The ability to make predictions while reading is an important component of reading comprehension. Students are often fairly comfortable making predictions about fictional stories, but struggle when it comes to doing the same with nonfiction or informational texts. The less-familiar text structures and content make this strategy more challenging. We’ve highlighted three lessons that provide opportunities for students to make predictions from informational text in print and on the web.
Lesson 1: Headings (Grades 1-2)
In this lesson, students make predictions from the headings used in informational text. Students then use the text and pictures to confirm their predictions. The lesson can be easily adapted for use with any informational text that contains headings. This lesson meets the following NCTE/IRA Standards: 1, 3.
Making Predictions in Nonfiction (Grades 3-5)
In this lesson, students practice making predictions and revising their predictions from nonfiction text. The lesson is written in such a way that it can be used with any nonfiction text. It includes both teacher modeling and guided practice, and a down-loadable graphic organizer. This lesson meets the following NCTE/IRA Standards: 1, 3.
Using the Internet to Facilitate Improved Reading Comprehension (Grades 3-5)
This lesson uses a popular web-based technology, Really Simple Syndication (or RSS) feeds, to facilitate inferential thinking. Students use the clues provided by RSS feeds to predict the content of the linked post, article, or video. They use a T-chart to record and evaluate their predictions. Thus, the lesson combines several fundamental skills for reading success: inferential thinking, critical thinking, and new literacy skills. This lesson meets the following NCTE/IRA Standards: 1, 3, 6, 8, 11, 12.
This article was written by Jessica Fries-Gaither. Jessica is an education resource specialist at The Ohio State University and project director of Beyond Penguins and Polar Bears. She has taught in elementary and middle school settings. Email Jessica at firstname.lastname@example.org.
Copyright February 2011 – The Ohio State University. This material is based upon work supported by the National Science Foundation under Grant No. 1034922. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This work is licensed under an Attribution-ShareAlike 3.0 Unported Creative Commons license.