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Assess the Assessment: An Automated Analysis of Multiple Choice Exams and Test Items.

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Title: Assess the Assessment: An Automated Analysis of Multiple Choice Exams and Test Items.
Authors: Nettekoven, Michaela1 michaela.nettekoven@wu.ac.at, Ledermüller, Karl1 karl.ledermueller@wu.ac.at
Source: Proceedings of the European Conference on e-Learning (ECEL). 2012, p397-405. 9p.
Subject Terms: *Multiple choice examinations, *Mobile learning, *Item response theory, Scale items, Rasch models
Abstract: Multiple choice exams are widely used in educational institutions, and normally strongly linked with an e-learning preparation phase and sometimes more recently embedded into an e-assessment environment. Besides many advantages (and some disadvantages), MC exams offer the possibility to be easily analyzed by various statistical methods. The Item Response Theory provides several methods and models for this purpose. The most famous is the Rasch Model and its various extensions. Unfortunately, few lecturers (outside the field of mathematics, statistics or psychology) are familiar with these models and/or have the statistical knowledge to apply them. We developed a tool to automatically analyze multiple choice exams and their embedded items, using several statistical methods like the mentioned Rasch Model and its extensions for polytomous item response categories as well as more commonly known methods like hierarchical clustering, multidimensional scaling, factor analysis or analysis of variance. The automatically generated report lists the respective key figures of the various analyses, together with detailed explanations to help non-statisticians to easily interpret the results, thus enabling them to assess and improve the quality of their multiple choice exams and test items. [ABSTRACT FROM AUTHOR]
Copyright of Proceedings of the European Conference on e-Learning (ECEL) is the property of Academic Conferences & Publishing International Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Multiple choice exams are widely used in educational institutions, and normally strongly linked with an e-learning preparation phase and sometimes more recently embedded into an e-assessment environment. Besides many advantages (and some disadvantages), MC exams offer the possibility to be easily analyzed by various statistical methods. The Item Response Theory provides several methods and models for this purpose. The most famous is the Rasch Model and its various extensions. Unfortunately, few lecturers (outside the field of mathematics, statistics or psychology) are familiar with these models and/or have the statistical knowledge to apply them. We developed a tool to automatically analyze multiple choice exams and their embedded items, using several statistical methods like the mentioned Rasch Model and its extensions for polytomous item response categories as well as more commonly known methods like hierarchical clustering, multidimensional scaling, factor analysis or analysis of variance. The automatically generated report lists the respective key figures of the various analyses, together with detailed explanations to help non-statisticians to easily interpret the results, thus enabling them to assess and improve the quality of their multiple choice exams and test items. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Proceedings of the European Conference on e-Learning (ECEL) is the property of Academic Conferences & Publishing International Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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