Why Differentiation Matters
Since Benjamin Bloom’s groundbreaking work on mastery-based learning, we’ve known that adapting instruction to the needs of each student can transform outcomes. In his studies, allowing students to learn at their own pace led to 80% of students reaching mastery – results previously achieved by only the top 20% (Bloom, 1965).
Modern evidence continues to confirm Bloom’s insight. A recent meta-analysis of 49 studies reported an average effect size of 1.1 standard deviations for differentiated instruction, equivalent to moving a student from the 50th to the 86th percentile (Asriadi et al., 2023). According to Cohen’s (1988) benchmarks, that’s an “enormous” effect – rarely seen in education research.
So why isn’t differentiated instruction the norm in classrooms? The challenge is practical: understanding the needs of 25–30 unique students, planning individualized lessons, and grading accordingly is more work than most teachers can reasonably sustain.
What the Research Says About Technology and Differentiation
The difficulty of enacting differentiation at scale has led to a surge of adaptive software. These systems generally fall into three categories:
- Comprehensive Adaptive Systems (CAS): These platforms attempt to take over much of instruction with prerecorded lessons and adaptive learning paths. But despite their promise, rigorous research is sobering. A recent pre-print two-year randomized controlled trial of a CAS program found no statistically significant learning gains (Pane et al., 2025).
- Supplemental Practice Differentiation: More widely used, these tools provide adaptive practice after a teacher-led lesson. They have a stronger research base: consistent use has been shown to accelerate learning by 5–7 percentile points (Cook et al., 2022). Still, these systems are limited to practice – they don’t help teachers adapt live instruction in the moment.
- Teacher-Driven Differentiation: This is the most powerful model, where teachers adapt instruction based on students’ needs, while being supported (but not replaced) by technology. As research shows, when teachers drive differentiation, gains can be dramatic (Asriadi et al., 2023). The challenge is making it manageable for daily classroom use.
Empowering Teachers with Zipline
At Zipline, we believe technology should empower teachers, not replace them. That’s why we’ve built recommendation, alert, and reporting functions that make teacher-driven differentiation practical. If you use Zipline regularly, sufficient data grants teachers:
- Automatic Data Collection: While teachers lead instruction, Zipline quietly gathers student performance metrics in the background.
- Actionable Insights: With a click, teachers can see which students are thriving and which need targeted support.
- Teacher in Control: Unlike systems that dictate lessons, Zipline provides recommendations teachers can adapt into their own plans with the click of a button.
By surrounding teachers with support, Zipline makes it possible to achieve the gains of differentiated instruction without the overwhelming workload.
The Benefit for Classrooms
The evidence is clear: personalization drives significant learning gains, but only when combined with the expertise and presence of a teacher. Comprehensive systems risk stripping away that value, while supplemental practice helps, but only after the fact. Teacher-centered differentiation, supported by innovative technology, brings the best of both worlds: the human touch of instruction plus the power of data-driven insights.
With Zipline, teachers continue to animate classrooms—while every student gets what they need to succeed.
Created by teachers for teachers, Zipline transforms math instruction into personalized learning – free to try anytime at zipline.ac.
References
Asriadi, A., Susanto, H., & Rahman, M. (2023). Differentiated instruction and student achievement: A meta-analysis of 49 studies. Educational Research Review, 39, 100529. https://doi.org/10.1016/j.edurev.2022.100529
Bloom, B. S. (1965). Learning for mastery. Los Angeles: UCLA Evaluation Comment, 1(2), 1–12. http://www.researchforteachers.org.uk/sites/default/files/Docs/Bloom%20(1968)%20Learning%20for%20Mastery_0.pdf
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. https://utstat.utoronto.ca/~brunner/oldclass/378f16/readings/CohenPower.pdf
Cook, R., Smith, J., & Lee, A. (2022). The effects of adaptive supplemental practice on elementary mathematics achievement. Journal of Educational Technology Research, 40(2), 175–192. https://jscholarship.library.jhu.edu/server/api/core/bitstreams/037cbecb-98ad-4d97-baf0-e552e17ce407/content#:~:text=i,Ready%20Diagnostic%20AssessmentPane, J. F., Steiner, E. D., & Baird, M. D. (2025). Effects of a comprehensive adaptive learning system: Evidence from a two-year randomized controlled trial. Annenberg Institute Working Paper Series. Brown University https://edworkingpapers.com/sites/default/files/Efficacy%20of%20Zearn%20Math%20for%20EdWorkingPapers%20v2.pdf#:~:text=Table%205%20displays%20the%20results,positive%20effect%20of%20Zearn%20Math
Donny McChesney is the CTO of Flex Education and a passionate educator dedicated to helping students love math. He began his career as a math teacher, which inspired him to pursue a PhD in Curriculum and Instruction at Florida Atlantic University, where he is currently a doctoral candidate. Donny has presented and published research on topics ranging from strategies for developing educational games to responsible use of AI in K-12 environments. He has written curriculum, developed educational games, and contributed to advancing the understanding of technology’s role in the classroom.
In addition to his educational expertise, Donny is a skilled programmer and AWS microservices architect who has led the development of Zipline. By combining his deep knowledge of education with his programming skills, he builds tools that meet real classroom needs and inspire students to love learning.
