Personalized mastery powered by learning science research and data-driven intelligence.
Research-based strategies and proven learning techniques coupled with data-powered intelligence enable tutor-like reasoning powers on our platform to deliver an unparalleled continuous adaptivity for every student.

Cognitive Science

Strategies and techniques to transform how knowledge is processed, retained and recalled to enable deep learning.

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Learning Science

Delivering maximum learning impact through the combined power of proven mastery, spiral and spaced techniques.

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Data Science

Innovative data modeling and data-powered intelligence to optimize each student's personalized mastery journey.

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Research Advisors

Distinguished scholars and renowned researchers from Stanford University.

Dr. Emma Brunskill

Department of Computer Science

Stanford University

Dr. Brunskill is a computer science professor at Stanford where she is part of the Stanford Artificial Intelligence Lab and the Statistical Machine Learning Group. Her research focuses on reinforcement learning in high stakes scenarios - how can an agent learn from experience to make good decisions when experience is costly or risky, such as in educational software. Her recent publications include: "The Misidentified Identifiability Problem in Bayesian Knowledge Tracing".

Dr. Karin Forsell

Department of Education

Stanford University

Dr. Forssell is the Program Director for the Learning, Design, and Technology Master's Program at the Stanford Graduate School of Education. Her research focuses on the choices people make in learning about and using new digital tools, with a special interest in teachers as learners and the design process. Her recent publications include "Making Meaningful Advances: TPACK for Designers of Learning Tools".

Dr. Rob Tibshirani

Department of Statistics

Stanford University

Dr. Tibshirani is a professor of statistics at Stanford where he leads research in data science and statistics and how they can bring scalable social change. He is passionate about combining math and computer science to solve real-world problems. His research focuses on the challenges with sorting through large amounts of data and separating out the consistent patterns from the noise. His recent publications include: "The Elements of Statistical Learning: Data Mining, Inference and Prediction".

Independently Recognized

ScootPad has been recognized as the world's leading adaptive learning platform by the non-profit education research firm EdSurge and reputed global market research firm HTF.

Sponsored by The Gates Foundation, EdSurge developed a framework to decode "adaptive learning technology" and offers a three-pronged approach to evaluate how tools adapt to learners in real-time.

ScootPad has been recognized as the leading adaptive learning platform that offers adaptive content, adaptive assessment and adaptive sequence. Of the 24 tools evaluated, ScootPad is the ONLY tool that offers adaptive learning across content domains (Math and ELA) for grades K-8 and accessible 24x7 over the cloud on any device.

ScootPad has been recognized as a leader in the rapidly growing Global Adaptive Learning market by HTF market research. Key players recognized in this research report include: D2L, Knewton, McGraw-Hill Education, ScootPad and Smart Sparrow.

HTF's research report Global Adaptive Learning Software Market 2017-2021, has been prepared based on an in-depth market analysis with inputs from industry experts. HTF's research also forecasts the global adaptive learning software market to grow at a CAGR of 31.07% during the period 2017-2021.

Cognitive Science

Strategies and techniques from proven research in cognitive science were painstakingly implemented to transform how knowledge is processed, retained and recalled to enable deep and long-lasting learning.

How ScootPad Works
Cognitive ScienceEducational PsychologyHuman Memory

Knowledge Segmentation

Reducing cognitive overload by creating bite-sized knowledge segments and allowing processing time between successive segments.

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How ScootPad Works
Cognitive ScienceHuman MemoryMultimedia

Dual Coding

Instruction designed to leverage both visual and auditory channels of human brain's processing capacity to enable deeper learning.

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How ScootPad Works
Cognitive ScienceEducational PsychologyHuman Memory

Making Learning Last

Enabling deep, long-lasting conceptual learning rather than surface-level, short-term memorization.

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How ScootPad Works
Cognitive ScienceNeuroscienceGrowth Mindset

Growth Mindset and Motivation

Fostering growth mindset and emotional behaviors to generate intrinsic motivation and self-determination.

