This post originally appeared on Next Gen Learning in Action on Education Week.
By Sajan George, Founder & Chief Executive Officer for Matchbook Learning
What is the greatest challenge to competency-based learning scaling across America?
A popular answer is the hard rules and soft infrastructure our K-12 systems devote toward seat time mandates. I disagree. While seat time is a policy barrier and there are no doubt a set of operating protocols that support and embed this historic feature, it's only a matter of time until we have enough proof points for momentum to shift away from seat time mandates.
The greatest challenge is our accountability systems which are deeply rooted in grade-level proficiency. Our accountability systems, for the most part, do not reward meeting every student where they are and progressing them based on mastery. In fact, most accountability systems punish you for this because it can appear that students who are behind grade level are falling even farther behind. Absolute grade-level proficiency, measured annually, is akin to expecting a marathoner to sprint 26 consecutive one-mile bursts. No marathoner competes like that.
The hat-trick for those of us providing competency-based learning for children who are way behind grade level (see: Matchbook Learning) is to be able to do enough of both--meeting students where they are AND progressing them to and through grade-level proficiency--in accelerated time frames to satisfy the state authorities and still adhere to our methodology's ideology.
I've recently consulted with three experts in the field of education to help me discern this vexing challenge for Matchbook Learning, the organization I lead. Their insights I believe could help our broader field as well.
1. Dr. Douglas Ready, Columbia University
Dr. Ready performed hundreds of regression analyses on Matchbook Learning's historical academic data (benchmark data, Performance Series data, and State/PARCC data). What was most compelling to Dr. Ready was that students took time (a year or more) to learn our model. That is, students two years or more with Matchbook outperformed students who had been with us less than two years on State level exam proficiency rates. We're talking a factor of two in Math (double) and four in ELA (quadruple). Furthermore, Dr. Ready hypothesized that teachers were also learning our model, which had a lag effect on their students. Based on this insight, our core team decided to pre-build every unit in Spark, our learning platform, with rigorous curriculum (we chose EngageNY) so that our teachers would not have to both design their units and learn a new model simultaneously.
2. Dr. Bror Saxberg, Kaplan, Chief Learning Officer
Dr. Saxberg spent a day with our team, sharing the latest in neuroscience research. Dr. Saxberg explained how the brain has two kinds of memory, working memory and long term memory, and that the goal of all learning is to move concepts and skills from working memory into long term memory. However, our students at Matchbook Learning often come to school with their working memory already full from trauma in their personal lives, making the challenge of learning even harder. To combat that, Dr. Saxberg suggested we refine our curriculum playlists to limit the number of options students can choose. The goal is to reduce the load on students' working memory.
3. Success Academy Charter Schools, Harlem
In summary, I would say to my fellow competency-based practitioners that to meet the challenge of proving and scaling our work, we must avoid three common mistakes that hold us back:
1. Teacher Competency Capacity Overload
2. Too Much Choice
3. Personalized Pacing & Progress = Slow Pacing & Rigor
Sajan George is Founder and CEO of Matchbook Learning. Follow him on Twitter @MatchbookLearn.
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