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Industry 4.0: Learning Analytics Using Artificial Intelligence and Advanced Industry Applications
Abstract
Learning analytics is a crucial factor in measuring the effectiveness of any educational programme. Industry 4.0, mainly artificial intelligence (AI) applications are integrated into the digital pedagogical techniques and the learning analytics that further enhance and develop the online instructional materials. Therefore, it is crucial to explain the usage of those technologies used for the learning analytics associated with the educational system to show the different use of AI. This book chapter explains: the meaning of the learning analytics in Industry 4.0 and education and its affiliated areas; AI and its related areas with examples from state-of-the-art corporate applications and solutions based on AI technologies integrated into the learning analytics.
... Bakker, M.N. and Axmann,M., (2021) explained the meaning of learning analysis in industry 4.0 and education. The application of AI and related areas in education has been explored in this book chapter [20]. Caratozzolo,P and Alvarez-Delgado,A (2021) has given education 4.0 framework to enrich active learning using virtual and AI tools to enhance the quality of education sector. ...
... (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 August 2024 doi:10.20944/preprints202408.2039.v120 ...
Embracing Fourth Industrial Revolution(4IR) techniques is a need for a long-term sustainability and
to do automation in the processes of Industrial and education sector. Rapid advancement in the information
technology and other engineering technologies demand more proactive steps in the education institutes. In the
post pandemic world, there is an increasing demand of changes in teaching and learning methodologies with
the support of modern digital technologies. So, all of the teaching institutes need to have a significant step
change in the areas of learning and teaching. The impact of 4 IR is more on the education sectors compared to
previous generation’s industrial revolution. Keeping track of key technologies can be quite a challenge as they
keep evolving at a very fast pace. Fourth Industrial revolution is the current technological digital revolution in
which major focus is on cyber physical and biological systems such as Artificial Intelligence, Robotics, Internet
on Things, Virtual Reality etc., The educators and institutions should establish the pedagogies and teaching
with the aid of Industry 4.0 techniques to evolve education 4.0. So, it is very important to understand the
challenges and competencies required about the implementation of 4IR by the faculty members and students
who are currently studying in Higher Education Institutes (HEIs). This paper aim is to do a systematic review
about industry 4.0 techniques and its application in relate to the online learning and teaching, change in
pedagogies, enhancement of data management in HEIs, digital and virtual laboratories etc.
This chapter aims to present pertinent theoretical frameworks and the most recent results of empirical research in health. It is written for amateurs and experts who wish to understand the industry better. A meta-ethnographic synthesis of research on healthcare applications for Industry 5.0 forms the basis of the review's method. The results show that the link between Industry 5.0 and healthcare applications have a positive relationship. Big data may be used by Industry 5.0 to learn new things and produce symmetrical innovation. It also establishes a digital knowledge network that offers accurate medical data and vital patient records. It establishes a digital knowledge network that offers accurate medical data and vital patient records. This chapter contributes to a better understanding of the link between Industry 5.0 and healthcare applications.
This research provides a comprehensive, first-of-its-kind, in-depth, data-driven analysis of the discussions on "curriculum alignment" in the light of "learned skills" and "acquired skills", as illustrated by cross-disciplinary records in Scopus. It was undertaken from 2010 to 2021 on 10,214 data points obtained to fully grasp the issues, names and themes that have contributed to the field over the past decade, and it presents the case for the increased value and new application of bibliometric analyses. When faced with scholarly research not included in Scopus, on the one hand, and links between previously divided research groups, on the other, ensuring the compatibility of the various scientific information archives is essential. Only in this manner can the research artefacts be made evident, the concerns and problems that have been either overlooked or under-researched be identified, and practical debate between academia and policymakers be facilitated.
