scispace - formally typeset
Search or ask a question

Does students learning competences for industry 4.0? 


Best insight from top research papers

Students learning competences for Industry 4.0 are a focus of research and educational innovation. Universities and institutions recognize the need to bridge the gap in students' education regarding new technologies and digital transformation competencies . The dominant aspects of Industry 4.0, such as artificial intelligence, big data analytics, and cloud computing, need to be incorporated into the curriculum to prepare students for the future . Teachers also need to develop competencies to face the industrial era 4.0, including critical thinking, collaboration, communication, and creativity . The competency-based approach in training engineering students is becoming more relevant in today's digitalized society, with distance education being proposed as a tool for implementing this approach . Overall, there is a recognition of the importance of equipping students with the necessary competences to thrive in the context of Industry 4.0.

Answers from top 4 papers

More filters
Papers (4)Insight
The paper discusses the implementation of a competency-based approach in training engineering students for Industry 4.0, but it does not explicitly mention the learning competences for students in Industry 4.0.
The paper discusses the implementation of a competency-based approach in training engineering students for Industry 4.0, highlighting the importance of developing professional competences for future engineers.
The paper discusses the need for universities to update their curriculum to include technologies relevant to Industry 4.0, suggesting that students should learn competences for Industry 4.0.
The paper states that by introducing Industry 4.0 into the industrial engineering curriculum, students can improve their digital tools knowledge, problem-solving, teamwork, and communication skills. Therefore, students' learning competences for Industry 4.0 can be enhanced through this educational approach.

Related Questions

Please explain what is Industry 4.0?5 answersIndustry 4.0 refers to the fourth industrial revolution, characterized by the integration of physical systems with cybersystems to improve efficiency and digitize manufacturing processes. It involves the use of technologies such as the Internet of Things (IoT), cloud computing, artificial intelligence (AI), and cyber-physical systems (CPS). Industry 4.0 enables machines to communicate with each other and with computer systems, allowing for remote monitoring and control of manufacturing processes. It also involves the collection and analysis of large volumes of data to enable quick and effective decision-making. The implementation of Industry 4.0 can lead to increased production and process efficiency, reduced expenses, improved product quality, and customer satisfaction. National and international strategies are being developed to adapt to this new era of manufacturing. Overall, Industry 4.0 represents a shift towards digitalization and automation in the manufacturing industry, with the aim of improving productivity and competitiveness.
What is the industry 4.0?4 answersIndustry 4.0 refers to the fourth industrial revolution, which involves the automation of industrial processes, digitalization, and the use of data to improve production efficiency and quality. It is driven by emerging technologies such as the Internet of Things (IoT), digital twins, artificial intelligence, cloud computing, and edge/fog computing. Industry 4.0 aims to connect physical assets to cybernetworks, capture and analyze data, and use AI-based systems for production monitoring, quality inspection, and process control. The implementation of Industry 4.0 technologies can lead to significant efficiency improvements and cost reductions in various industrial sectors. It also has implications for education, with the concept of Education 4.0 aiming to modernize and apply education to the age of Industry 4.0 using new technologies and methods. Overall, Industry 4.0 represents a technological shift towards smart cyberphysical systems and sustainable economic development.
1.Which are the key components and uses of Industry 4.0 technology?5 answersIndustry 4.0 technology encompasses various components and applications. These include Internet of Things (IoT), Cyber-Physical Systems (CPS), Augmented Reality (AR), Cloud Computing (CC), Internet of Services (IoS), Big Data Analytics (BDA), Artificial Intelligence (AI), Digital Twins (DT), Blockchain (BC), Industrial Robotics (IR), and Additive Manufacturing (AM). These technologies have the potential to improve key performance indicators (KPIs) for creating a Resilient Supply Chain 4.0, such as flexibility, redundancy, visibility, agility, collaboration, robustness, and information sharing. Additionally, Industry 4.0 IoT technology can be used for monitoring stock status, automating ordering and distribution processes, optimizing delivery routes, reducing logistics and administrative costs, and improving customer service. The growth of industrialization in the 21st century depends significantly on the progressive involvement of AI applications, which have shown remarkable success in various sectors such as automotive, consumer products, industrial manufacturing, and telecommunications. AI, machine learning, and big data analytics technologies play a vital role in the realization and adoption of Industry 4.0, offering numerous merits and contemporary solutions. These technologies can also be integrated to create innovative solutions for tracking and managing inventory resources in real-time, replacing traditional methods and improving efficiency in industries.
What are the competencies required for Industry 4.0 in the pharmaceutical environment?5 answersThe competencies required for Industry 4.0 in the pharmaceutical environment include leadership, strategic vision of knowledge, self-organization, giving and receiving feedback, pro-activity, creativity, problem solving, interdisciplinarity, teamwork, collaborative work, initiative, communication, innovation, adaptability, flexibility, and self-management. Additionally, knowledge of contemporary fields such as information and communication technology, algorithms, automation, software development and security, data analysis, general systems theory, and sustainable development theory is necessary.
What qualities and abilities do engineers need in the Industry 4.0 era?5 answersEngineers in the Industry 4.0 era require a combination of technical and soft skills. Technical competencies such as mathematics, physics, chemistry, and logic are essential. However, the industry also demands socio-emotional skills, particularly teamwork, leadership, networking, and multiculturalism. Additionally, engineers need to develop project competencies, entrepreneurship, creativity, and innovation. In the context of Industry 5.0, engineers and managers should possess knowledge in areas such as management, innovation, and technology. Furthermore, engineers in the Industry 4.0 era must have cognitive abilities to solve interdisciplinary problems in a digital environment, along with social-emotional skills like communication, motivation, autonomy, perseverance, resilience, collaboration, and creativity.
What's the interplay between sustainability and Industry 4.0?5 answersIndustry 4.0 and sustainability are interconnected, with Industry 4.0 technologies enabling sustainable development goals in economic, environmental, and social dimensions. The implementation of sustainability strategies, particularly in the context of circular economy practices, pushes industries to revise their business models and form unexpected collaborations with stakeholders such as suppliers, customers, and even competitors, leading to sustainable economic growth and durability. However, there is a need for a comprehensive assessment of social, governance, and life cycle aspects in sustainability analysis, as current studies mainly focus on technological and environmental issues. The digital transition and Industry 4.0 technologies provide opportunities for companies to measure sustainability performance using proper indicators and metrics, considering the Triple Bottom Line concept of environmental, economic, and social impacts. While Industry 4.0 can improve energy and material efficiency, it is important to consider the wider impacts and unintended harm that may arise, and sustainable engineering approaches can help mitigate these risks.

