The usefulness of X-Ray Tutor (XRT) as an individually adaptive computer-based training system for aviation security screening officers to increase X-ray image interpretation competency has been proven in several studies. Many airports require their screening officers to conduct weekly recurrent training in order to enhance their capabilities and competencies. During training, X-ray images of passenger bags similar to how they appear at the security checkpoint have to be judged regarding the dangerousness of their content. Screening officers have to detect threat objects within the bags and discriminate harmless objects from threat objects. The advantage of XRT is its level-based construction. The training begins at the lowest difficulty level and increases in difficulty level with achieved performance for each screening officer individually. The aim of this article is to create a guideline and norm regarding training requirements in order to ensure that screening officers maximize their training benefit. Based on the X-Ray Tutor level algorithm we can express minimum requirements for screening officers in terms of image difficulty levels. For example, the minimum level to be achieved in X-Ray Tutor after one year of recurrent training was determined based on normative data assuming that screeners take about 1-2 training sessions of 20 minutes per week. Alternatively, it can also be expressed as requirement to reliably detect threat items depicted in difficult views with high superposition and medium bag complexity. Recommendations for standard reference levels and detailed background information on the X-Ray Tutor level are given.


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    Title :

    Relationship between level of detection performance and amount of recurrent computer-based training


    Contributors:
    Michel, S. (author) / Koller, S.M. (author) / Schwaninger, A. (author)


    Publication date :

    2008


    Size :

    6 Seiten, 10 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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