The paper looks back across dominant ways of delivering Higher Education until the present day and then looks forward. There is an approximate continuum described from Education 1.0 through 2.0 to 3.0. Education is mapped onto the emergence and development of the Web and the revolutions known as ‘Industrial’ over the last 250 years. Then some foresight is deployed with the particular lens of graduates’ employment prospects and contributions to the future – dubbed ‘Education 4.0’. The paper hopes to stimulate dialogue and promote preparedness and innovation for more changes arriving fast over Higher Education’s wider horizons.
Learner Management Systems (LMSs) are widely deployed across the industry as they provide a cost-saving approach that can support flexible learning opportunities. Despite their benefits, LMSs fail to cater for individual learning behavior and needs and support individualised prediction and progression. Learning Analytics (LAs) support these gaps by correlating existing learner data to provide meaningful predictive and prescriptive analysis. The industry and research community have already recognised the necessity of LAs to support modern learning needs. But a little effort has been directed towards the integration of LA into LMSs. This paper presents a novel automated Intelligence Learner Management System (iLMS) that integrates learner management and learning analytics into a single platform. The presented iLMS considers Machine Learning techniques to support learning analytics including descriptive, predictive and perspective analytics.
It is expected that the fourth industrial revolution will have a massive impact on individual career future experience by changing the basis of work, employment, and business in the upcoming future. Moreover, one-third of today's jobs will disappear by 2025 due to technology's intelligence development. Thus, students' employability skills are considered an effective tool to sustain employment at the future labour market. However, there are discrepancies in employability skills vision between higher education students' perspectives and those of employers. This study aims to recognize higher education students' perspectives about employability skills and to compare them with the labour market employability skills as requested by employers. A questionnaire was designed to test graduate students' perspectives on employability using the WEF 2020 employability skills. The results show that students are not fully aware of the fourth industrial revolution employability skills, thus, there is a gap between future labour market demand and students' perspectives about future employability skills. This study is testing employability in its two aspects. Firstly, the UAE graduate students' perspective of the employability skills as required by employers. Secondly, testing students' evaluation of the university educational programs' readiness in preparing them for the job market. JEL Classification: J62, J23
This review aims to provide a concise overview of four distinct research fields: Artificial Intelligence and EDucation (AIED), Computer-Supported Collaborative Learning (CSCL), Educational Data Mining (EDM), and Learning Analytics (LA). While all four fields are focused on understanding learning and teaching using technology, each field has a relatively unique or common perspective on which theoretical frameworks, methods, and ontologies might be appropriate. In this review we argue that researchers should be encouraged to cross the boundaries of their respective field and work together to address the complex challenges in education.
Analysis of learning behavior of MOOC enthusiasts has become a posed challenge in the Learning Analytics field, which is especially related to video lecture data, since most learners watch the same online lecture videos. It helps to conduct a comprehensive analysis of such behaviors and explore various learning patterns for learners and predict their performance by MOOC courses video. This paper exploits a temporal sequential classification problem by analyzing video clickstream data and predict learner performance, which is a vital decision-making problem, by addressing their issues and improving the educational process. This paper employs a deep neural network (LSTM) on a set of implicit features extracted from video clickstreams data to predict learners’ weekly performance and enable instructors to set measures for timely intervention. Results show that accuracy rate of the proposed model is 82%–93% throughout course weeks. The proposed LSTM model outperforms baseline ANNs, Super Vector Machine (SVM) and Logistic Regression by an accuracy of 93% in real used courses’ datasets.
