Analysis: Multiple Choice Items - Distribution of Incorrect


This is a useful view when doing a side-by-side analysis of the actual assessment because it offers insight into how students responded to any given item. It is most helpful to first identify a list of "items of concern" in the first step before going too deeply here. It is also helpful to identify possible areas of strength for students and curriculum.

NOTE: This view identifies the INCORRECT responses only. The 100% seen next to each item means you are viewing "100% of the incorrect responses." It does not mean "100% of the students answered each item incorrectly. Also, the "weight" of those percentages varies depending on the number of incorrect responses.

Example:
The 1, 2, 3, and 4 correspond to multiple choice selections. On the Regents Exams these are the same: 1, 2, 3, 4. On the 3-8 assessments, these correspond to letters (i.e., 1 = A, 2 = B, etc.).

1234
MC-00125%50%0%25%
MC-00250%0%0%50%

If 20 students answered MC-001 incorrectly, then we know 5 answered 1, 10 answered 2, 0 answerd 3 (the right answer by process of elimination), and 5 answered 4.

Remember that we're working with percentages here, so it's important to know the total number of incorrect responses for each item because perhaps only 4 students responded incorrectly for MC-002. The mistakes of 4 students may be lend some insight into those students, but out of a class of 25 students, if 20 answered an item incorrectly the insight from these errors may lead to instructional and curricular investigation. We cannot identify what the correct response (from this view of the data) was for MC-002 because two responses had 0% so there is no process of elimination.


Facilitation Questions

  • What are some trends you noticed when investigating the errors students made? Were there patterns or trends in the types of questions? Were there patterns or trends in the content of the questions?
  • Why is it important to know the total number of incorrect responses for each item before looking too deeply at this view of the data?
  • What additional student performance data do you have that supports or contradicts what you found here?
  • What did you learn about how you teach or assess students from this analysis of the data?


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