See what other people are reading

What are the benefits of cooperatives for rural development?
5 answers
Cooperatives have several benefits for rural development. They provide a mechanism for collective bargaining and protection of the financial interests of the impoverished and underrepresented in society. Cooperatives, particularly agricultural cooperatives, play a crucial role in achieving sustainability goals by effectively using local resources and providing solutions to economic, social, and environmental problems in rural areas. They contribute to the economic development of societies by creating employment opportunities and increasing income levels. Cooperatives also have the potential to reduce rural poverty and enhance social inclusion by promoting members' socio-economic development and increasing people's control over local economies. The Integrated Cooperative Model (ICM) has been effective in linking the rural poor in terms of production, marketing support, and financial services, and member-based education and training are recommended to enhance cooperative effectiveness. Positive government intervention is also needed to punish those who misuse cooperative funds.
How does technology support personalized learning for studnets?
5 answers
Technology supports personalized learning for students by utilizing various advancements such as artificial intelligence, big data analysis, and adaptive content. These technologies enable the recording and analysis of students' learning process and results, allowing teachers to understand their individual learning dynamics and provide targeted support. Personalization in vocational training is achieved through the use of modern information and communication technologies, allowing students to create personalized educational trajectories based on their unique characteristics. Personalized recommendation systems aid in selecting interesting courses from a vast array of online resources, increasing learners' efficiency and satisfaction. Additionally, the integration of artificial intelligence and machine learning techniques in virtual learning environments enables learner profiling and continuous refinement of personalized education. These advancements in technology provide educators with accurate teaching ideas and support students' personalized and diversified development, ultimately enhancing the effectiveness and performance of the learning process.
Influence of Fossil Fuel Dependency on EU Energy Security Strategy?
4 answers
The EU's energy security strategy is heavily influenced by its dependency on fossil fuels. The reliance on Russian fossil fuel imports has posed challenges to the energy security of European countries, particularly those heavily dependent on Russian resources. The energy crisis in Europe, conflicts between Russia and Ukraine, and rising energy costs have further exacerbated the issue. To address this, European countries have implemented various strategies such as diversifying energy sources, increasing energy efficiency, and promoting renewable energy. The EU has also imposed energy bans and implemented policies to reduce dependency on Russian energy imports. Transitioning to renewable energy sources and reducing dependency on Russian imports are seen as crucial steps towards enhancing energy security in the EU. By adopting these measures, the EU aims to achieve carbon neutrality and decarbonize its energy industry while ensuring energy security.
What are thebias and discrimination by AI ?
4 answers
Bias and discrimination in AI refer to the unfair treatment or unequal outcomes that can occur as a result of algorithmic decision-making. Algorithmic discrimination can manifest in various ways, including feature-selective discrimination, associative discrimination, and big data-enabled price discrimination. The causes of algorithmic discrimination can be attributed to original bias, learning bias, and external bias. Efforts to regulate bias and discrimination in AI systems focus on identifying and minimizing risks rather than imposing blanket bans. Algorithmic bias can lead to unfair results and inequalities, shaping people's thinking and potentially resulting in discrimination. In the context of mortgage applications, bias has been observed, with black applicants being more likely to be declined a mortgage compared to white applicants with similar circumstances. The use of machine-learning models without human involvement can amplify bias and discrimination, cautioning against their use in decision-making with significant implications.
How to monitor air quality?
5 answers
Air quality can be monitored using various methods. One approach is to use an air quality monitoring system that utilizes sensors and microcontrollers to detect the concentration of pollutants in the air. These systems can measure the ppm levels in the air and classify it as fresh or poor air. The challenges in designing such systems include establishing reliable connectivity, visualizing data effectively, integrating multiple sensors, ensuring accurate measurements, and enabling real-time analysis. To address these challenges, microcontrollers like ESP32 with built-in Wi-Fi and Bluetooth capabilities can be used for connectivity. Additionally, the integration of sensors like MQ2 for gas detection and DHT11 for temperature and humidity measurement enables comprehensive air quality monitoring. Cloud-based IoT platforms like ThingSpeak can be utilized for data logging, remote monitoring, and real-time alerts and notifications. By implementing these systems, real-time monitoring of air quality can be achieved, providing an essential tool for public health and environmental protection.
To what extent is social media contributing to social justice?
4 answers