The conventional education system lacks the focus to create employable graduates. The existing industries in the Information Technology sector widely recruit based on a very specific set of skills and academic performances. To create better career opportunities, the colleges and universities should ensure that the graduates are qualified to accomplish the basic skills required by any organizations. This demands systematic approaches to be adopted with an innovative teaching style during the academic curriculum-based training in engineering colleges. The international accreditation organization ABET is a worldwide recognized educational board that provides streamlined guidelines for competency skills and to deliver a quality education for students. In this research work, an extensive study has been performed on the needs of the industry and it has been compared to the quality of the course being offered to the students. The curriculum is designed based on the industry experts’ feedback. To achieve the Student Outcome Criteria as per ABET accreditation, the practices of structured approach is adopted along with the vision of lifelong learning in this research work. The teaching-learning process of first year under-graduate programming course and its evaluation techniques is considered in this research work. The competency skills like problem solving skills, critical thinking, and creative thinking are analysed using learning analytics strategies for first year Python programming course. The performances of the students are broadly categorized based on the metrics like logical, conceptual, analytical and conceptual thinking, while simultaneously focusing on their time management skills and commitment to learn. Artificial Neural Network (ANN), Naive Bayesian algorithm and logistic regression models are used to identify and measure the competency skills of the learners achieved in this course and validating these metrics with the student learning outcome. Implementing Artificial Intelligence concepts will provide results that can aid in creating the most suitable teaching-learning environment resulting in the best outcome for disruptive engineering education. Learning Analytics will provide an understanding and optimization of learning and its environments thereby ensuring sustainable development. This analysis presents an opportunity to identify the gap between the academic curriculum and the industry’s expectations in terms of competency skills to be acquired by the learners. Additionally, it helps to improve the teaching-learning process according to the dynamic changes from the industries and builds the foundation for students to be lifelong learners.
In recent years, the evolution of research in the domain of education focuses on learning analytics which provides vital insights on education outcome by collecting valuable data about learning person or/and institutions. The main objective of this article is to inspect the research literature disseminated on learning analytics predictive models in the last five years. This work discusses a detailed study on the learning analytics by categorizing based on prediction algorithms, data set used, and factors prioritized for prediction. This paper also explains the various applications, life cycle models of learning analytics and the vital challenges that could improve the overall performance of the learning analytics system. This work may provide comprehensive details on learner analytics and its possible line of future research for the researchers to propose novel models to append research value to the domain.
The use of digital media is increasingly being promoted in school teaching. Since this aspect changes the interaction between teachers and pupils, this research is concerned with the development of a prototype of a mobile application for Android and iOS, in which different learning applications for language acquisition are offered on the basis of learning analytical measurements provided by experts in the field. By logging and collecting interactions of the user, it is possible to create a variety of statistical evaluations and thus respond to the needs and weaknesses of students. For the evaluation of the application, a user experience test was carried out, whereby the child-friendly operation of the application was tested. Due to the very positive feedback, the design was found to be good and can therefore be further developed.
This study presents an advanced predictive learning analytics system, OU Analyse (OUA), and evidence from its evaluation with online teachers at a distance learning university. OUA is a predictive system that uses machine learning methods for the early identification of students at risk of not submitting (or failing) their next assignment. Teachers have access, via interactive dashboards, to weekly predictions of risk of failing for each of their students. In this study, we examined how the degree of OUA usage by 559 teachers, of which 189 were given access to OUA, related to student learning outcomes of more than 14 000 students in 15 undergraduate courses. Teachers who made “average” use of OUA, that is accessed OUA throughout the life cycle of a course presentation, and in particular between 10% and 40% of the weeks a course was running, and intervened with students flagged as at risk were found to benefit their students the most; after controlling for differences in academic performance, these students were found to have significantly better performance than their peers in the previous year's course presentation during which the same teachers made no use of predictive learning analytics. Predictive learning analytics is an innovative student's support approach in online pedagogy that, as shown in this study, can empower online teachers in effectively monitoring and intervening with their students, over and above other approaches, and result in improved learning outcomes.
Practitioner Notes
What is already known about this topic Pedagogical and personal support to students is a significant responsibility of online teachers.
Student's support is a challenging activity due to the lack of face‐to‐face interactions.
Predictive learning analytics (PLA) can identify students at risk of failing their studies.
What this paper adds One of the few large‐scale studies is available for examining the impact of analytics on student's performance.
Teachers' usage of PLA was significantly related to better learning outcomes.
Online teachers had students with better learning outcomes when accessing PLA data rather than when they had no access.
