Before, during and after reading
A traits matrix helps students understand the unique meaning of a word by comparing it to other words. By uncovering how words are similar and different, students learn the nuance and subtlety of word meaning.
Before reading, a traits matrix works like a prediction chart and draws on students’ prior knowledge and associations. During and after reading, it enhances discussion of texts and helps students master concepts that seem simple but become more complex under examination.
- Choose vocabulary words from the central text.
- Select a topic for semantic feature analysis (e.g., gender, power or government).
- In the far-left column of the matrix, list vocabulary words related to the topic.
- List some features or characteristics related to the topic across the top row of the matrix.
- If done before reading, have students fill in definitions for those words they already know and skip to Step 7.
- If done during or after reading, have students fill in definitions for those words they already know or were able to deduce from context clues. For those words that cannot be determined from context or that students are not likely to know, teachers should provide definitions or reference materials for students to look up the words.
- Have students place a “+” 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. In cases where it depends, allow students to mark “+/–” but ask them to explain.
- Whether before, during or after reading, prompt students to correct definitions and associations during the course of the activity. When the matrix is completed, look for patterns and discuss associations.
English language learners
This strategy is appropriate for intermediate English language learners (level three or above). Scaffold for less proficient learners by using a semantic gradient scale first. Write two words on opposite extremes, and have students assign gradient versions of the word to the space in between. For example, if you put “hot” and “cold” on each end, students come up with words like “sizzling” or “scorching” versus “icy” or “freezing.” Words like “chilly” or “cool” are closer to the middle. These gradations can help with the analysis of higher concept words.
Connection to anti-bias education
Semantic feature analysis aligns well with reading complex texts about anti-bias and social justice topics. A traits matrix helps students understand vocabulary and sharpens their understanding of abstract concepts like fairness or power (see samples). Ask students to consider how people from different time periods and social movements would respond to the vocabulary words (e.g., how an activist from the civil rights movement or the women’s suffrage movement might fill out the matrix).