This article focuses on proposed semantically enhanced model of decision support system for learning management system (LMS). The model is based on a survey of LMSs and various plugins used in these to improve educational process. Systems based on semantic technologies are capable of integrating heterogeneous data, flexibly changing data schemas, semantic search (using ontologies), and joint knowledge development. The knowledge base that was developed for the proposed system model is presented in an ontological form. Ontology-based applications limit the "fragility" of the software and increase the likelihood of its reuse. In addition, they profitably redirect the efforts previously focused on software development and maintenance of creation and modification of knowledge structures. In the proposed knowledge base, we developed the necessary rules for further recommendations of specialization and courses for users. These recommendations are based on users' data extracted from profiles and user preferences.
Abstract: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 possibility to determine the study profiles is proposed.
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 learning progress and based on that a model of smart educational system which use knowledge engineering and Watson technology is proposed. This system is relevant both for basic education and for adult education.
Industry 4.0 and highly automated critical infrastructure can be seen as cyber-physicalsocial 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 unknown worst-case scenarios during training. In this paper, we provide an adversarial training framework for the collective intelligence. We show how cognitive capabilities can be copied ("cloned") from humans and trained as a (responsible) collective intelligence. We made some modifications to the Generative Adversarial Networks architectures and adapted them for the cloning and training tasks. We modified the Discriminator component to a so-called "Turing Discriminator", which includes one or several human and artificial discriminators working together. We also discussed the concept of cellular intelligence, where a person can act and collaborate in a group together with their own cognitive clones. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Abstract: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 of semantic, visual and auditory similarities. Each similarity factor can be measured with different methods. According to the user's feedback, the weights of the factors and similarity distance are adjusted to modify the level of difficulty in further iterations. The system is designed to attempt to retrieve knowledge about the learners by recognition of aspects that are difficult for them. Proposed solution could be considered as a self-adaptive system, which is trying to recognize individual model of the learner and apply it for further facilitation of his/her learning process. The use of the system will be demonstrated in future work.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.