Implications for practice and/or policy PLA can empower online teachers and complement the teaching practice.
PLA can help in the identification and proactive intervention of students at risk of failing their studies.
Actions should be taken to motivate and engage online teachers with PLA.
Purpose
The purpose of this paper is, first, to examine student perspectives of their university experience in terms of the soft employability skills they develop; second, how prepared those students feel for the future employment market and finally investigate whether there are differences in perceptions between Chinese and Malaysian students given their different educational experience.
Design/methodology/approach
In this study, 361 predominantly Chinese undergraduate students at two universities, one in China and the other in Malaysia completed the 15-item Goldsmiths soft skills inventory using an online survey.
Findings
The results, analysed using factor analysis and confirmatory factor analysis, indicated that the university curriculum develops student soft skills, particularly in the Malaysian university and supports the relationship between soft skill and student preparedness for employment. The results also indicate that compared with the respondents from the Chinese university, the Malaysian university respondents were more likely to be positive to statements concerning their respective university’s ability to develop their soft skills.
Research limitations/implications
Such findings have implications for education providers and business in that it is important for universities to embed soft skills into the curriculum in order to develop graduate work readiness.
Originality/value
What this research contributes is not only consolidation of existing research in the contemporary context of a disruptive jobs market, it takes research forward through analysing student perceptions from two universities, one in Malaysia and the other in China, of the skills they develop at university and the importance of soft skills to them and their perceptions of future employment and employability. Such research will provide insight, in particular, into the role of education providers, the phenomena of underemployment among graduates in China, and be of practical significance to employers and their perception that graduates lack the necessary soft skills for the workplace (Anonymous, 2017a; Stapleton, 2017; British Council, 2015; Chan, 2015).
This article is focused on the importance and influence of Industry
4.0 and consequently the Internet-connected technologies for the creation of value added
for organizations and society. The contribution of the article is mainly conceptual.
With the development of the Internet, the Internet of things that is central to the new
industrial revolution has led to ?Industry 4.0.? The aim of this article is to
synthesize the known theory and practices of Industry 4.0, and to investigate the
changes that will result from Industry 4.0 and with the development of the Internet of
things.
The analysis of data collected from the interaction of users with educational and information technology has attracted much attention as a promising approach for advancing our understanding of the learning process. This promise motivated the emergence of the new research field, learning analytics, and its closely related discipline, educational data mining. This paper first introduces the field of learning analytics and outlines the lessons learned from well-known case studies in the research literature. The paper then identifies the critical topics that require immediate research attention for learning analytics to make a sustainable impact on the research and practice of learning and teaching. The paper concludes by discussing a growing set of issues that if unaddressed, could impede the future maturation of the field. The paper stresses that learning analytics are about learning. As such, the computational aspects of learning analytics must be well integrated within the existing educational research.
p class="0abstract"> Common factors, which are related to study success include students’ sociodemographic factors, cognitive capacity, or prior academic performance, and individual attributes as well as course related factors such as active learning and attention or environmental factors related to supportive academic and social embeddedness. In addition, there are various stages of a learner’s learning journey from the beginning when commencing learning until its completion, as well as different indicators or variables that can be examined to gauge or predict how successfully that journey can or will be at different points during that journey, or how successful learners may complete the study and thereby acquiring the intended learning outcomes. The aim of this research is to gain a deeper understanding of not only if learning analytics can support study success, but which aspects of a learner’s learning journey can benefit from the utilisation of learning analytics. We, therefore, examined different learning analytics indicators to show which aspect of the learning journey they were successfully supporting. Key indicators may include GPA, learning history, and clickstream data. Depending on the type of higher education institution, and the mode of education (face-to-face and/or distance), the chosen indicators may be different due to them having different importance in predicting the learning outcomes and study success. </p
The Fourth Industrial Revolution is changing everything - from the way we relate to each other, to the work we do, the way our economies work, and what it means to be human. We cannot let the brave new world that technology is currently creating simply emerge. All of us need to help shape the future we want to live in. But what do we need to know and do to achieve this?
