0000000000892497
AUTHOR
Mariia Gavriushenko
Bridging human and machine learning for the needs of collective intelligence development
There are no doubts that artificial and human intelligence enhance and complement each other. They are stronger together as a team of Collective (Collaborative) Intelligence. Both require training for personal development and high performance. However, the approaches to training (human vs. machine learning) are traditionally very different. If one needs efficient hybrid collective intelligence team, e.g. for managing processes within the Industry 4.0, then all the team members have to learn together. In this paper we point out the need for bridging the gap between the human and machine learning, so that some approaches used in machine learning will be useful for humans and vice-versa, some …
Adaptive Vocabulary Learning Environment for Late Talkers
The main aim of this research is to provide children who have an early language delay with an adaptive way to train their vocabulary taking into account individuality of the learner. The suggested system is a mobile game-based learning environment which provides simple tasks where the learner chooses a picture that corresponds to a played back sound from multiple pictures presented on the screen. Our basic assumption is that the more similar the concepts (in our case, words) are, the harder the recognition task is. The system chooses the pictures to be presented on the screen by calculating the distances between the concepts in different dimensions. The distances are considered to consist o…
Towards Evidence-Based Academic Advising Using Learning Analytics
Academic advising is a process between the advisee, adviser and the academic institution which provides the degree requirements and courses contained in it. Content-wise planning and management of the student’ study path, guidance on studies and academic career support is the main joint activity of advising. The purpose of this article is to propose the use of learning analytics methods, more precisely robust clustering, for creation of groups of actual study profiles of students. This allows academic advisers to provide evidence-based information on the study paths that have actually happened similarly to individual students. Moreover, academic institutions can focus on management and upda…
Smart Educational Process Based on Personal Learning Capabilities
Personalized learning is increasingly gaining popularity, especially with the development of information technology and modern educational resources for learning. Each person is individual and has different knowledge background, different kind of memory, different learning speed. Teacher can adapt learning course, learning instructions or learning material according to the majority of learners in class, but that means that learning process is not adapted to the personality of each individual learner. That is why it is important to have smart educational process based on personal learning capabilities. This paper presents a literature survey on different learning systems which detects learni…
Anonymization as homeomorphic data space transformation for privacy-preserving deep learning
Industry 4.0 is largely data-driven nowadays. Owners of the data, on the one hand, want to get added value from the data by using remote artificial intelligence tools as services, on the other hand, they concern on privacy of their data within external premises. Ideal solution for this challenge would be such anonymization of the data, which makes the data safe in remote servers and, at the same time, leaves the opportunity for the machine learning algorithms to capture useful patterns from the data. In this paper, we take the problem of supervised machine learning with deep feedforward neural nets and provide an anonymization algorithm (based on the homeomorphic data space transformation),…
On personalized adaptation of learning environments
This work is devoted to the development of personalized training systems. A major problem in learning environments is applying the same approach to all students: i.e., teaching materials, time for their mastering, and a training program that is designed in the same way for everyone. Although, each student is individual, has his own skills, ability to assimilate the material, his preferences and other. Recently, recommendation systems, of which the system of personalized learning is a part, have become widespread in the learning environments. On the one hand, this shift is due to mathematical approaches, such as machine learning and data mining, that are used in such systems while, on the ot…
On developing adaptive vocabulary learning game for children with an early language delay
Vocabulary replenishment is an ordinary child development process. Deviations in this process can significantly affect the further progress and perception of the educational material in the school. A hypothesis was proposed that the similarity of words on various factors can influence the child's understanding. To test this hypothesis in this work, we propose the development of AdapTalk game for children. This game is concentrated on learning words, in the context of animals. The game will create the basis for further testing the influence of the semantic similarity of words for the child. This work describes the development background, the basic principles of calculating the semantic simil…
Supporting Institutional Awareness and Academic Advising using Clustered Study Profiles
The purpose of academic advising is to help students with developing educational plans that support their academic career and personal goals, and to provide information and guidance on studies. Planning and management of the students’ study path is the main joint activity in advising. Based on a study log of passed courses, we propose to use robust, prototype-based clustering to identify a set of actual study path profiles. Such profiles identify groups of students with similar progress of studies, whose analysis and interpretation can be used for better institutional awareness and to support evidence-based academic advising. A model of automated academic advising system utilizing the possi…
Cloning and training collective intelligence with generative adversarial networks
Industry 4.0 and highly automated critical infrastructure can be seen as cyber‐physical‐social systems controlled by the Collective Intelligence. Such systems are essential for the functioning of the society and economy. On one hand, they have flexible infrastructure of heterogeneous systems and assets. On the other hand, they are social systems, which include collaborating humans and artificial decision makers. Such (human plus machine) resources must be pre‐trained to perform their mission with high efficiency. Both human and machine learning approaches must be bridged to enable such training. The importance of these systems requires the anticipation of the potential and previously unknow…
An intelligent learning support system
Fast-growing technologies are shaping many aspects of societies. Educational systems, in general, are still rather traditional: learner applies for school or university, chooses the subject, takes the courses, and finally graduates. The problem is that labor markets are constantly changing and the needed professional skills might not match with the curriculum of the educational program. It might be that it is not even possible to learn a combination of desired skills within one educational organization. For example, there are only a few universities that can provide high-quality teaching in several different areas. Therefore, learners may have to study specific modules and units somewhere e…