Adaptation and Assessment (TwoA) asset in C# (v1.1)

Enkhbold Nyamsuren

Adaptation and Assessment (TwoA) asset in C# (v1.1)

Enkhbold Nyamsuren

Research output: Non-textual form and Research tools › Software › Academic

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Abstract

Developed 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.

Original language	English
Publication status	Published - 17 Mar 2016

Keywords

automatic game adaptation
stealth assessment
adaptation algorithm
RAGE

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C v1Accepted author manuscript, 9.08 KB

Rage: Realising an Applied Gaming Eco-system
Westera, W. (PI), Georgiadis, K. (CoI), Saveski, G. (CoI), van Lankveld, G. (CoI), Bahreini, K. (CoI), van der Vegt, W. (CoI), Berkhout, J. (CoI), Nyamsuren, E. (CoI), Kluijfhout, E. (CoI) & Nadolski, R. (CoI)
European Union
1/02/15 → 31/07/19
Project: Research

Cite this

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title = "Adaptation and Assessment (TwoA) asset in C\# (v1.1)",

abstract = "Developed 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.",

keywords = "automatic game adaptation, stealth assessment, adaptation algorithm, RAGE",

author = "Enkhbold Nyamsuren",

note = "DS\_Description: 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-2.0 DS\_Citation:Nyamsuren, E. (2016). Adaptation and Assessment (TwoA) asset in C\# (version 1.1) [Software]. Heerlen, The Netherlands: Open Universiteit. Available under the Apache License, Version 2.0 from https://github.com/rageappliedgame/HatAsset ",

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T1 - Adaptation and Assessment (TwoA) asset in C# (v1.1)

AU - Nyamsuren, Enkhbold

N1 - DS_Description: 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-2.0 DS_Citation:Nyamsuren, E. (2016). Adaptation and Assessment (TwoA) asset in C# (version 1.1) [Software]. Heerlen, The Netherlands: Open Universiteit. Available under the Apache License, Version 2.0 from https://github.com/rageappliedgame/HatAsset

PY - 2016/3/17

Y1 - 2016/3/17

N2 - Developed 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.

AB - Developed 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.

KW - automatic game adaptation

KW - stealth assessment

KW - adaptation algorithm

KW - RAGE

M3 - Software

ER -

Adaptation and Assessment (TwoA) asset in C# (v1.1)

Abstract

Keywords

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Projects

Rage: Realising an Applied Gaming Eco-system

Cite this