Active Learning based Laboratory towards Engineering Education 4.0

Delgado-Prieto, Miguel; Sobrino, Angel Fernandez; Soto, Lucía Ruiz; Romero, David A.; Biosca, Pere Fibla; Martínez, L.

doi:10.1109/etfa.2019.8869509

Cited by 28 publications

(13 citation statements)

References 11 publications

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“…Education 4.0 is based on learning by doing, in which students are encouraged to learn and discover in unique ways through experimentation [11]. Prieto et al [12] outlined an Industry 4.0 Technologies Laboratory (I4Tech Lab), a technological environment for academic research and industrial promotion of related technologies, supported by an active-learning teaching methodology. Another possibility is to use augmented reality in a modular learning system with an interactive virtual model of the equipment, technical data, and information processed in real-time [13].…”

Section: Education 40 In the Field Of Teaching Processesmentioning

confidence: 99%

Characterization of the Teaching Profile within the Framework of Education 4.0

et al. 2021

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The authors of the Education 4.0 concept postulated a flexible combination of digital literacy, critical thinking, and problem-solving in educational environments linked to real-world scenarios. Therefore, teachers have been challenged to develop new methods and resources to integrate into their planning in order to help students develop these desirable and necessary skills; hence, the question: What are the characteristics of a teacher to consider within the framework of Education 4.0? This study was conducted in a higher education institution in Ecuador, with the aim to identify the teaching profile required in new undergraduate programs within the framework of Education 4.0 in order to contribute to decision-making about teacher recruitment, professional training and evaluation, human talent management, and institutional policies interested in connecting competencies with the needs of society. Descriptive and exploratory approaches, where we applied quantitative and qualitative instruments (surveys) to 337 undergraduate students in education programs and 313 graduates, were used. We also included interviews with 20 experts in the educational field and five focus groups with 32 chancellors, school principals, university professors, and specialists in the educational area. The data were triangulated, and the results were organized into the categories of (a) processes as facilitators (b), soft skills, (c) human sense, and (d) the use of technologies. The results outlined the profile of a professor as a specialized professional with competencies for innovation, complex problem solving, entrepreneurship, collaboration, international perspective, leadership, and connection with the needs of society. This research study may be of value to administrators, educational and social entrepreneurs, trainers, and policy-makers interested in implementing innovative training programs and in supporting management and policy decisions.

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Section: Education 40 In the Field Of Teaching Processesmentioning

confidence: 99%

Characterization of the Teaching Profile within the Framework of Education 4.0

et al. 2021

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“…Teaching 4.0 is the implementation of various technology through academic related supported teaching methodology to produce an active-learning outcome (Prieto et al, 2019). It might also be the use of augmented reality tools in a modular learning system with an interactive virtual model of the equipment (Miranda & Molina, 2020), cloud computing data, and information processed in real-time (Martin et al, 2018).…”

Section: Technology In Teaching 40mentioning

confidence: 99%

Teaching 4.0 Competency in Higher Learning Institutions: A Systematic Mapping Review

Masdoki

Din

2021

IJLTER

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Technology 4.0 has forced the education system to undergo a huge transformation by changing teaching and learning methods. Teaching 4.0 requires teachers to apply varieties of technology used in the teaching and learning process to make it more appealing to new millennials. However, the response to this change has been relatively slow. Consequently, the preparation of effective teaching methods and strategies from educators that can benefit students through learning from differentiated learning styles. Thus, the aim of this study is to collect and extract information from the literature in searching for the research gap, and analyse the most significant studies on the teaching of 4.0 competency in a Higher Learning Institution. The searching process will focus on papers published in journals or presented at specialised international conferences from 2015 until 2021 using a systematic mapping review (SMR). A total of 380 relevant research papers from Scopus, Web of Science, Google Scholars online databases and grey literature were retrieved. Nine final papers were selected in the study. Results from the review showed that the predefined requirement criteria for Teaching 4.0 competencies were not all satisfied in literature. There are limited studies on the Teaching 4.0 competency. Thus, a conceptual framework of measurement for Teaching 4.0 competencies should be developed. It could act as a solution by providing a comprehensive competency measurement and to determine relevant 4.0 competency among Higher Learning Institution educators.

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“…In this paradigm, we can consider the term "competency" as the set of skills, values, and knowledge that allows the student to successfully perform a given task or activity [6]. In order to develop the Education 4.0 curricula for engineers, ref.…”

Section: Introductionmentioning

confidence: 99%

Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems

et al. 2021

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One of the main focuses of Education 4.0 is to provide students with knowledge on disruptive technologies, such as Machine Learning (ML), as well as the skills to implement this knowledge to solve real-life problems. Therefore, both students and professors require teaching and learning tools that facilitate the introduction to such topics. Consequently, this study looks forward to contributing to the development of those tools by introducing the basic theory behind three machine learning classifying algorithms: K-Nearest-Neighbor (KNN), Linear Discriminant Analysis (LDA), and Simple Perceptron; as well as discussing the diverse advantages and disadvantages of each method. Moreover, it is proposed to analyze how these methods work on different conditions through their implementation over a test bench. Thus, in addition to the description of each algorithm, we discuss their application to solving three different binary classification problems using three different datasets, as well as comparing their performances in these specific case studies. The findings of this study can be used by teachers to provide students the basic knowledge of KNN, LDA, and perceptron algorithms, and, at the same time, it can be used as a guide to learn how to apply them to solve real-life problems that are not limited to the presented datasets.

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Active Learning based Laboratory towards Engineering Education 4.0

Abstract: Portal del coneixement obert de la UPC http://upcommons.upc.edu/e-prints Aquesta és una còpia de la versió author's final draft d'un treball a congrés

Cited by 28 publications

References 11 publications

Characterization of the Teaching Profile within the Framework of Education 4.0

Characterization of the Teaching Profile within the Framework of Education 4.0

Teaching 4.0 Competency in Higher Learning Institutions: A Systematic Mapping Review

Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems

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