Testing Practices and Scoring Guidelines

Liesl Angelle discusses avoiding common mistakes in administering oral reading tests with aimsweb. Key points include using standardized instructions, managing testing conditions, and accurately scoring. Emphasis is placed on not altering instructions, avoiding practice effects, and handling dialect differences. The importance of qualitative data alongside quantitative scores is highlighted, with guidelines for calculating median scores and managing interruptions.

Why is it important to use the same passages for testing in fall, winter, and spring?

• To ensure students remember the passages

• To maintain consistency in testing conditions

• To reduce the number of passages needed

• To make the test easier for students

What should be avoided to prevent students from gaining an unfair advantage during testing?

• Allowing students to see the test beforehand

• Using different passages for each student

• Testing students in a noisy environment

• Giving students extra time to read

What is a common mistake made during testing?

• Testing in a quiet room

• Using too many passages

• Providing feedback during the test

• Altering standardized instructions

How should repetitions in a student's reading be scored?

• Ignore the repetitions

• Mark each repetition as incorrect

• Count repetitions as extra words

• Mark the word as correct

What should be done if a student skips an entire row during reading?

• Ignore the skipped row

• Count each word in the row as an error

• Ask the student to read the row again

• Mark the row as correct

Why is the median score used instead of an average in reporting?

• It provides a higher score

• It is more statistically reliable

• It is required by the software

• It is easier to calculate

What is the purpose of collecting qualitative data during testing?

• To make the test more challenging

• To provide a more comprehensive understanding of a student's reading abilities

• To reduce the number of errors

• To replace quantitative data