Student Performance by Demographics- Individual Tests

👥 What is Overall Student Performance by Demographics?

This report shows how different student groups performed on assessments — broken down by demographic categories like:

• Ethnicity/Race
• Gender

In relation to how they did:

• Number Assessed
• Number Failed
• Number Passed
• Percent Passing

Key Observations

What the Numbers Are Really Showing: Think of this assessment like a thermometer – it’s supposed to measure what your students know, but just like a broken thermometer might give different readings in different rooms, this test might be measuring things differently for different groups of students.

The Pattern You’re Seeing:

• Your Hispanic/Latino students (who make up almost half your school) are passing at 83.8% while Asian students are passing at 92.3%
• This 8-point gap is bigger than you’d expect if the test was working the same way for everyone
• The gender difference (1.75 points) is small enough that it’s probably not a big concern
• Some groups have so few students (like only 4 American Indian students) that their percentages don’t tell us much

Peak Considerations

Why This Pattern Matters: When you see patterns like this across large groups, it’s usually the test, not the students. The Hispanic/Latino students aren’t less capable – the test might just not be showing their true abilities.

What Could Be Going Wrong:

• Questions might use cultural references some students don’t know (like asking about snow skiing in a community where most kids have never seen snow)
• Language might be unnecessarily complex, testing reading ability instead of the actual subject
• Examples might not connect to all students’ life experiences
• The test might be measuring cultural familiarity instead of actual knowledge

Implications for Assessment Quality

What This Data Suggests About the Test: This pattern indicates the assessment may not be providing an accurate picture of what all students actually know and can do.

Fairness Concerns:

• The test might be unfairly disadvantaging certain groups of students
• Large performance gaps between demographic groups suggest the test may not be measuring the same thing for everyone
• This could lead to incorrect conclusions about student abilities and needs

Reliability Issues:

• When a test works differently for different groups, we can’t trust that it’s measuring consistently
• The small sample sizes for some groups make it hard to know if their results are meaningful
• Score comparisons between demographic groups may not be valid

Actionable Recommendations

For Test Analysis:

1. Review test content – Check if questions use examples that make sense to all student groups
2. Examine language complexity – Look for unnecessarily difficult wording that might confuse students
3. Analyze cultural references – Identify items that might favor students from certain backgrounds

For Data Interpretation:

1. Use caution with comparisons – Don’t assume demographic differences reflect actual ability differences
2. Look for patterns – When large groups consistently underperform, investigate the test rather than the students
3. Consider multiple measures – Use various assessments to get a complete picture of student learning

Immediate Assessment

Questions to Investigate:

• How was this test developed and by whom?
• Was it reviewed for potential bias before being used?
• Have similar patterns appeared in other schools or districts?
• What other data sources can provide insight into student performance?

Action Items:

• Review the actual test questions for potential bias
• Look at item-level data to see which specific questions show the biggest demographic gaps
• Compare these results to other assessments or measures of student achievement

Future Test Development

What Better Test Development Looks Like:

During Test Creation:

• Tests should be reviewed by people from different backgrounds before being used
• Questions should use examples that make sense to all students
• Tests should be tried out with diverse groups of students first

Ongoing Monitoring:

• Regular analysis of performance by demographic groups
• Systematic review and revision of problematic test items
• Training for test developers on creating fair assessments

Quality Assurance:

• Establish clear standards for acceptable performance gaps between groups
• Create processes for investigating and addressing bias when it’s detected
• Involve community stakeholders in test review processes