An adaptive testing system for programming proficiency using Item Response Theory.
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| Titel: | An adaptive testing system for programming proficiency using Item Response Theory. |
|---|---|
| Autoren: | Apró, Anikó, Tajti, Tibor |
| Quelle: | Annales Mathematicae et Informaticae; 2025, Vol. 61, p31-42, 12p |
| Schlagwörter: | ADAPTIVE testing, ITEM response theory, PYTHON programming language, PROGRAMMING languages, JAVASCRIPT programming language, JAVA programming language, SQL, COMPUTER programming education |
| Abstract: | This paper presents the design and implementation of an adaptive testing system for assessing university students' programming skills in Python, C#, Java, JavaScript, and SQL. Adaptive testing dynamically adjusts question difficulty based on individual performance, enabling more precise and efficient assessment compared to traditional fixed-form tests. We provide an overview of adaptive testing principles and the Item Response Theory (IRT) models (1PL-3PL) that underpin the system. Our approach integrates continuous, categorical, and accelerated adaptive methodologies to optimize both accuracy and test length. The system is implemented as a Flask-based web application that selects questions from a customizable bank, adapting to the learner's estimated knowledge level in real time. Key features include topic-based item selection, immediate scoring, detailed post-test analytics, and end-of-test formative recommendations (tailored by language/level with estimated study time). The system demonstrates how IRT-based adaptive programming assessment supports personalized, data-driven evaluation in higher education and hiring. [ABSTRACT FROM AUTHOR] |
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| Datenbank: | Complementary Index |
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