Automated student knowledge testing and control system ZELIS
Purpose. To substantiate the methodology and develop the software of the automated system of testing and monitoring the knowledge of ZELIS students. Methodology. Examination papers contain text, tables, pictures, formulas, etc., that is, all the possibilities of the MS WORD editor. Questions and answer options are generated randomly. As the operation showed, the most effective number is 30 questions and 5 answers to them. When working with forms, a large number of people are tested. Each form is scanned to receive a raster file in *.jpg format. To recognize these files, a mathematical apparatus has been developed that accurately determines the number of the correct answer based on the form. In online mode, students receive input data through the Internet, which is relevant in the conditions of martial law, as well as when testing students’ knowledge during the educational process without additional use of laboratory time. Findings. The ZELIS software is updated in the Visual Studio 2019 environment in the C# programming language using the MS ACCESS or SQL SERVER DBMS, as well as the FireBase RealTime DataBase cloud DBMS. Originality. Input information for creating tests comes in a single RTF format that allows you to use tables, figures, formulas, etc. The system also provides the possibility of simultaneous testing of a large number of persons. This process uses proprietary recognition algorithms and takes only a few minutes to process a large number of forms. Practical value. The article describes the automated system of testing and monitoring the knowledge of ZELIS students, which was developed by the authors. The system was developed at the State University of Economics and Technology and has been operating since 2015. Approximately 250 exams are conducted in one online paper-based testing session, with approximately 1,200 students participating.
Authors
- Oleksandr Zelensky (ORCID: https://orcid.org/0000-0001-8780-587X)
- V. P. Lysenko (ORCID: https://orcid.org/0000-0002-5200-1211)
Publication Details
- Journal
- Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu
- Published
- 2024-12-30
- DOI
- https://doi.org/10.33271/nvngu/2024-6/193
- Primary Topic
- Advanced Data Processing Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00