> ## Documentation Index > Fetch the complete documentation index at: https://docs.learningcommons.org/llms.txt > Use this file to discover all available pages before exploring further. # Literacy > Access and understand the literacy dataset used for evaluator development, including expert-annotated benchmark data for text complexity evaluation. [Look at the annotated data.](https://github.com/learning-commons-org/evaluators/tree/main/datasets) ↗ ## About the dataset The Literacy dataset contains high-quality text complexity annotations for the CommonLit Ease of Readability (CLEAR) Corpus by literacy and education experts. The CLEAR Corpus was produced by [CommonLit in collaboration with Georgia State University](https://www.commonlit.org/blog/introducing-the-clear-corpus-an-open-dataset-to-advance-research-28ff8cfea84a/) ↗ and released in December 2021. It comprises nearly 5000 publicly available excerpts, each mapped against dimensions including Flesch-Kincaid and BT Easiness (Bradley-Terry coefficient based on teacher ratings of the text). We expanded the dataset by scoring a subset of rows for dimensions in Student Achievement Partners (SAP)'s [Qualitative Text Complexity Rubric for Informational Text](https://learnwithsap.b-cdn.net/app/uploads/2026/04/Qualitative-Text-Complexity-Rubric-Informational.pdf) ↗. Our [Literacy evaluators](/evaluators/literacy-evaluators/introduction) use this dataset as a benchmark when assessing AI-generated content. We hope edtech developers can use it to complement their own literacy work as well. Our initial release in September 2025 focuses on Grades 3 and 4 across sentence structure and vocabulary, but we plan to expand to all grades and all dimensions of text complexity assessed through{" "} SAP's Qualitative Text Complexity rubric. ## Our process Our process for producing annotated data is as follows: * Filter the CLEAR corpus to an approximate grade 3-4 range using Flesch Kincaid Grade Level. * Partner with literacy experts from SAP and Achievement Network (ANet) to score against dimensions in SAP's Rubric. * With SAP and ANet, establish a gold set of \~80 examples per grade with representation across the 4 tiers of text complexity (Slightly, Moderately, Very, and Exceedingly complex) * Use the gold set to test and qualify a cohort of educators with 2+ years of experience teaching English Language Arts (ELA) at the corresponding grade level. * Produce 200+ rows (50+ per complexity tier), calibrating annotator scores using the Dawid-Skene method. flowchart showing a high-level overview of the annotation process Finally, we package the dataset by mapping multiple dimensions of text complexity to the clear\_id column. This results in an annotated dataset that can be easily merged with the CLEAR corpus. ## Columns Last updated September 23, 2025 This list will be updated as we incorporate more text complexity dimensions. | Column | Definition | | :---------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **UID** | Unique identifier for each row. | | **Clear ID** | Identifier for texts based on the CLEAR corpus. This is not a unique identifier, as some texts were scored for multiple grades. | | **Grade** | Grade-level for which the text is scored. For example, if Grade=3 and Sentence Structure Complexity Score Slightly Complex, then the text is Slightly Complex for a third-grade student (see overall project documentation for specific assumptions). | | **Flesch-Kincaid** | Flesch-Kincaid Grade Level score for the text, provided from the CLEAR corpus. | | **Text** | Text from the CLEAR corpus that was annotated. | | **Sentence Score** | Overall annotator rating for sentence structure complexity. Takes the values slightly complex, moderately complex, very complex, and exceedingly complex. See the technical docs for additional details on these categories. | | **Sentence Score Rationale** | Annotators’ explanations for their sentence structure score. | | **Vocabulary Score** | Overall annotator rating for vocabulary complexity. Takes the values slightly complex, moderately complex, very complex, and exceedingly complex. See overall project documentation for additional details on these categories. | | **Vocabulary Score Rationale** | Annotators’ explanations for their vocabulary score. | | **Tier 2 Words** | Words that are commonly used in academic settings and are more complex than colloquial, or everyday language, and often have multiple meanings

**Example:** "There are eight planets in the Solar System. From closest to farthest from the Sun, they are: Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune." Most planets (Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune) are Tier 2 words for Grade 3. | | **Tier 3 Words** | Words that are limited to a specific domain or that are so rare that an avid reader would likely not encounter them in a lifetime

**Domain-specific example:** enzyme
**Rare unconventional example:** abecedarian | | **Archaic Words** | Words, or uses of words that are not commonly used in modern conversational language

**Example**: "The jury retired to deliberate on their verdict." Using "retire" to mean "withdrawing to a private place" is an archaic use. | | **Other Complex Words** | Additional complex words for students of the grade level, as identified by annotators. | | **Background Knowledge Assumption** | LLM-generated information on the background knowledge that students of a particular grade are likely to have about a topic. Information was provided to annotators during the scoring process. See the overall project documentation for detailed methodology on how this was generated. | Receiving **Not Scored** for a given column means that the text was not annotated for that column. ## Limitations * Annotator coverage per item is limited; reported precision and agreement reflect this coverage. * Annotations in future updates will improve statistical reliability by increasing confidence, reducing variance, and stabilizing borderline cases. * Flesch–Kincaid grade estimates (based on sentence and word length) are heuristic and do not capture qualitative factors (e.g., conceptual difficulty, vocabulary sophistication, thematic maturity). * They are recorded as metadata and may be referenced in the evaluator prompt as one of multiple signals. * They are not the sole determinant of evaluator labels.