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Learning Science

The power of mastery learning principles coupled with proven spiral and spaced learning strategies were painstakingly implemented to maximize the learning impact for every student.

How ScootPad Works
Learning ScienceCognitive Psychology

Interleaving

Exposure to related concepts is interleaved rather than blocked to enable robust encoding and better long-term recollection.

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How ScootPad Works
Learning ScienceCognitive PsychologyHuman Memory

Spacing

Concepts are revisited repeatedly over time across many sessions and recalled after a longer break to ensure knowledge retention.

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How ScootPad Works
Learning ScienceInstructional Design

Scaffolding

Identifying knowledge gaps and delivering supportive instructional scaffolds is critical and educationally consequential.

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How ScootPad Works
Learning ScienceCognitive Psychology

Retrieval Practice

Delayed subsequent retrieval practice is more potent for reinforcing retention than immediate practice.

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How ScootPad Works
Learning ScienceCognitive Psychology

Mastery Learning

a.k.a. "learning for mastery", students are required to master each concept before moving on to dependent concepts.

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How ScootPad Works
Learning ScienceInstructional Design

Personalized System of Instruction

a.k.a. "The Keller Plan", is an enhanced mastery learning strategy to ensure reinforcement throughout the learning process.

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How ScootPad Works
Learning ScienceCognitive Psychology

Advanced IRT

Dynamic and multidimensional student knowledge measurement at each bite-sized concept contingent on the performance across the interdependent web of concepts.

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How ScootPad Works
PsychometricsInstructional Design

Technology-Enhanced Items

Interactive and rigorous items enable deeper engagement, higher level of thinking and improved measures of student knowledge.

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Data Science

Here at ScootPad, we spend our days and nights thinking about how to leverage data, build new models of predictions and improve the overall adaptivity through the power of data-driven intelligence - all focused on the singular vision to maximize learning outcomes for every student at scale.

How ScootPad Works
Data ScienceGraph TheoryArtificial Intelligence

Concept Data Intelligence

Scalable knowledge modeling and deeper Knowledge DNA insights enable the platform to deliver real-time remediation and learning path optimization.

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How ScootPad Works
Data ScienceArtificial IntelligenceMachine Learning

Student Data Intelligence

The Student Learning DNA reflecting a deeper student profile enables the platform to tailor a unique learning experience and mastery journey for each student.

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How ScootPad Works
Data ScienceArtificial IntelligenceItem Analysis

Content Data Intelligence

Data-driven content modeling and actionable Content DNA insights enable us to improve content performance and effectiveness.

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How ScootPad Works
Data ScienceArtificial IntelligenceDiscrete Optimization

Adaptive Engine

Personalized mastery delivered continuously to every student with the combined power of data-driven insights and mastery-based spiral learning techniques.

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How ScootPad Works
Data ScienceArtificial IntelligenceMachine Learning

Self-Learning

Learning from the data, our engine gets more intelligent over time as more students contribute more data to the platform.

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Open Problems

ScootPad is committed to continued research and improvement of the data intelligence capabilities on the platform to further enable deeper and impactful personalized mastery for students.

Smarter Knowledge Map Nodes & Relationships

ScootPad's Knowledge Map is an interconnected web of thousands of nodes representing discrete concepts with hundreds of thousands of interconnections between them. Historically, these relationships have been manually assigned by content designers and validated/certified by the respective subject matter experts. However, given ScootPad's access to data from historical usage by students and by leveraging techniques from graph theory, it is possible to auto classify and make these relationships smarter. Such smart relationships can enable our engine to learn from these relationships and deliver an optimal learning progression for new students in future.

Identifying Student Learning Styles

Everyone learns and understands concepts in different ways. One student may prefer to directly launch into practice questions on a concept, another student may prefer to watch a video and then jump into the practice. At ScootPad, we have over 5 years of data of students interacting with content and questions on the platform and we are constantly mining this data to identify patterns of student behavior. Student Learning style identification is an area of active research at ScootPad and a logical next step to enhance our adaptive engine's abilities to learn from the data and automatically guide what a student should do next.