JITA

JITA Journal of Information Technology and Applications

Vol. 2 No. 1 (2012): JITA - APEIRON

Sanja Maravić-Čisar, Robert Pinter, Dragica Radosav, Petar Čisar

Comparison of Examination Methods Based on Multiple-choice Questions Using Personal Computers and Paper-based Testing

Original scientific paper

DOI: https://doi.org/10.7251/JIT1201022C

Abstract

Computer-based testing, by facilitating the interaction between teaching and learning, can improve the quality of learning through improved formative feedback which is a key aspect of formative assessment. This study makes a contribution to the research on computer-based testing by examining the mode differences between the paper-and-pencil test and computer-based test. The previously conducted researches in this area dealt with the students of primary and secondary schools. In those researches the points of observation were the students’ successes in mathematics, English and social sciences; no research was done in field of programming languages such as C++ with post-secondary students.
The main aim of this study was to find out whether there are differences in the achieved results in two ways of testing: computer-based testing and paper-and-pencil test. Also, the intention was to detect those characteristics of computer based test, which may have a negative effect on students’ achievements. The participants were a representative sample of the population of all engineering students studying computer science at Subotica Tech. The findings of this study led the authors to reach the conclusion that there are no significant differences in scored results for the paper-and-pencil testing and the computer-based testing.

Keywords: computer-based test; paper-and-pencil test; assessment; testing; post-secondary education

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.