196 research outputs found

    Adaptation and Assessment (TwoA) asset in TypeScript (v1.0)

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    Adaptation and Assessment (TwoA) asset in TypeScript (v1.2)

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    Adaptation and Assessment (TwoA) asset in C# (v1.2)

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    RAGE products are licensed under the Apache License, Version 2.0 (the "License"). See the License for the specific language governing permissions and limitations under the License: http://www.apache.org/licenses/LICENSE-2.0Developed within the RAGE project funded by EU within Horizon2020 program. This asset enables a real-time automatic adaptation of game difficulty to player's expertise level. The adaptation algorithm makes use of a stealth assessment algorithm that assigns difficulty ratings and expertise ratings to the players and the game modules respectively. The asset tracks changes in these ratings allowing assessment of players' learning progress either by players themselves or by instructors. This is the version written in C# language.This study is part of the RAGE project. The RAGE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains

    A functionality prototype for the TwoA asset

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    Adaptation and Assessment (TwoA) asset in TypeScript (v1.0)

    Get PDF
    RAGE products are licensed under the Apache License, Version 2.0 (the "License"). See the License for the specific language governing permissions and limitations under the License: http://www.apache.org/licenses/LICENSE-2.0Developed within the RAGE project funded by EU within Horizon2020 program. This asset enables a real-time automatic adaptation of game difficulty to player's expertise level. The adaptation algorithm makes use of a stealth assessment algorithm that assigns difficulty ratings and expertise ratings to the players and the game modules respectively. The asset tracks changes in these ratings allowing assessment of players' learning progress either by players themselves or by instructors. This is the version written in TypeScript language.This study is part of the RAGE project. The RAGE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains
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