In Shaping the Fourth Industrial Revolution, Klaus Schwab and Nicholas Davis explore how people from all backgrounds and sectors can influence the way that technology transforms our world. Drawing on contributions by more than 200 of the world's leading technology, economic and sociological experts to present a practical guide for citizens, business leaders, social influencers and policy-makers this book outlines the most important dynamics of the technology revolution, highlights important stakeholders that are often overlooked in our discussion of the latest scientific breakthroughs, and explores 12 different technology areas central to the future of humanity.
Emerging technologies are not predetermined forces out of our control, nor are they simple tools with known impacts and consequences. The exciting capabilities provided by artificial intelligence, distributed ledger systems and cryptocurrencies, advanced materials and biotechnologies are already transforming society. The actions we take today - and those we don't - will quickly become embedded in ever-more powerful technologies that surround us and will, very soon, become an integral part of us.
By connecting the dots across a range of often-misunderstood technologies, and by exploring the practical steps that individuals, businesses and governments can take, Shaping the Fourth Industrial Revolution helps equip readers to shape a truly desirable future at a time of great uncertainty and change.
This chapter provides an overview of the descriptive, predictive, and prescriptive analytics landscape. Data mining is first introduced, followed by coverage of the role of machine learning and artificial intelligence in analytics. Supervised and unsupervised learning are compared, along with the different applications that fall under each. The characteristics and role of reports in descriptive analytics are described, along with the extraction of data in a multidimensional environment. Key algorithms, covering different predictive analytics applications, are described in some detail.
As higher education increasingly moves to online and digital learning spaces, we have access not only to greater volumes of student data, but also to increasingly fine-grained and nuanced data. A significant body of research and existing practice are used to convince key stakeholders within higher education of the potential of the collection, analysis and use of student data to positively impact on student experiences in these environments. Much of the recent focus in learning analytics is around predictive modeling and uses of artificial intelligence to both identify learners at risk, and to personalize interventions to increase the chance of success.
In this paper we explore the moral and legal basis for the obligation to act on our analyses of student data. The obligation to act entails not only the protection of student privacy and the ethical collection, analysis and use of student data, but also, the effective allocation of resources to ensure appropriate and effective interventions to increase effective teaching and learning.
The obligation to act is, however tempered by a number of factors, including inter and intra-departmental operational fragmentation and the constraints imposed by changing funding regimes. Increasingly higher education institutions allocate resources in areas that promise the greatest return. Choosing (not) to respond to the needs of specific student populations then raises questions regarding the scope and nature of the moral and legal obligation to act. There is also evidence that students who are at risk of failing often do not respond to institutional interventions to assist them.
In this paper we build and expand on recent research by, for example, the LACE and EP4LA workshops to conceptually map the obligation to act which flows from both higher education’s mandate to ensure effective and appropriate teaching and learning and its fiduciary duty to provide an ethical and enabling environment for students to achieve success. We examine how the collection and analysis of student data links to both the availability of resources and the will to act and also to the obligation to act. Further, we examine how that obligation unfolds in two open distance education providers from the perspective of a key set of stakeholders – those in immediate contact with students and their learning journeys – the tutors or adjunct faculty.
Futurists predict that a third of jobs that exist today could be taken by Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA) by 2025. However, very little is known about how employees perceive these technological advancements in regards to their own jobs and careers, and how they are preparing for these potential changes. A new measure (STARA awareness) was created for this study that captures the extent to which employees feel their job could be replaced by these types of technology. Due to career progression and technology knowledge associated with age, we also tested age as a moderator of STARA. Using a mixed-methods approach on 120 employees, we tested STARA awareness on a range of job and well-being outcomes. Greater STARA awareness was negatively related to organisational commitment and career satisfaction, and positively related to turnover intentions, cynicism, and depression.
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