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