Responsive educational proposals to develop skills to meet the demands of Industry 4.0 have become imperative to guarantee inclusive, equitable, and quality education and promote lifelong learning opportunities for all, also reducing the negative impact of COVID-19 and the major post-pandemic social issues. This article analyzes which components of Education 4.0 have been considered in 21st century skills frameworks and identifies the teaching and learning methods and key stakeholders impacted. We conducted a systematic literature review (SLR) with research questions to highlight studies that address 21st century frameworks worldwide, identifying which teaching–earning strategies contain 4.0 components, their learning dimensions, and the targeted stakeholders. The findings allowed us to identify opportunities to create or improve 21st century skills frameworks with the required Education 4.0 components to develop future skills. Our study revealed the absence of these frameworks for teachers and schools. Most are oriented toward students, developing competencies through the dimensions of character, meta-learning, and linking active learning teaching strategies. This work presents studies incorporating innovative educational practices and the core Education 4.0 components. It concludes with a reflection on creating educational models to develop complex-reasoning competencies and auto-systemic thinking to support problem-solving and address social needs.

/pdf/components-of-education-4-0-in-21st-century-skills-2t9hd4b6.pdf

Components of Education 4.0 in 21st Century Skills Frameworks: Systematic Review

Construction projects and cities account for over 50% of carbon emissions and energy consumption Industry 4 0 and digital transformation may increase productivity and reduce energy consumption A digital twin (DT) is a key enabler in implementing Industry 4 0 in the areas of construction and smart cities It is an emerging technology that connects different objects by utilising the advanced Internet of Things (IoT) As a technology, it is in high demand in various industries, and its literature is growing exponentially Previous digital modeling practices, the use of data acquisition tools, human–computer–machine interfaces, programmable cities, and infrastructure, as well as Building Information Modeling (BIM), have provided digital data for construction, moni-toring, or controlling physical objects However, a DT is supposed to offer much more than digital representation Characteristics such as bi-directional data exchange and real-time self-management (e g , self-awareness or self-optimisation) distinguish a DT from other information modeling sys-tems The need to develop and implement DT is rising because it could be a core technology in many industrial sectors post-COVID-19 This paper aims to clarify the DT concept and differentiate it from other advanced 3D modeling technologies, digital shadows, and information systems It also intends to review the state of play in DT development and offer research directions for future investigation It recommends the development of DT applications that offer rapid and accurate data analysis platforms for real-time decisions, self-operation, and remote supervision requirements post-COVID-19 The discussion in this paper mainly focuses on the Smart City, Engineering and Construction (SCEC) sectors © 2021 by the author Licensee MDPI, Basel, Switzerland

Differentiating digital twin from digital shadow: Elucidating a paradigm shift to expedite a smart, sustainable built environment

Energy storage plays an important role in the adoption of renewable energy to help solve climate change problems. Lithium-ion batteries (LIBs) are an excellent solution for energy storage due to their properties. In order to ensure the safety and efficient operation of LIB systems, battery management systems (BMSs) are required. The current design and functionality of BMSs suffer from a few critical drawbacks including low computational capability and limited data storage. Recently, there has been some effort in researching and developing smart BMSs utilizing the cloud platform. A cloud-based BMS would be able to solve the problems of computational capability and data storage in the current BMSs. It would also lead to more accurate and reliable battery algorithms and allow the development of other complex BMS functions. This study reviews the concept and design of cloud-based smart BMSs and provides some perspectives on their functionality and usability as well as their benefits for future battery applications. The potential division between the local and cloud functions of smart BMSs is also discussed. Cloud-based smart BMSs are expected to improve the reliability and overall performance of LIB systems, contributing to the mass adoption of renewable energy.

/pdf/concept-review-of-a-cloud-based-smart-battery-management-2dohnfry.pdf

Concept Review of a Cloud-Based Smart Battery Management System for Lithium-Ion Batteries: Feasibility, Logistics, and Functionality

Education 4.0 is being empowered more and more by artificial intelligence (AI) methods. We observe a continuously growing demand for adaptive and personalized Education. In this paper we present an innovative approach to promoting AI in Education 4.0. Our first contribution is AI assisted Higher Education Process with smart sensors and wearable devices for self-regulated learning. Secondly we describe our first results of Education 4.0 didactic methods implemented with learning analytics and machine learning algorithms. The aim of this case study is to predict the final score of students before participating in final examination. We propose an Early Recognition System equipped with real data captured in a blended learning course with a personalized test at the beginning of the semester, an adaptive learning environment based on Auto Tutor by N. A. Crowder theory with adaptive self-assessment feedback. It is obvious that focusing on students' success and their experiences is a win-win scenario for students and professors as well as for the administration.

Education 4.0 - Artificial Intelligence Assisted Higher Education: Early recognition System with Machine Learning to support Students' Success

Educational activity is increasingly moving online and course contents are becoming available in digital format. This enables data collection and the use of data for analyzing learning process. For the 4th Revolution in Education, an active and interactive presence of students contributes to a higher learning quality. Machine Learning techniques recently have shown impressive development steps of the use of data analysis and predictions. However, it has been far less used for assessing the learning quality. For this paper we conducted analysis based on neural networks, support vector machine, decision trees and cluster analysis to estimate student's performance at examination and shape the next generation's talent for Industry 4.0 skills.

