Kun, László Ákos, Berezvai, Szabolcs
ORCID: https://orcid.org/0000-0002-6399-583X, Lógó, Emma and Szilágyi, Brigitta
ORCID: https://orcid.org/0000-0002-2566-0465
(2025)
Novel Adaptive Entry-Level Maths Assessment Test for Sorting Students to Different Level Courses.
In:
Proceedings of the 53rd Annual Conference of the European Society for Engineering Education (SEFI 2025).
European Society for Engineering Education (SEFI), Bruxelles, pp. 588-596.
. ISBN 978 287 3520 29 8
DOI 10.5281/zenodo.17631398
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Official URL: https://doi.org/10.5281/zenodo.17631398
Abstract
In engineering programs significant differences can be observed in the mathematical proficiency of students at the beginning of their university studies due to the considerable disparity between the content of the intermediate and advanced level math classes in the Hungarian high school system. To place incoming students in courses that match their knowledge in large-scale courses where the number of students exceeds 100, an effective assessment test is needed that not only evaluates the procedural knowledge but also the competences in mathematics. In the fall of 2024, a comprehensive entry-level assessment was conducted among incoming freshmen, involving a total of 2,196 students attending to six different faculties. This contribution aims to present a novel adaptive entry-level math assessment test for freshmen, which was performed before the start of the first-year Calculus courses. The test consisted of three parts, where the results of each part were used to select the set of questions to be asked in the next section. By analyzing the results of the three-part adaptive test, we aim to explore how incoming students can be grouped based on their knowledge levels. This categorization enables us to recommend either the A or B level mathematics courses, best suited to their proficiency. © SEFI 2025.All rights reserved.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | Mathematics in engineering, assessment tests, competence in mathematics, adaptive testing, dropout |
| Divisions: | Institute of Data Analytics and Information Systems |
| Subjects: | Mathematics, Econometrics Education |
| Funders: | MTA-ELTE Theory of Learning Mathematics Research Group |
| DOI: | 10.5281/zenodo.17631398 |
| ID Code: | 12707 |
| Deposited By: | MTMT SWORD |
| Deposited On: | 09 Apr 2026 13:09 |
| Last Modified: | 09 Apr 2026 13:09 |
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