<![CDATA[Extraneous cognitive load (ECL) measurements are important to evaluate the quality of digital learning design, but self-report instruments need to be tested for psychometric quality. This study aims to evaluate students' ECL instruments using the Rasch model. Data was collected through a survey of 38 students using an ECL questionnaire containing 17 items with a Likert scale of 1–5, then analyzed with the Rasch Rating Scale Model to assess reliability-separation, fit items and persons, as well as targeting of persons through the Wright map, The results showed that mapping of person-items on a logit scale could be done, but the targeting was less optimal because the average person was far below the average of items. indicates low ECL dominance and limited item coverage at low range. Category diagnostics show a very uneven distribution of responses (categories 4–5 are hardly used), and the probability curve of categories that do not form a clear peak in the upper categories, so the 5 category scale function is not yet effective. It was concluded that the instrument could be used as an initial measurement of ECL, but it needed to be improved on some items and the addition of items to improve precision and interpretability.Keywords: AI, Biology, ECL, Learning Media, Technology.]]>
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