How Glendale Union High School District Increased Math Rigor Without Lowering Expectations

Background

Glendale Union High School District entered the 2024–25 school year focused on a critical instructional priority: improving math proficiency while maintaining high expectations for all students. District leaders were also exploring mastery-based learning, strengthening college and career readiness, and building AI literacy in ways that aligned with instructional goals.

Rather than treating these initiatives separately, Glendale sought a unified approach that could bring rigor, differentiation, and actionable data into everyday classroom practice without sacrificing teacher ownership or instructional coherence.

Challenges

Prior to its partnership with Khan Academy Districts, many math intervention and lab courses followed a familiar structure: whole-class instruction followed by independent computer work. While this model provided access to digital practice, it surfaced several challenges:

• Student engagement during independent practice was inconsistent.
  
• Differentiation was difficult to sustain across classrooms.
  
• Technology often functioned as a supplement rather than an integrated instructional tool.
  
• Teachers experienced tension when deciding between supporting struggling students and maintaining rigor.
  
  

District leaders recognized that increasing rigor required more than assigning harder work. They needed to redesign how practice, feedback, and instructional time were structured.

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“We realized that teaching a regular class and then sending students to a separate hour of computer work wasn’t driving the engagement or differentiation we wanted.” - Administrator

Solutions

Glendale redesigned math instruction using intentional station rotation models that embedded Khan Academy directly into daily classroom practice.

Instead of isolating technology use, teachers integrated mastery-based practice into structured rotations, allowing them to do the following:

• Provide targeted small-group instruction
  
• Differentiate support without lowering expectations
  
• Use real-time data to guide instructional decisions
  
• Maintain full ownership of pacing, grouping, and instruction
  
  

At the district level, leaders established clear, shared expectations to support consistency and coherence:

• Students engaged in 30–60 minutes of Khan Academy practice per week.
  
• Teachers set a goal for students to level up at least two skills to proficient each week.
• Administrators used dashboards to monitor progress, surface patterns, and support instructional conversations.

Professional learning reinforced these practices through differentiated training for AP and intervention teachers, ongoing data-focused check-ins, and district-led coaching support.

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“When instruction became more rigorous, students initially struggled, but that was an expected and important part of the learning process.”

District Reported Outcomes

Within the first months of implementation, the following parameters were met:

• Students averaged approximately 50 minutes of weekly math practice.
  
• A growing share of students met or exceeded weekly learning targets.
  
• Students averaged more than two skills leveled to proficient per week, exceeding district expectations.
• Glendale incorporated AI support as a scaffold for students working through challenging material while preserving teacher oversight and instructional focus.
  
  

Equally important, district leaders and educators reported stronger alignment between instructional goals and daily classroom practice. Practice became more purposeful, data became easier to act on, and teachers were better positioned to support both remediation and acceleration within the same classroom.