WHO WE ARE
STEP LEARNING. We're a small R&D firm following the science to solve pinch-points
in education. Using our proprietary AI-enabled metacognition engine, we create individual student scaffolds containing
Individualized Learning Plans and targeted remediation.
Our intellectual property leans heavily into group-based solutions using our own advanced peer conferencing platform.
There is nothing like it.
ABOUT STEP LEARNING
WHAT WE DO
Metacognition. Over the past couple of years, our small group of engineers, scientists, and partners have developed organic, dynamic and evolving solutions to next-gen education requirements. Our palette includes over 165 proprietary modules (APIs) each module is completely autonomous, scalable, and compartmentalized. When combined with our proprietary metacognation-centered tech (the patterns behind one's thinking or decisions) we've developed an education platform uniquely focused on applying today's most advanced technologies with the indivualized requirement of every student; whatever special or unique needs that student may have.
GROUPS ARE THE GLUE
Our Metacognitive Engine works behind-the-scenes through our community-building conferencing platform, Huddle. Huddle is designed work within the flexible confines of our Scaffold, providing unmatched classroom capabilities (such as Oversee Mode, Attention Verification, Advanced Sub.Huddles, and Anonymity), with high security, and seamless integration of our comprehensive API suite. Of course, Step is fully hybrid, also accomodating individual students.
APPLYING METACOGNITION IN STEP EDUCATION
SELF-MONITORING:
Step create assignments that naturally lead students to reflect on their learning process.
For example, writing tasks (e.g., CCSS.ELA-LITERACY.W.3.1) include reflective journal entries that encourage students to consider how they formed their
arguments.
GROUP GOALS:
Step uses "think-alouds" aligned with CCSS to model how to approach a task.
For example: While analyzing a text (CCSS.ELA-LITERACY.RI.6.5), you can verbally explore how to identify the author's intent without
explicitly saying "this is metacognition."
SCAFFOLDING SKILL PROGRESSIONS:
Step poses higher-order questions that naturally trigger metacognition, like:
"Why do you think this solution works?"
"What other approaches might lead to a better result?"
These align well with math practice standards (e.g., CCSS.MATH.PRACTICE.MP3).
Step provides Dynamic Learning Plan (ILP) Generation
1. Personalize Content: Recommend lessons, videos, or practice problems tailored to the specific misconception.
2. Set Micro-Goals: Break down the learning plan into small, achievable objectives (e.g., "Learn to find the least common denominator").
3. Provide Scaffolding: Include step-by-step guides or hints to help the student address the gap.
4. Enable Mastery-Based Progression: Ensure the student demonstrates understanding before advancing to more complex topics.
INDIVIDUALIZED METACOGNITION:
Why "Implicit" Matters
Implicit scaffolding avoids overwhelming learners by embedding metacognitive processes within the tasks themselves, allowing students to
develop these skills naturally without being explicitly told they are engaging in metacognition.
Example: If a student answers incorrectly because they added numerators and denominators directly, the AI recognizes a conceptual error in
fraction addition.
Step generates immedidate feedback and analysis:
1. Analyze the Error: Identify whether the error stems from a conceptual misunderstanding, a procedural error, or a lack of foundational knowledge.
2. Classify the Error: Assign the response to predefined misconceptions or skill deficiencies.
3. Ask Clarifying Questions: Follow up with targeted prompts to pinpoint gaps further.
OUR VALIDATED API-MODULES
Our framework contains over 165 interoperable modules each connected by our easy-to-deploy API. (with SDKs for major languages). Here are some few key modules and features...