Quality of Student Support at IT Educational Programmes: Case of Lviv Polytechnic National University

Oleksiv, Ihor; Kharchuk, Viktoriya; Shulyar, R.V.; Dluhopolskyi, Oleksandr

doi:10.1109/acit52158.2021.9548648

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Comparisons with state-ofthe-art methods demonstrate the highest accuracy achieved by the proposed approach (7% according to RMSE in comparison with existing method). Further research and experimentation can be conducted to explore the applicability of this approach in other domains [31][32][33] as well as a development of new method for independently obtaining global and local components using two nonlinear SGTM neural-like structure.…”

Section: Discussionmentioning

confidence: 99%

An improved ANN-based global-local approximation for small medical data analysis

Izonin,

Tkachenko,

Bliakhar

et al. 2023

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: The task of approximation of complex nonlinear dependencies, especially in the case of short datasets, is important in various applied fields of medicine. Global approximation methods describe the generalized behavior of the model, while local methods explain the behavior of the model at specific data points. Global-local approximation combines both approaches, which makes such methods a powerful tool for processing short sets of medical data that can have both broad trends and local variations.OBJECTIVES: This paper aims to improve the method of sequential obtaining global and local components of the response surface to increase the accuracy of prediction in the case of short sets of medical data.METHODS: In this paper, the authors developed a new method that combined two ANNs: a non-iterative SGTM neural-like structure for obtaining the global component and GRNN as a powerful tool of local approximation in the case of short datasets.RESULTS: The authors have improved the method of global-local approximation due to the use of a General Regression Neural Network instead of RBF ANN for obtaining the local component, which ensured an increase in the accuracy of the body fat prediction task. The authors optimized the operation of the method and investigated the efficiency of the sequential obtaining global and local components of the response surface in comparison with the efficiency using a number of existing methods.CONCLUSION: The conducted experimental studies for solving the body fat prediction task showed the high efficiency of using the improved method in comparison with a number of existing methods, including ensemble methods.

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Section: Discussionmentioning

confidence: 99%

An improved ANN-based global-local approximation for small medical data analysis

Izonin,

Tkachenko,

Bliakhar

et al. 2023

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

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“…Thus, given that the vast majority of studies prove the positive or neutral impact of ICT and e-commerce on the employment rate in different countries, there is a need for a detailed analysis of the probabilities of staff leakage, which is directly dependent on the nature of the development of ICT in European Union (EU) enterprises (Dluhopolskyi et al 2019;Koziuk et al 2020;Dluhopolskyi et al 2021;Oleksiv et al 2021;Panchenko et al 2021). The main aim of the study is to identify the nature of the flow of ICT employees between high and low education attainment level groups and also to forecast employment rates in the ICT field for future periods.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting of the Employment Rate in the EU ICT Field

Zatonatska

Klapkiv

Dluhopolskyi

et al. 2022

CER

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The ingrained tendency to implement information and communication technologies (ICT) in EU enterprises over the last decade has caused dramatic changes in the labor market. Since the demand for ICT personnel is growing, there is still a need to create a comprehensive strategy to effectively manage ICT specialists when restructuring enterprises. The aim of the research is to identify transferring processes between low‑ and high‑skilled ICT personnel and predict the employment rate in the ICT field until 2025. A Markov chain was used as the method of analysis. Using statistical data about the employment rate of ICT personnel by education attainment level, we have built a Markov chain model that describes the processes of ICT personnel with different levels of education. Data from 2005 to 2019 was used to build forecasting because of the absence of the latest information. We demonstrate that with the help of digitalization, the employment rate of ICT staff in 2025 will increase by 64% compared to 2018. The research verifies that ICT personnel will be in great demand until 2023 and, importantly, low‑ and middle‑skilled personnel will be in demand, as well as high‑skilled personnel. The employment rate in the ICT field will be at its highest level in 2022 as the favorable economic conditions for ICT adoption will help it. The growing demand for low‑ and medium‑skilled ICT staff are met both by staff relocation processes and by the increasing digitalization of business units and public sector institutions. The added value of the analysis is the prediction that the largest growth in ICT personnel employment will occur by 2023, but employment growth will slow down after that. The main obstacle to employment growth through digitalization is the global economic crisis because of different reasons.

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“…These digital tools include digital learning management systems (LMS) (Canvas, Moodle, Google Classroom), virtual classroom tools (Zoom, Hangouts Meets, DingTalk), student information systems (PowerSchool SIS, Gradelink), self-directed learning content (Khan Academy, Quizlet, British Council), Massive Open Online Course Platforms (Canvas Network, Coursera, EdX, Udemy, Prometheus), etc. (Polianovskyi et al, 2019;Oleksiv et al, 2021;Liuta et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Digital and Technological Support of Distance Learning at Universities under COVID-19 (Case of Ukraine)

Polianovskyi

Zatonatska

Dluhopolskyi

et al. 2021

RREM

Self Cite

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The crisis of COVID-19 and the lockdowns announced in all countries of the world in early 2020 affected all people and areas of the economy, including education. In this regard, universities around the world were quickly forced to transform the educational process into a distance format. Thus, the article analyses the advantages and disadvantages of distance learning and teaching for students, as well as the peculiarities of the response of universities to actual challenge. The article presents the results of scientific-theoretical and applied research on the problem of providing distance learning at a high-quality level. The authors used the case of Taras Shevchenko National University of Kyiv, which was one of the first in Ukraine in 2020 to introduce a powerful distance educational platform “KNU Online”. The scientific novelty of the study is that the authors analysed the current state of higher education in a pandemic and made a forecast of possible changes in the market of educational services using ICT. In addition, in the period of turbulence, the processes are analysed, when free economic zones are actively inclined to find innovative ways to increase their own level of competition and provide educational services on quality and continuous basis. It is proposed to use the case “KNU Online” as a good practice to obtain all the necessary range of educational services in the most accessible and convenient way.

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Quality of Student Support at IT Educational Programmes: Case of Lviv Polytechnic National University

Cited by 4 publications

References 6 publications

An improved ANN-based global-local approximation for small medical data analysis

An improved ANN-based global-local approximation for small medical data analysis

Forecasting of the Employment Rate in the EU ICT Field

Digital and Technological Support of Distance Learning at Universities under COVID-19 (Case of Ukraine)

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