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Submitted
Aug 26, 2019
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Nov 11, 2019
Published
May 13, 2020
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Abstract

The major challenge in the digital era is the management of big data. A substantial share of digitization is taken from the e-learning. In this respect, the current article deals with generation of questions for testing the acquired levels of students’ knowledge. For this purpose, an algorithm for generation of questions for tests with different level of complexity is proposed. The main stage of this algorithm is using of mathematical combinatorial optimization model. Using this model makes it possible to formulate different tasks, whose solutions determine a subset of questions that correspond to different degree of test complexity. Essential part of this model is the use of binary integer variables to determine whether a question will be a part of the test or not. The advantage of the proposed approach is the flexibility to decrease or increase the number of questions used to compose the test preserving the required score in accordance to the particular level of test complexity. The conducted investigations over a year show, that the effect of testing can improve retention of knowledge and lead to improved end results. The applicability of the proposed algorithm along with the formulated mathematical model is demonstrated in a case study on the excerpt of questions from the web programming course. The proposed algorithm could be used for generation of tests with different degree of complexity for other learning contents.

Keywords

e-Learning Test Generation Combinatorial Optimization Mathematical Model Questions Difficulties

Article Details

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Author Biography

Daniela Ivanova Borissova, Institute of Information and Communication Technologies at the Bulgarian Academy of Sciences

Daniela BORISSOVA is associated professor on “Application of the principles and methods of the cybernetics in different scientific areas (engineering)” in the Institute of Information and Communication Technologies at Bulgarian Academy of Sciences, department of Information Processes and Decision Support Systems. She holds academic research degree doctor of science on “Informatics”. She is also a part-time professor at University of Library Studies and Information Technologies. She major research interests are in the e-learning systems and tools, distance education software, computer science, algorithm design, engineering systems modeling and design.

How to Cite
Borissova, D. I., & Keremedchiev, D. (2020). Generation of e-Learning tests with different degree of complexity by combinatorial optimization. Journal of E-Learning and Knowledge Society, 16(2), 17-24. https://doi.org/10.20368/1971-8829/1135016

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