On the Development of Laboratory Projects in Modern Engineering Education

Bye, Robin Trulssen; Osen, Ottar L.

doi:10.1109/educon.2019.8725086

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…Authors in [36] believe that because of the rapid improvements in technology, educators are constantly challenged to come up with new and relevant laboratory projects. This can be a time-consuming and costly process that, if neglected, will result in out-of-date laboratory projects and a decline in student learning outcomes.…”

Section: Low-cost Solutions For Modern Technology Educationmentioning

confidence: 99%

Low-Cost Education Kit for Teaching Basic Skills for Industry 4.0 Using Deep-Learning in Quality Control Tasks

et al. 2022

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The main purposes of this paper are to offer a low-cost solution that can be used in engineering education and to address the challenges that Industry 4.0 brings with it. In recent years, there has been a great shortage of engineering experts, and therefore it is necessary to educate the next generation of experts, but the hardware and software tools needed for education are often expensive and access to them is sometimes difficult, but most importantly, they change and evolve rapidly. Therefore, the use of cheaper hardware and free software helps to create a reliable and suitable environment for the education of engineering experts. Based on the overview of related works dealing with low-cost teaching solutions, we present in this paper our own low-cost Education Kit, for which the price can be as low as approximately EUR 108 per kit, for teaching the basic skills of deep learning in quality-control tasks in inspection lines. The solution is based on Arduino, TensorFlow and Keras, a smartphone camera, and is assembled using LEGO kit. The results of the work serve as inspiration for educators and educational institutions.

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Section: Low-cost Solutions For Modern Technology Educationmentioning

confidence: 99%

Low-Cost Education Kit for Teaching Basic Skills for Industry 4.0 Using Deep-Learning in Quality Control Tasks

et al. 2022

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“…Evidence shows that laboratory experiments provide insights into a phenomenon's model identification, offer mechanisms for model validation and limitations of its assumptions, allow for the prediction of the performance of complex systems, afford the testing and compliance with specifications of devices and enable the exploration for new fundamental information [16,27,57]. In recent years, with the rapid advancement of technology, the need of using modern equipment and new methodologies (e.g., virtual laboratories) to develop laboratory projects has been emphasized and used by multiple researchers, especially by those from developed countries [18,22,61,73]. For instance, computer simulations have been mainly used in teaching fundamental concepts of FM and computational fluid dynamics through both interactive and noninteractive virtual experimentation [1,32,38,77,103].…”

Section: Introductionmentioning

confidence: 99%

Combining hands‐on and virtual experiments for enhancing fluid mechanics teaching: A design‐based research study

Gutiérrez

Escusa

Lyon

et al. 2022

Comp Applic In Engineering

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Fluid Mechanics courses comprise both theoretical and laboratory modules. In developing nations, computer‐assisted techniques are not commonly applied in Fluid Mechanics instruction. Forced by the COVID‐19 pandemic, South American universities are, however, using them for online teaching. This contribution presents an 8‐semester (2016–2019) educational intervention over an undergraduate Fluid Mechanics course. It mainly blends physical (hands‐on) and virtual experiments (computer fluid dynamics‐based simulations) for the laboratory module, which are complemented by flipped classroom‐based prompts for the theoretical module. The intervention follows design‐based research as a research method and is guided via conjecture mapping and fidelity of implementation standards. Our results suggest that the intervention improves fluid mechanics laboratory instruction, although improvements depend upon the participation of other educational actors such as teaching assistants and laboratory technicians to some extent. Laboratory report grades (the assessment instrument) follow the Gompertz probability distribution. Following UNESCO standards, a portion of the intervention output is shared as open educational resources. This contribution encourages upscaling the educational intervention through the formation of cooperative clusters to build common‐pool Fluid Mechanics resources. Learning scientists have underlined the need to better understand laboratory instruction processes. They have been addressed in very few instances in developing countries. We believe that this study has the potential to provide valuable insights on the matter.

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“…Laboratory training is fundamental in engineering education [1][2][3][4][5][6][7][8][9][10], but understanding how the experimental activities contribute to meaningful learning and to achieve the goals established for a specific curricular unit (CU) is challenging [11][12][13][14]. Researchers often classify the educational engineering laboratories as follows: (i) hands-on with real equipment; (ii) simulated or virtual; and (iii) remote [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Effect of Laboratory Activities on Core Curricular Units of an Engineering Master’s Program: A Multivariate Analysis

Lopes

Silva

Seabra

2021

Mathematical Problems in Engineering

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This paper studies the effect of laboratory activities on the results obtained by the students on 7 core curricular units (CUs) of an Integrated Master in Mechanical Engineering. Each CU is characterized by means of 4 performance indices, over the 6-year period 2014/2015–2019/2020. Firstly, individual indices in a per semester basis are compared. Secondly, the CUs are regarded as objects defined in a 4-dimensional space of features, and the multidimensional scaling (MDS) technique is adopted for clustering and computer visualization. The MDS is powerful for analyzing the multivariate dataset, unveiling patterns not perceived by standard statistical methods.

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On the Development of Laboratory Projects in Modern Engineering Education

Cited by 8 publications

References 34 publications

Low-Cost Education Kit for Teaching Basic Skills for Industry 4.0 Using Deep-Learning in Quality Control Tasks

Low-Cost Education Kit for Teaching Basic Skills for Industry 4.0 Using Deep-Learning in Quality Control Tasks

Combining hands‐on and virtual experiments for enhancing fluid mechanics teaching: A design‐based research study

Assessing the Effect of Laboratory Activities on Core Curricular Units of an Engineering Master’s Program: A Multivariate Analysis

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