Dedicated to Scientific Research

Our organization is dedicated to Scientific Research as a way to drive innovation and outcomes. We create technologies and digital experiences that are rooted in evidence and centered around human needs.

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Overview

Learning Science

Motivation

Online Experimentation

Engagement

Educational Games

Our team has spent over a decade in diving into the science of learning. Through a technology lens and a learning engineering perspective, we have conducted groundbreaking studies to show the potential of digital experimentation and evidence-based improvement of edtech products.

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Educational Data Science

Process Mining

Learning Analytics

AI in Education

Psychometrics

Playpower's educational data science team has worked with data from millions of learners across the globe, striving to understand what makes learning work. We have received multiple awards for designing novel ways of visualizing educational data.

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Design & Human-
Computer Interaction

Product Design

Game Design

Smart Systems

Interaction Design

With broad experience in design and human computer interaction, our team has collaborated with leading researchers across the world to design technology that has been loved by many happy users.

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Cognitive Science

Cognitive Assessments

EEG Data

Human Wellbeing

Aesthetics

Researchers at Playpower have gone into questions of the cognition in order to discover how to design human-centered digital experiences that foster growth and augment human capacity. We work with cognition and neural data to uncover what workings of the mind's intelligence.

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Design Factors for Motivation: Difficulty, Novelty, and Suspense

We conduct online experiments testing how different factors affect player motivation. Engaging game design is thought to come from being “not too hard, not too easy.” Our evidence suggests the maxim should be “not too hard, not too boring.”

Experimentation for Optimizing Learning and Motivation

We show that product experiments involving thousands of experimental conditions and tens of thousands of online learners can be used to optimize learning outcomes and to contribute to learning scientific research.

Machine Learning for Product Adaptivity

We use machine learning algorithms to automate product design experiments, resulting in optimized student outcomes at scale. Product experiments are a key enabling technology for continuous improvement, efficacy measurement, and personalized, adaptive learning.

Psychometric Validation of Neurological Assessment Games

We develop a game to measure “Selective Sustained Attention” in small children. We then demonstrate appropriate methods for validating the psychometric properties of the game.

Building Effective Games for Number Sense

We design a game for the assessment and instruction of “number sense.” We base the design of the game on the neuroscience of the approximate number system. We demonstrate various aspects of the efficacy of the game for learning and assessment.

Simple Interactions for Social Learning

We explore how social interactions in low-income households in India help overcome the limits of low-cost digital technology.

Radically Affordable Learning Hardware

We provide ethnographic depictions of the incorporation of $10 educational computers into low-income households in India. We show how various design factors might support the longevity of contemporary technology systems.

Assessment Data Forensics

We help education organizations maintain high integrity in their exams. We use data analytics and computer vision-based methods to analyze assessment data from both digital and paper systems.

Process Analysis for Educational Data

We invent new methods to understand the processes of education technology interaction. We build process models from student and teacher data to find actionable places for product improvement.

Design Factors for Motivation: Difficulty, Novelty, and Suspense

We conduct online experiments testing how different factors affect player motivation. Engaging game design is thought to come from being “not too hard, not too easy.” Our evidence suggests the maxim should be “not too hard, not too boring.”

Experimentation for Optimizing Learning and Motivation

We show that product experiments involving thousands of experimental conditions and tens of thousands of online learners can be used to optimize learning outcomes and to contribute to learning scientific research.

Machine Learning for Product Adaptivity

We use machine learning algorithms to automate product design experiments, resulting in optimized student outcomes at scale. Product experiments are a key enabling technology for continuous improvement, efficacy measurement, and personalized, adaptive learning.

Psychometric Validation of Neurological Assessment Games

We develop a game to measure “Selective Sustained Attention” in small children. We then demonstrate appropriate methods for validating the psychometric properties of the game.

Building Effective Games for Number Sense

We design a game for the assessment and instruction of “number sense.” We base the design of the game on the neuroscience of the approximate number system. We demonstrate various aspects of the efficacy of the game for learning and assessment.

Simple Interactions for Social Learning

We explore how social interactions in low-income households in India help overcome the limits of low-cost digital technology.

Dedicated to Scientific Research

Learning Science

Educational Data Science

Design & Human-
Computer Interaction

Cognitive Science

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Company

Dedicated to Scientific Research

Learning Science

Educational Data Science

Design & Human- Computer Interaction

Cognitive Science

Design & Human-
Computer Interaction