Choosing Blended Learning Software

Purpose: This document is designed to guide tutoring program leaders, educators, and instructional staff in selecting a blended learning software into high-impact tutoring models. 

Blended learning integrates live instruction with digital tools, offering personalized, adaptive practice for students and data-driven insights for tutors. For students, blended learning offers opportunities to practice independently through tailored activities that capitalize on different learning modalities – the ways in which students use their senses throughout the learning process to acquire new skills (e.g; kinesthetic, visual, auditory, and tactile) – and further individualize instruction. For tutors, blended learning provides a wealth of knowledge and granular data about student learning to help explicitly target their live instruction. 

Benefits

  • Personalized Practice: Assigns targeted exercises based on individual student needs.
  • Efficiency: Reduces tutor preparation time through centralized resources.
  • Data-Driven Instruction: Provides real-time performance data to inform teaching.
  • Engagement: Utilizes multiple sensory modalities to enhance learning.
  • Scalability: Maintains instructional quality while serving more students.

Selection Criteria

Considerations for Selecting Blended Learning Software
Data & Student Progress Tracking
  • Does the software provide concise, actionable data for both tutors and students?
  • Does it assess progress in real-time, beyond formal assessments?
  • Do the data tools:
    • Identify students who have mastered or need intervention on specific skills.
    • Highlight common misconceptions across a cohort.
    • Recommend next steps for instruction and additional practice?
Student Engagement
  • Does the software incorporate gamification (e.g., leaderboards, experience points, achievements)?
  • Does it facilitate peer-to-peer communication and collaboration?
Research-Based Design
  • Does the software follow content and pedagogical best practices (e.g., research-based reading or math instruction, immediate feedback)?
Adaptive & Personalized Learning
  • Can tutors assign specific content for individual students?
  • Does the software dynamically adapt based on student strengths and struggles?
  • Can struggling students receive scaffolding and additional support?
  • Do on-level and advanced students have access to extension tasks?
  • Does the software continuously adjust after initial placement, or is it only adaptive at the start?
  • Can students customize their goals, pace, and learning path?
User Experience & Accessibility
  • Is the interface intuitive for both students and tutors?
  • Does the software meet UDL and web accessibility standards?
  • Is the design minimalistic to avoid overwhelming users?
  • Could infrastructure limitations (e.g., slow internet, outdated devices) prevent students from accessing the software at school or home?
  • Test the software in real-world conditions. Ensure it runs smoothly on the least equipped students’ devices and internet connections. Consider:
    • What devices are students using?
    • How slow or unreliable is their internet?
    • Can a five-year-old Chromebook utilizing a phone’s data hotspot run the software effectively?

Examples of Blended Learning Software

  • ALEKS: Adaptive assessments and personalized learning paths
  • Cignition: Game-based math learning for grades 3–7
  • Khan Academy: Free tutorials and exercises across various subjects
  • Newsela: Leveled news articles with quizzes and writing prompts
  • Woot Math: Interactive math curriculum with collaborative tools
  • Zearn: Combines live instruction with digital lessons for grades 1–5

Recommended Supplemental Backend Software for Tutors (Not Student-Facing)

While not student-facing blended learning software per se, this backend data analysis software is useful for tutors to use.

  • Intervene Data Dash: A data analysis program that automatically identifies student strengths, struggles, and misconceptions from formative assessments, generating a one-page “Readiness Summary” with instructional insights. The system groups students by common errors to support differentiated instruction and measure instructional effectiveness.