Social media is playing a significant role in contributing to social justice. It has become a platform for various movements to create awareness and bring justice to various issues. Twitter, in particular, has allowed users to freely discuss social justice issues, such as sexual assault cases, using hashtags like #BlackTwitter and #MeToo. Engagement with sociopolitical content on social media platforms has been found to be associated with critical consciousness, which includes awareness of inequities and motivation to address them. Additionally, social media has contributed to the advancement of women's education and justice in South Asian countries, making education more accessible and bringing people together to support the oppressed. Overall, social media's impact on social justice is evident through the creation of mass movements, discussions on important issues, and the development of critical consciousness among users.
What is femism?
4 answers
Feminism is an ideology and humanistic philosophy that advocates for social equality between men and women, opposing patriarchy and sexism. It is a movement that seeks the equality of the sexes and the rights of women, with origins in Enlightenment thinking. Feminism has a significant influence on both national and international politics. In literature, feminism involves the examination and analysis of the portrayal of women characters and the appreciation of female authors, as well as setting standards for literature from a feminist perspective. Feminism in the Indian context is a recent renovation of early Vedic concepts of man-woman relationships, influenced by Western liberalism and feminist thought. Feminism aims for the economic, political, and social equality of the sexes, with different theoretical underpinnings and action plans.
Issues on education here in the philippines
5 answers
The education system in the Philippines faces several challenges including unequal access, uneven quality of education, and underdeveloped research and innovation systems. Despite significant education reforms, the sector still struggles with issues of inclusion, equity, and quality. Inadequate household income, poor implementation of laws and policies, and varying quality of institutions contribute to these challenges. The COVID-19 pandemic has further highlighted problems such as difficulty in distributing learning materials, lack of equipment and internet access, and the unpreparedness of parents and teachers for distance learning. However, there are also opportunities for growth and development, such as the increased use of online learning and partnerships with the private sector. To address these issues, the government has implemented initiatives like free tuition and international benchmarking, and there is a focus on the internationalization of higher education and the use of technology.
What is the critical discussion about digital transformation in healthcare?
5 answers
Digital transformation in healthcare is a critical area of research that has the potential to significantly transform the healthcare industry and improve health outcomes for individuals and communities. The adoption of digital technologies such as telemedicine, electronic health records, wearables, and mobile health applications has already started to revolutionize healthcare delivery. However, the adoption of these technologies is not without its challenges, including privacy and security concerns, regulatory barriers, and resistance from healthcare providers. The transition to digital transformation is a difficult task because of healthcare's reliance on legacy technologies, as well as serious regulatory problems and security issues. It is important for healthcare organizations and policymakers to address these challenges and implement strategies to ensure the effective integration of digital technologies into the healthcare system. Overall, the critical discussion about digital transformation in healthcare revolves around the potential benefits and challenges associated with the adoption of digital technologies to improve healthcare delivery and patient outcomes.
What are the challenges and limitations of using machine learning with small data?
4 answers
Machine learning with small data poses several challenges and limitations. The limited availability of data makes it difficult to achieve reliable model generalization and transferability, leading to poor performance in real-world applications. Small data problems are compounded by issues such as data diversity, imputation, noise, imbalance, and high-dimensionality. Additionally, the small sample size hinders the ability to learn and generalize effectively, which is a key difference between human and artificial intelligence. The nature of experimental organic chemistry often restricts practitioners to small datasets, limiting the application of machine learning techniques. To address these challenges, various techniques have been proposed, including transfer learning, self-supervised learning, and generative models, which have shown promising potential in overcoming the limitations of small data. By adopting a holistic data-centric approach and leveraging statistical analysis, the value of small data can be maximized in chemistry research.
What are competencies related to AI Literacy?
5 answers
Competencies related to AI literacy include understanding AI, using and applying AI, detecting AI, AI ethics, creating AI, AI self-efficacy in learning and problem solving, and AI self-management. These competencies are important for individuals to navigate and interact with AI technologies effectively. Additionally, psychological competencies such as problem solving, learning, and emotion regulation in regard to AI are also considered in the context of AI literacy. Developing AI literacy is crucial for various groups including young learners, professionals, and educators, as AI becomes integrated into different aspects of society. AI literacy can be measured using questionnaires and quantitative scales that assess different facets of AI literacy, including awareness, use, evaluation, and ethics. Overall, AI literacy encompasses a range of competencies that are essential for individuals to understand, engage with, and make informed decisions about AI technologies.