Education 4.0 — Fostering student's performance with machine learning methods

We present an Adaptive User Interface (AUI) for online courses in higher education as a method for solving the challenges posed by the different knowledge levels in a heterogeneous group of students. The scenario described in this paper is an online beginners' course in Mathematics which is extended by an adaptive course layout to better fit the needs of every individual student. The course offers an entry-level test to check each student's prior knowledge and skills. The results are used to automatically determine which parts of the course are relevant for the student and which ones can be hidden, based on parameters set by the course teachers. Initial results are promising; the new adaptive learning platform in mathematics is leading to higher student satisfaction and better performance

Adaptive user interface for higher education based on web technology

This paper presents our results from Education 4.0, didactically implemented with an efficient Blended Learning 4.0 (BL 4.0) Process. During the reflection phase of the BL 4.0 Process we build up a Learning Analytics Cockpit (LA Cockpit). Therefore the Neural Network (NN) learn from the click data and the achieved points in the control questions of the courses (the first two months) as well as the pass / fail (classification) or the achieved points (regression) in the exam. We test the learned NN during the next execution of the learning course (achieved forecast accuracy 76%-100% in the classification) and use it again during the next semester. The learning of the NN and the testing are proceeding to the LA use. LA cockpit, implemented in PHP as a plugin for the learning management system Moodle, is only used to apply the learned NN during the working phase. We have learned NN for three courses. We have been able to test the effects of using the cockpit in two simulations by carrying out the first successful applications in courses mathematics and information and knowledge management of business administration degree in WS 17/18 and SS 18. Based on these forecasts, warning e-mails were sent to students classified as at risk. This almost halved the failure rate. Education 4.0 für Akademiker 4.0 Kompetenzen – BlendedLearning-4.0-Prozess mit Learning Analytics Cockpit Monica Ciolacu , Heribert Popp, Rick Beer, Leon Binder Abstract: Wir präsentieren unsere Ergebnisse von Education 4.0, didaktisch umgesetzt mit einem effizienten Blended-Learning-4.0-Prozess. In der Reflexionsphase des Blended-LearningProzesses bauen wir ein Learning Analytics Cockpit auf. Dafür lassen wir das Neuronale Netz Wir präsentieren unsere Ergebnisse von Education 4.0, didaktisch umgesetzt mit einem effizienten Blended-Learning-4.0-Prozess. In der Reflexionsphase des Blended-LearningProzesses bauen wir ein Learning Analytics Cockpit auf. Dafür lassen wir das Neuronale Netz 1 Technische Hochschule Deggendorf, Fachgebiet Wirtschaftsinformatik, Dieter-Görlitz-Platz 1, 94469, Deggendorf, monica.ciolacu@th-deg.de 2 University Politehnica of Bucharest, CETTI, Faculty of Electronics, Telecommunications and Information Technology, Bucharest, Romania 3 Technische Hochschule Deggendorf, Fachgebiet Wirtschaftsinformatik, Dieter-Görlitz-Platz 1, 94469, Deggendorf, heribert.popp@th-deg.de 4 Universität Passau, Fakultät Informatik, Innstraße 41, 94032 Passau, rick.beer@th-deg.de 5 Technische Hochschule Deggendorf, Studiengang Wirtschaftsinformatik, Dieter-Görlitz-Platz 1, 94469, Deggendorf, leon.binder@stud.th-deg.de Monica Ciolacu et al. (NN) aus den Klickdaten und den erreichten Punkten in den Kontrollfragen der Kurse (der ersten zwei Monate) sowie das Bestehen/Nichtbestehen (Klassifikation) bzw. die erreichten Punkte (Regression) in der Klausur lernen. Dann testen wir die gelernten NN bei der nächsten Durchführung des Kurses (erreichte Prognosegenauigkeit 76%-100% bei der Klassifikation) und setzen sie bei der nächsten Kursdurchführung ein. Das Lernen der NN und das Testen sind dem Cockpiteinsatz vorgelagert. Das LA Cockpit, das in PHP als Plugin zum Lernmanagementsystem Moodle implementiert ist, dient nur in der Arbeitsphase zur Anwendung des gelernten NN. Bisher lernten wir NN für drei Kurse und konnten in zwei Simulationen die Auswirkungen des CockpitEinsatzes testen indem wir im Fach Mathematik und im Fach Informationsund Wissensmanagement (IWM) des Studiengangs Betriebswirtschaftslehre im WS 17/18 bzw. SS 18 erste erfolgreiche Anwendungen durchführten. Basierend auf den Prognosen wurden Warn-EMails an als gefährdet eingestufte Studierende verschickt. Dadurch konnten die Durchfallquote in Mathematik und die Nichtantrittszahl in IWM nahezu halbiert werden.

/pdf/education-4-0-fur-akademiker-4-0-kompetenzen-blended-2mx42rpn7p.pdf

Education 4.0 für Akademiker 4.0 Kompetenzen - Blended Learning 4.0 Prozess mit Learning Analytics Cockpit (Education 4.0 for Tall Thin Engineer Competencies - Blended Learning 4.0 Process with Learning Analytics Cockpit).

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Education 4.0 — Fostering student's performance with machine learning methods

Adaptive user interface for higher education based on web technology

Education 4.0 für Akademiker 4.0 Kompetenzen - Blended Learning 4.0 Prozess mit Learning Analytics Cockpit (Education 4.0 for Tall Thin Engineer Competencies - Blended Learning 4.0 Process with Learning Analytics Cockpit).