of neurons (e.g., processing elements) producing outputs for certain inputs [42][43][44][45][46].…”

Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

“…The processing elements have links in between them forming a layer of networks. A neural network usually consists of an input layer, some hidden layers, and an output layer [42][43][44][45][46].…”

Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Experimental research and ANN modeling on the impact of the ball burnishing process on the mechanical properties of 5083 Al-Mg material

Başak¹,

Özkan²,

Toktaş³

2020

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5083 Al-Mg is the widely used material in food, chemistry, vehicle, machinery, and construction sectors, as well as in the aviation and space industries. The burnishing is normally used as the finishing operation for this material with the advantages such as surface roughness, reduced fracture formation, hardness, fatigue strength, and an increase of the wear resistance. These positive improvements are dependent on burnishing process parameters such as feed rate, burnishing force, ball diameter, and a number of revolutions. The study contains determination and optimization of the machining parameters and their effects on the surface roughness, microhardness, and the strength of 5083 Al-Mg material in the ball burnishing processes. Multiple regression and ANOVA analysis were performed to identify significant process parameters. A new Artificial Neural Networks (ANN) model with different neuron structures and algorithms has also been developed using experimental results to supplement the multiple regression model as the desired R 2 values could not be achieved with the latter. The ANOVA analysis indicated that both the burnishing force and the number of revolutions have a significant effect on the surface roughness and hardness with optimums 300 N and 200 rpm, respectively. Results from the two models were compared with each other. The developed ANN model is shown to estimate the surface roughness and the surface hardness with high reliability (R 2 = 0.999992) without costly experimental trials. K e y w o r d s : burnishing, surface roughness and hardness, microhardness, strength analysis, Artificial Neural Networks (ANN)

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Investigating the effectiveness of STEAM education on students’ conceptual understanding of force and energy topics

Cited by 56 publications

References 31 publications

A Curriculum Integrating STEAM and Maker Education Promotes Pupils' Learning Motivation, Self-Efficacy, and Interdisciplinary Knowledge Acquisition

A Curriculum Integrating STEAM and Maker Education Promotes Pupils' Learning Motivation, Self-Efficacy, and Interdisciplinary Knowledge Acquisition

Perusing the Past to Propel the Future: A Systematic Review of STEM Learning Activity Based on Activity Theory

Experimental research and ANN modeling on the impact of the ball burnishing process on the mechanical properties of 5083 Al-Mg material

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