A Solution to the Measurement Problem in the Idiographic Approach Using Computer Adaptive Practicing

Abstract

Molenaar's manifesto on psychology as idiographic science (Molenaar, 2004) brought the N = 1 times series perspective firmly to the attention of developmental scientists. The rich intraindividual variation in complex developmental processes requires the study of these processes at the level of the individual. Yet, the idiographic approach is all but easy in practical research. One major limitation is the collection of short interval times series of high quality data on developmental processes. In this paper, we present a novel measurement approach to this problem. We developed an online practice and monitoring system which is now used by thousands of Dutch primary school children on a daily or weekly basis, providing a new window on cognitive development. We will introduce the origin of this new instrument, called Math Garden, explain its setup, and present and discuss ways to analyze children's individual developmental pathways.

Artificial intelligence moving serious gaming: Presenting reusable game AI components

“…This means that the learning process becomes highly efficient: progression is optimised, while no time is wasted on tasks that do not contribute to learning. Continued assessment of the player's skill serves as a form of formative learning analytics that can be used by the player or the teacher to monitor learning progress and identify potential learning barriers (Hofman et al 2018). Finally, game difficulty assessment also enables the analysis and optimisation of the game's learning content (Nyamsuren et al 2018a).…”

Tracking with (Un)Certainty

“…To conclude, since CAL systems are becoming more widely adopted in education, the inferences made about individual children based on the reliable learning analytics provided by these systems should be a crucial part of designing the system (Brinkhuis et al 2018;Hofman et al 2018c). Based on these inferences tailored instructions could be provided, either automated within the system or by teachers in a classroom setting.…”

Governing games Adaptive game selection in the Math Garden

“…Game recommendations in educational games with many domains to choose from are clearly beneficial, especially regarding the relations between domains [12], which can be used to suggest what to practice. We define governing [13] as computerized, data driven supervision in an educational practice system, with three specific goals: 1) an individualized domain selection mechanism, 2) guide students in selecting best domains to practice and 3) increase most important abilities.…”

“…An expert strategy keeps learning, for example predicting student domain knowledge based on knowledge in other domains [12,17], such as a correlation matrix of all domains. Predictions based on such a model can be used for domain selection to maximize learning gain.…”