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The retrieved reading passage and a lexical dictionary are utilised to generate the remaining question components (Susanti et al. 2015).
According to Susanti et al. (2016), the word difficulty of the question components contributes to the difficulty of question items by up to 60%.
Unlike these past attempts, the automatic question generation system used in this work utilises Web texts from the Internet as well as information from WordNet to generate the question components (Susanti et al. 2015).
Their study revealed that (1) the word difficulty level of the question components contributed up to 60% of the item difficulty and (2) distractors had the greatest impact on item difficulty.
This study shows that item difficulty can indeed be controlled intrinsically by controlling the question components, thus suggesting the possibility of generating items automatically on-the-fly during the administration of a computerised adaptive test.
In this study, we propose a method that controls the item difficulty using three predetermined factors related to the question components: (1) reading passage difficulty (RPD), (2) semantic similarity between the correct answer and distractors (SIM), and (3) distractor word difficulty level (DWD).
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The following are the comments for each question component for MQs.
The general structure of word problems is centered on three components: (a) a setup component, which provides the content (for instance, the place or story problem); (b) an information component, which provides data from which to derive a mathematical model; and (c) a question component, which is the main task directed to the solver (Gerofsky 2004).
The discussion question component of the assignments was based on assigned readings from the course textbook (Simpson, 2010) and covered the terminology and concepts related to the skills of constructing and interpreting phylogenetic trees.
In order to answer these questions components of the executive functioning system, associational networks, and models of information processing are implicated and applied to a DvT framework.
Once the model is validated, it can be used for forecasts, decisions, or actions determined by the problem-question component.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com