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By Shawn Francis, Senior Director of Product — PersonalizationThere is a striking convergence in the language that most innovation-focused education products use to describe their programs.

Phrases like “personalized learning,” “data-driven instruction,” and “individualized pathways” are common; however, they are often abstracted from any sort of clear connection to tools and instructional resources that enable a real shift in teacher practice and student outcomes.

They are comforting in their promise of a tech-enabled future where all students get the right thing at the right time, but that promise does not always bear out in the content solutions that end up in classrooms.

Teachers find themselves drowning in an ever-rising flood of student data from multiple sources of varying quality, with little support in turning data into insights, and then into action.

The Urgency of Personalized LearningThe gap we often find between how a product is positioned and how it actually works is not something unique to personalized learning solutions.

But the lost opportunity, and the impact on students, certainly is.

In Judy Hughey’s “Individual Personalized Learning,” she states “Personalized learning is designed to address the disengagement of today’s students and to be proactive in closing the growing achievement gaps occurring in far too many schools.

The goal of personalized learning is to engage students in the process, building on their interests, aptitudes, and strengths, thus creating intrinsic motivation for achievement and success” (1).

Amidst a protracted achievement recovery from learning loss due to Covid school closures, the need for authentic personalized learning has arguably never felt more relevant or urgent.

A landmark 2015 research study by the RAND Corporation showed gains in Mathematics and English Language Arts across 62 schools enacting a range of personalized learning strategies and systems — -notably, with accelerated growth among the students with lower starting achievement (2).

One of the largest studies of the impacts of personalized learning at the time, this set off a frenzy across school, district, and technology leaders to shift towards personalized learning in classrooms.

However, the study’s broad definition and set of practices did not leave an explicit blueprint for successful product development and program implementation.

In the years since, the number of personalized learning solutions in the market has grown dramatically, whether they be standalone adaptive products, infused into core solutions, or plug-and-play technology systems intended to support content from multiple sources.

However, as teachers’ instructional ecosystems and available lesson resources have grown exponentially, many find themselves struggling to synthesize data across tools and make efficacious choices for each student’s needs.

Sometimes this is from a lack of available data analysis tools, limited articulation of a clear vision of personalized learning coupled with the continuing education to enact those practices in the classroom, or both.

To begin to close the gap between the promise of personalized learning in the classroom, teachers need two things: data systems that help them make sense of complex data and professional learning that builds a framework and related practices for implementing high-quality personalization in their classrooms.

Combining Proficiency Data Across SourcesIncreasingly, teachers are faced with a depth and breadth of data around student performance, which would be enviable if not for the fact that drawing clear conclusions across data sources is rarely a simple proposition.

A recent report from Instructure noted that “The number of unique edtech tools accessed by students and teachers also increased from last school year; on average, students accessed 45 tools during the 2023–24 school year, three more than the previous school year (42).

Educators accessed an average of 49 tools during the school year (up from 42 last school year)” (3).

Each tool a student or educator accesses generates critical data around student performance, understanding, and need.

Determining student proficiency with respect to a particular skill or standard can seem daunting when performance data is fractured across multiple moments of measurement, in disconnected data sources.

Providing teachers with a singular, combined view of proficiency across multiple data sources is foundational to building capacity for implementing personalizing learning at scale.

McGraw Hill’s Standards and Skills Graph offers a unique approach to solving this challenge.

This data and insights tool aggregates student performance across multiple McGraw Hill programs, coupled with benchmark assessment data from local sources, to provide teachers with one comprehensive view of student proficiency.

These student proficiency measures are paired with individualized instructional recommendations, ensuring students can engage in personalized resources that are based on all of their digital interactions rather than a single assessment in a single program.

Teacher Competencies and Professional Learning SupportIn a 2021 meta-analysis of existing personalized learning research, the authors concluded that “Those who select, configure, and deploy personalized learning in authentic learning environments need encouragement to formalize their theory of change” (4).

In recognition of this, McGraw Hill’s Teacher Competencies are built with personalized learning threaded throughout, ensuring that teachers’ approach to continuing to build their capacity is founded around a genuine commitment to truly individualized instruction.

The teacher competencies push further than simply encouraging educators to use data to drive instruction.

Grounded in best practices from a number of research studies regarding efficacious personalized learning systems, teachers are asked to not just “Collect and utilize data to inform planning for both whole group and differentiated group instruction,” but also to “Utilize student data to group students for targeted instruction,” a key differentiator in the aforementioned RAND Corporation study.

Notably, these teacher competencies focus on students as the focal point of an effective personalized learning system implementation.

They express the importance of student ownership and autonomy, through engagement with their data, articulation of their learning goals, and having true choice and voice in their learning pathways.

A Foundation For a Personalized Learning FutureComprehensive data systems that demystify student data from multiple sources, coupled with robust professional learning focused on student-centered, data-driven classrooms, are key pillars of an impactful personalized learning experience for students.

Through curriculum and technology solutions that focus on both, teachers can begin narrowing the gap between the promise and reality of personalized learning in their classrooms.

(1) Hughey, Judy (2020) “Individual Personalized Learning,” Educational Considerations: Vol.

• 46: №2.
  

https://doi.

org/10.

4148/0146-9282.

• 2237 (2) Pane, John F.
  
• , et al.
  
• Continued Progress: Promising Evidence on Personalized Learning.
  
• Rand Corporation, 2015.
  
• (3) Instructure.
  

The EdTech Top 40: A Look at K-12 EdTech Engagement During the 2023–24 School Year.

 2024.

• (4) Bernacki, Matthew L.
  
• , et al.
  

“A Systematic Review of Research on Personalized Learning: Personalized by Whom, to What, How, and for What Purpose(s)?

” Education Psychology Review, 2021.

• Shawn Francis is Senior Director of Product — Personalization at McGraw Hill.
  

An education technology leader, he has spent the past 20 years of his career focused on transforming student data into innovative, personalized experiences for learners that support instructional engagement and student success across a variety of learner needs and contexts.

In his work, he has developed products and programs across the entire student K-12 journey, from foundational skills to college planning.

Shawn is a graduate of New York University, and lives in New York City.

Bridging the Gap Between the Promise and the Reality of Personalized Learning was originally published in Inspired Ideas on Medium, where people are continuing the conversation by highlighting and responding to this story.