{"product_id":"advanced-methods-in-automatic-item-generation","title":"Advanced Methods in Automatic Item Generation","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eAdvanced Methods in Automatic Item Generation is an overview of the research on automatic item generation (AIG) in the technology-enhanced educational measurement sector. It covers the theoretical foundations and concepts of AIG, as well as the practical considerations for producing and applying large numbers of useful test items. \u003c\/blockquote\u003e\u003cp\u003e\n                                                            \u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e\n                              \u003cstrong\u003eLength\u003c\/strong\u003e: 246 pages\u003cbr\u003e\n                              \u003cstrong\u003ePublication date\u003c\/strong\u003e: 19 May 2021\u003cbr\u003e\n                              \u003cstrong\u003ePublisher\u003c\/strong\u003e: Taylor \u0026amp; Francis Ltd\u003cbr\u003e\n                          \u003c\/p\u003e \u003cp\u003e\u003cbr\u003eThe field of automatic item generation (AIG) in the context of technology-enhanced educational measurement has witnessed a significant surge in research. As test administration procedures increasingly incorporate digital media and Internet use, assessment stakeholders, ranging from graduate students to scholars to industry professionals, have abundant opportunities to explore and develop diverse types of tests and test items. This comprehensive analysis provides a thorough exploration of the theoretical foundations and concepts that underpin AIG, alongside the practical considerations necessary to generate and utilize large quantities of valuable test items.\u003cbr\u003e\u003cbr\u003eThe increasing integration of digital media and Internet use in test administration procedures has opened up a wealth of opportunities for assessment stakeholders to delve into the study and creation of various test types and test items. This comprehensive analysis offers a thorough exploration of the theoretical foundations and concepts that define automatic item generation (AIG), as well as the practical considerations required to produce and apply large numbers of useful test items.\u003cbr\u003e\u003cbr\u003eAIG involves the use of computer algorithms and machine learning techniques to generate test questions that are tailored to specific learning objectives or assessment criteria. The process begins with the definition of the test domain, which encompasses the subject matter and the desired level of difficulty. Once the domain is defined, the algorithms analyze large databases of test questions and answer choices to identify patterns and relationships. These patterns are then used to generate new test items that are unique and challenging, while also ensuring that they cover the relevant content and skills.\u003cbr\u003e\u003cbr\u003eOne of the key advantages of AIG is its ability to produce a large number of test items quickly and efficiently. Traditional test development methods can be time-consuming and resource-intensive, particularly when it comes to creating multiple versions of a test or generating test items for different populations. By leveraging AIG, assessment stakeholders can generate large quantities of test items in a fraction of the time and cost compared to manual development.\u003cbr\u003e\u003cbr\u003eHowever, the use of AIG also presents several challenges and considerations. One of the primary concerns is the quality of the test items generated by AIG. While algorithms can identify patterns and relationships in test questions and answer choices, they may not always capture the nuances and complexities of the test domain. This can lead to test items that are either too easy or too difficult, which can negatively impact the reliability and validity of the assessment.\u003cbr\u003e\u003cbr\u003eTo address this concern, assessment stakeholders need to carefully evaluate the quality of the test items generated by AIG. This can involve conducting item analysis, which involves examining the difficulty, discrimination, and relevance of the test items. Item analysis can also help identify any biases or stereotypes that may be present in the test items, which can be addressed through appropriate adjustments or revisions.\u003cbr\u003e\u003cbr\u003eAnother challenge associated with AIG is the need for ongoing maintenance and updates. As the test domain changes or new research emerges, the algorithms used in AIG may need to be updated or modified to ensure that the test items continue to be relevant and challenging. This can be a time-consuming and resource-intensive process, particularly for large-scale assessments.\u003cbr\u003e\u003cbr\u003eTo address this challenge, assessment stakeholders need to establish a process for ongoing maintenance and updates of the AIG system. This can involve regular monitoring of the performance of the algorithms, as well as periodic updates to the test item bank and the algorithms themselves. Additionally, assessment stakeholders may need to invest in training and development programs to ensure that staff members are equipped with the necessary skills and knowledge to maintain and update the AIG system.\u003cbr\u003e\u003cbr\u003eIn conclusion, advanced methods in automatic item generation have revolutionized the field of technology-enhanced educational measurement. By leveraging computer algorithms and machine learning techniques, assessment stakeholders can generate large quantities of test items quickly and efficiently, while also ensuring that the test items are unique, challenging, and relevant. However, the use of AIG also presents several challenges and considerations, including the quality of the test items generated, the need for ongoing maintenance and updates, and the potential for biases and stereotypes in the test items. By carefully evaluating the quality of the test items generated by AIG, establishing a process for ongoing maintenance and updates, and investing in training and development programs, assessment stakeholders can ensure that the use of AIG enhances the reliability and validity of educational assessments.\u003c\/p\u003e\u003cp\u003e\n                            \u003cstrong\u003eWeight\u003c\/strong\u003e: 360g\n                            \u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 152 x 226 x 18 (mm)\n                            \u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9780367458324\n                            \n                          \u003c\/p\u003e","brand":"Mark J.Gierl,Hollis Lai,Vasily Tanygin","offers":[{"title":"Paperback \/ softback","offer_id":44103683014906,"sku":"9780367458324","price":39.97,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/315031ee76230ec97fc4449d988e20f5.jpg?v=1633144424","url":"https:\/\/shulphink.com\/products\/advanced-methods-in-automatic-item-generation","provider":"Shulph Ink","version":"1.0","type":"link"}