The Semantic Feature Analysis strategy engages students in reading assignments by asking them to relate selected vocabulary to key features of the text. This technique uses a matrix to help students discover how one set of things is related to one another. Use this strategy to help students:
- understand the meaning of selected vocabulary words
- group vocabulary words into logical categories
- analyze the completed matrix
A Semantic Feature Analysis improves students' comprehension, vocabulary, and content retention. This strategy helps students to examine related features or concepts and make distinctions among them. By analyzing the completed matrix, students are able to visualize connections, make predictions, and better understand important concepts.
Teachers can use this strategy with the whole class, small groups, or individually. Monitoring each student's matrix provides teachers with information about how much the students know about the topic. This allows teachers to tailor instruction accordingly.
Create and use the strategy
Select a passage of text for students. Model the procedure for using the matrix as a tool for recording reading observations. Provide students with key vocabulary words and important features related to the topic. Assist students as they prepare their matrix. Vocabulary words should be listed down the left hand column and the features of the topic across the top row of the chart. Once the matrix outline is complete, review all the words and features with the students and have them carefully read the text selection.
Follow the steps below for using the Semantic Feature Analysis strategy:
- Have students read the assigned text.
- As they read, have students place a "+" sign in the matrix when a vocabulary word aligns with a particular feature of the topic. If the word does not align students may put a "-" in the grid. If students are unable to determine a relationship they may leave it blank.
- After reading and completing the matrix, have students analyze their completed graphics by:
Grammar-Based Feature Generation for Time-Series Prediction (SpringerBriefs in Applied Sciences and Technology / SpringerBriefs in Computational Intelligence)