Abstract
In this paper, a novel approach Immune Algorithm (IA) is applied to improve the efficiency of composing near optimal test sheet from item banks to meet multiple assessment criteria. We compare the results of immune and Genetic Algorithm (GA) to compose test-sheets for multiple assessment criteria. From the experimental results, the IA approach is desirable in composing near optimal test-sheet from large item banks. And objective conceptual vector (OCV) and objective test-sheet test item numbers (M) can be effectually achieved. Hence it can support the needs of precisely evaluating student’s learning status. We successfully extend the applications of artificial intelligent - Immune to the educational measurement.
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Lee, CL., Huang, CH., Lin, CJ. (2007). Test-Sheet Composition Using Immune Algorithm for E-Learning Application. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_82
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DOI: https://doi.org/10.1007/978-3-540-73325-6_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73322-5
Online ISBN: 978-3-540-73325-6
eBook Packages: Computer ScienceComputer Science (R0)