Chunking
Overview
Chunking involves breaking texts down into more manageable pieces to help learners focus their Attention while reading and to comprehend text more effectively. This strategy helps to strengthen and build Reading Fluency. Chunking techniques can be used with traditional and digital texts as instructors can arrange and group different parts of the texts for learners to process each chunk of text individually. This can be done in a number of ways depending on the needs of the learner. Chunking can be particularly effective for supporting multilingual learners and for struggling readers, including those with dyslexia.
Use It In Your Learning Environment
Teachers can break down high level texts into smaller paragraphs or sections to help learners understand the main ideas of each piece. For these advanced texts, chunking can aid with annotation, allowing learners to highlight difficult vocabulary and ask questions of the text without getting overwhelmed by the long format.
Developers can create spaces where teachers can digitally alter text to group paragraphs together and highlight important sections to help readers focus Attention. Products should have the capability for teachers to upload scanned text to allow for digital chunking of the text. Additionally, developers can provide a space for content-specific key Vocabulary words that can support breaking texts into meaningful and accessible sections. Annotation capabilities can also help teachers model how to chunk texts independently and give students practice opportunities.
Additional Resources
Additional examples, research, and professional development. These resources are possible representations of this strategy, not endorsements.
Factors Supported by this Strategy
More Teacher Modeling and Support Strategies
Immediate feedback can improve a learner's confidence, self-awareness and enthusiasm for learning, which leads to increased Motivation.
Adult learners benefit from knowing there is an instructor available to provide support as needed, especially during asynchronous learning.
