Designing an adaptive learner model for a mathematics game

The RAIDING Project (Researching Adaptivity for Individual Differences in Number Games) aims to develop a game for 7-8 year olds, to develop their times tables and number bond skills. One of the design principles of the project is to implement a level of adaptivity into the game, so that the difficulty of the mathematical content adapts to the player's current level of arithmetic fluency. A learner model has been developed to enable the game to use previous gameplay performance to calculate the player's current level of arithmetic fluency, and thereby provide new tasks at an appropriate level of difficulty. A second design principle is to decouple the mathematical difficulty from gameplay rewards, so that progress in the game is achieved through time and effort rather that solely as a result of mathematical achievement. We predict that these two design principles will produce games that are motivating and help players to experience flow. This paper describes and discusses our adaptive implementation, and our approach to decoupling of mathematical learning from rewards. We evaluate the success of the game to date and consider scope for potential development and improvement. We also show how the analytical data produced by the learner model has been used to identify unhelpful in-game behaviours and adapt the game design. A future goal of the project is to explore whether the adaptivity of the learner model can be expanded to include gameplay ability (including elements hand-eye coordination and response times) and allow for separate dynamic adjustment of (non-mathematical) difficulty. We are particularly interested in investigating the affordances of such a "two-axis" flow in the game

Use of Time Information in Models behind Adaptive System for Building Fluency in Mathematics

ABSTRACT In this work we introduce the system for adaptive practice of foundations of mathematics. Adaptivity of the system is primarily provided by selection of suitable tasks, which uses information from a domain model and a student model. The domain model does not use prerequisites but works with splitting skills to more concrete sub-skills. The student model builds on variation of Elo rating system which provide good accuracy and easy application in online system. The main feature of the student model is use of response times which can carry useful information about mastery

Teaching and Learning with Technology During the COVID-19 Pandemic: Highlighting the Need for Micro-Meso-Macro Alignments

All over the world teaching and learning transitioned to forms of online education due to the COVID-19 pandemic. In this contribution, we recognize challenges that this disruptive change brought about for teachers and learners. We reflect on these challenges, based on discussions at EDUsummIT2019 in Quebec about the theme “Learners and learning contexts: New alignments for the digital age”. Informed by theoretical conceptualization and empirical evidence we identify micro-meso-macro alignments that need to be in place to move education into the digital age: alignments for quality learning contexts, alignments in support for teachers, and alignments through partnerships

Explaining mistakes in single digit multiplication:A cognitive model

Error patterns for arithmetic problems are very rich in information, but they are hard to investigate systematically because of the small number of mistakes made. To be able to investigate errors in arithmetic we therefore used an online educational application called Math Garden, which teaches children arithmetic in the form of several different tasks. Because of the large number of users, Math Garden provides sufficient data to investigate errors systematically. Using the Math Garden data set, we developed a cognitive model in the PRIMs architecture that can give a comprehensible account of the errors made in single-digit multiplication problems. The model does a relatively good job of explaining errors on easy problems, but has difficulties explaining mistakes for harder problems. In addition to the current model, we propose some approaches to improve the model to explain mistakes in the harder problems as well.</p