Text Complexity versus Task Complexity: Item Difficulty Modeling for Reading Items
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| Title: | Text Complexity versus Task Complexity: Item Difficulty Modeling for Reading Items |
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| Language: | English |
| Authors: | M. Christina Schneider, Jing Chen, Jeremy Heneger |
| Source: | Practical Assessment, Research & Evaluation. 2026 31(1). |
| Availability: | University of Massachusetts Amherst Libraries. 154 Hicks Way, Amherst, MA 01003. e-mail: pare@umass.edu; Web site: https://openpublishing.library.umass.edu/pare/ |
| Peer Reviewed: | Y |
| Page Count: | 17 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Test Items, Reading Comprehension, Difficulty Level, Instructional Program Divisions, Reading Achievement, Predictor Variables, Summative Evaluation, Readability |
| Assessment and Survey Identifiers: | Flesch Kincaid Grade Level Formula, Lexile Scale of Reading |
| ISSN: | 1531-7714 |
| Abstract: | This study investigates item features to aid in improving understanding of what makes items that measure reading comprehension easy or difficult. In this item difficulty modeling (IDM) study, item and passage features were included as predictors that represented text-task interactions and stimulus demands. The passage-level features included two common quantitative metrics of text complexity: the Lexile Framework® for Reading and Flesch-Kincaid. Passage word count, item type, Depth of Knowledge (DOK), and item to Range Achievement-Level Descriptor (RALD) match were held constant across conditions. Two IDM models were examined; one included passage-level text complexity features and not grade level, and the other included grade level and not passage level text complexity features. We found that quantitative metrics of text complexity added 3% to the IDM compared to when grade was substituted for those features. Text-task interactions as represented by RALDs and DOK levels were found to provide unique and significant information to the IDM model as did item type and particular standard topics. Implications for RALD construction and additional research related to RALDs for reading are discussed. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1507968 |
| Database: | ERIC |
| Abstract: | This study investigates item features to aid in improving understanding of what makes items that measure reading comprehension easy or difficult. In this item difficulty modeling (IDM) study, item and passage features were included as predictors that represented text-task interactions and stimulus demands. The passage-level features included two common quantitative metrics of text complexity: the Lexile Framework® for Reading and Flesch-Kincaid. Passage word count, item type, Depth of Knowledge (DOK), and item to Range Achievement-Level Descriptor (RALD) match were held constant across conditions. Two IDM models were examined; one included passage-level text complexity features and not grade level, and the other included grade level and not passage level text complexity features. We found that quantitative metrics of text complexity added 3% to the IDM compared to when grade was substituted for those features. Text-task interactions as represented by RALDs and DOK levels were found to provide unique and significant information to the IDM model as did item type and particular standard topics. Implications for RALD construction and additional research related to RALDs for reading are discussed. |
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| ISSN: | 1531-7714 |
