A Bayesian-optimal principle for learner-friendly adaptation in learning games

Is School a Better Environment Than Home for Digital Game-Based Learning? The Case of GraphoGame

“…The game is aimed at Finnish-speaking children and it consists of tasks in which the learner connects written language units to those of spoken language units, ranging from connecting single sounds to letters and then connecting longer spoken words and pseudowords to their written counterparts. The difficulty of each task is determined by the child's responses to previous tasks, according to a Bayesian-probabilitymodel-based adaptation technique developed by Kujala, Richardson, and Lyytinen (2010). The game version includes a reward system: After connecting 20 speech sounds to their written counterparts, the player receives a game token.…”

Obtaining the best value for money in adaptive sequential estimation

“…proposed in Kujala et al (2010). With the entropy loss function (1), this can be written in the convenient form…”

“…In Kujala, Richardson, and Lyytinen (2010), we extended this framework by considering situations where the observation of Y x is associated with some random cost C x . We proposed an algorithm that chooses each placement by maximizing the expected gain in utility divided by the expected cost.…”

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a b s t r a c tIn [Kujala, J. V., Richardson, U., & Lyytinen, H. (2010). A Bayesian-optimal principle for learner-friendly adaptation in learning games.

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Hidden Markov Bayesian Game with Application to Chinese Education Game

“…Dias and Ramos [13] gave a dynamic clustering of energy by an extended hidden Markov approach. Ansari et al [14] and Kujala et al [15] employed hidden Markov approaches to analyze some game situations. Since our model considers both nature states and observable signals over multiple periods, applying the HMM in a game context-although different from existing approaches-is appropriate in our setting.…”