Smoothing techniques are designed to improve the accuracy of equating functions. The main purpose of this study is to compare seven model selection strategies for choosing the smoothing parameter (C) for polynomial loglinear presmoothing and one procedure for model selection in cubic spline postsmoothing for mixed‐format pseudo tests under the random groups design.
Research suggests that the three-option format is optimal for multiple choice questions (MCQs). This conclusion is supported by numerous studies showing that most distractors (i.e., incorrect answers) are selected by so few examinees that they are essentially nonfunctional. However, nearly all studies have defined a distractor as nonfunctional if it is selected by fewer than 5% of examinees.
Adaptive learning requires frequent and valid assessments for learners to track progress against their goals. This study determined if multiple-choice questions (MCQs) “crowdsourced” from medical learners could meet the standards of many large-scale testing programs.
To create examinations with scores that accurately support their intended interpretation and use in a particular setting, examination writers must clearly define what the test is intended to measure (the construct). Writers must also pay careful attention to how content is sampled, how questions are constructed, and how questions perform in their unique testing contexts.1–3 This Rip Out provides guidance for test developers to ensure that scores from MCQ examinations fit their intended purpose.