By Mark J Gierl, Thomas M. Haladyna
"Automatic merchandise iteration (AIG) represents a comparatively new and specific study zone the place particular cognitive and psychometric theories are utilized to check building practices for the aim of manufacturing attempt goods utilizing know-how. the aim of this e-book is to deliver researchers and practitioners updated at the starting to be physique of study on AIG via organizing in a single quantity what's at the moment identified approximately this study region. half I starts with an summary of the strategies and issues useful for knowing AIG by way of targeting either its background and present purposes. half II provides theoretical frameworks and useful purposes of those frameworks within the construction of merchandise iteration. half III summarizes the mental and important features of generated goods . half IV concludes with a dialogue of the statistical versions that may be used to estimate the object features of generated goods, beneficial properties one destiny program of AIG, describes the present applied sciences used for AIG, and in addition highlights the unresolved concerns that needs to be addressed as AIG maintains to mature as a study zone. complete The booklet presents a complete research of either the theoretical thoughts that outline automated merchandise new release and the sensible issues required to enforce those innovations. diversified purposes Readers are supplied with novel functions in assorted content material components (e.g., technology and interpreting comprehension) that diversity throughout all academic degrees trouble-free through university"-- Read more...
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Additional info for Automatic item generation : theory and practice
Irvine & P. C. ), Item generation for test development (pp. 199–218). Mahwah, NJ: Lawrence Erlbaum Associates. Bejar, I. I. (2009). Model-based item generation: A review of recent research. In S. E. ), Measuring psychological constructs: Advances in model-based approaches. Washington, DC: American Psychological Association Books. Bejar, I. , Lawless, R. , Morley, M. , Wagner, M. , Bennett, R. , & Revuelta, J. (2003). A feasibility study of on-the-fly item generation in adaptive testing. Journal of Learning, Technology and Assessment 2(3), 2–29.
The decision about which animals to include in the pool serves as the statistical difficulty “driver” in this example, meaning that different animals could elicit different item difficulty estimates. Using weak-theory AIG, the goal is most often to select those animals that would yield items with a similar level of difficulty. S2 includes content on animal habitat. In our example, three habitats are presented: prairie, mountains, and tundra. Other habitats expected to yield generated items with a similar difficulty level could be included.
Alternatively, when the goal of using weak theory is to generate items that are not statistically calibrated (in this case, the generated items will still need to be field tested), then the content specialist’s task is to manipulate those elements that yield large numbers of instances of the parent item through the generative process. This second goal is seldom the purpose of weak-theory AIG, although it can be used to generate items needed to initially develop banks or to replenish the banks as items are retired.
Automatic item generation : theory and practice by Mark J Gierl, Thomas M. Haladyna