A rational model of the Dunning-Kruger effect supports insensitivity to evidence in low performers.

Evaluating one's own performance on a task, typically known as 'self-assessment', is perceived as a fundamental skill, but people appear poorly calibrated to their abilities. Studies seem to show poorer calibration for low performers than for high performers, which could indicate worse metacognitive ability among low performers relative to others (the Dunning-Kruger effect). By developing a rational model of self-assessment, we show that such an effect could be produced by two psychological mechanisms, in either isolation or conjunction: influence of prior beliefs about ability or a relation between performance and skill at determining correctness on each problem.

Assessing Mathematics Misunderstandings via Bayesian Inverse Planning.

Online educational technologies offer opportunities for providing individualized feedback and detailed profiles of students' skills. Yet many technologies for mathematics education assess students based only on the correctness of either their final answers or responses to individual steps. In contrast, examining the choices students make for how to solve the equation and the ways in which they might answer incorrectly offers the opportunity to obtain a more nuanced perspective of their algebra skills.

Explaining Constrains Causal Learning in Childhood.

Three experiments investigate how self-generated explanation influences children's causal learning. Five-year-olds (N = 114) observed data consistent with two hypotheses and were prompted to explain or to report each observation. In Study 1, when making novel generalizations, explainers were more likely to favor the hypothesis that accounted for more observations.

Faster Teaching via POMDP Planning.

Human and automated tutors attempt to choose pedagogical activities that will maximize student learning, informed by their estimates of the student's current knowledge. There has been substantial research on tracking and modeling student learning, but significantly less attention on how to plan teaching actions and how the assumed student model impacts the resulting plans. We frame the problem of optimally selecting teaching actions using a decision-theoretic approach and show how to formulate teaching as a partially observable Markov decision process planning problem.

Inferring learners' knowledge from their actions.

Watching another person take actions to complete a goal and making inferences about that person's knowledge is a relatively natural task for people. This ability can be especially important in educational settings, where the inferences can be used for assessment, diagnosing misconceptions, and providing informative feedback. In this paper, we develop a general framework for automatically making such inferences based on observed actions; this framework is particularly relevant for inferring student knowledge in educational games and other interactive virtual environments.

Optimally designing games for behavioural research.

Computer games can be motivating and engaging experiences that facilitate learning, leading to their increasing use in education and behavioural experiments. For these applications, it is often important to make inferences about the knowledge and cognitive processes of players based on their behaviour. However, designing games that provide useful behavioural data are a difficult task that typically requires significant trial and error.

Analyzing the rate at which languages lose the influence of a common ancestor.

Analyzing the rate at which languages change can clarify whether similarities across languages are solely the result of cognitive biases or might be partially due to descent from a common ancestor. To demonstrate this approach, we use a simple model of language evolution to mathematically determine how long it should take for the distribution over languages to lose the influence of a common ancestor and converge to a form that is determined by constraints on language learning. We show that modeling language learning as Bayesian inference of n binary parameters or the ordering of n constraints results in convergence in a number of generations that is on the order of n log n.

Greater learnability is not sufficient to produce cultural universals.

Looking across human societies reveals regularities in the languages that people speak and the concepts that they use. One explanation that has been proposed for these "cultural universals" is differences in the ease with which people learn particular languages and concepts. A difference in learnability means that languages and concepts possessing a particular property are more likely to be accurately transmitted from one generation of learners to the next.