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This paper describes the teaching model for Education 4.0, that uses elements from different disciplines such as electronic technology, Artificial Intelligence (AI), health, multimodal learning analytics, education, etc. Smart blended learning process for Education 4.0, implements in each phase AI methods such as user modeling, adaptivity, machine learning, chatbots, and semantic text recognition tools to increase learning success and to reduce the dropout rate in examinations. On the one hand, multimodal data, from online course and wearables, enables further statements about the concrete learning situation, and on the other hand, it increases the students' subjective experience of emotional states, their stress perception, well-being or energy level. This paper reports on experiments where the combination of AI, Internet of Things (IoT) and Biofeedback in a smart blended learning process attempt to consider the characteristics of learners better.

The paper presents smart blended learning for fostering students’ performance. It uses models of student activity and detects causes of a possible drop out, using learning analytics and real-time data. Taking advantage of embedded sensors (non-invasive, low-cost, flexible and distraction free) built into wearable devices real-time data can be used to support students’ success, well-being and health. In our contribution to the research topic we evaluate the students' experience with smart blended learning using biofeedback methods, sensor data extracted from wearables for five specific lectures, during study periods and in examinations. The preliminary results indicate a correlation between the physiological signals and the grades in examination.

A major challenge for Education 4.0 is to make use of wearable devices for helping students in monitoring their learning behavior and their activities (steps, heart rate variability, and heart rate) in real-time. The first aim of this paper is to present our implementation of adaptivity and Artificial Intelligence (AI) methods within the Education 4.0 process. In this work, we investigate embedded biosensors (noninvasive, low-cost, and distraction-free) used in smartphones and smartwatches. The next objective is to enable IoT for Higher Education, i.e. a novel system assisted by AI that takes embedded biosensor data and environmental data into account in order to estimate students’ wellbeing and health. In this regard, we propose a framework that uses wearable devices to collect data with biofeedback methods to support students’ academic success.

Artificial Intelligence (AI) will play a key role in Higher Education. Our contribution leads the road to innovation within IoT for Education 4.0, using a system comprising smartwatch data, health data, learning analytics and Artificial Intelligence. By using embedded sensors from wearable devices, we can add value to Education 4.0. Smartwatches are IoT devices, equipped with a multitude of embedded sensors collecting distraction-free huge amounts of real-time data during students’ learning. In our paper we highlight advantages of wrist-based wearables like smartwatches for Education 4.0. We develop a User Experience Questionnaire for the measurement of acceptance of advanced electronic technology in Higher Education. We identify as experiment’s results the most important sensors and protocols for Education 4.0.

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.

The rise of digital technology creates disruptive innovation, new opportunities in communication, it transforms industrial production and it changes the way we work, learn and live. That leads to the fourth Industrial Revolution, which changes the working world enormously. New skills, future-ready curricula, new learning and training concepts as well as the flexibility in education are necessary. To deliver that, it needs a fourth revolution in the educational system with a strong active presence and most important Artificial Intelligence (AI) driven features for better performance. Education 4.0 needs also a strong partnership between industry and academicals environment in the formation of human resources for Industry 4.0. We present in this paper our answers of education regarding the challenges of fourth revolution in Industry and the seven AI driven didactic methods identified for the University of Future.

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.

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