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

Enkhbold Nyamsuren

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

Enkhbold Nyamsuren

Research output: Non-textual form › Software › Academic

33 Downloads (Pure)

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 TypeScript language.

Original language	English
Place of Publication	Heerlen
Publisher	Open Universiteit Nederland
Publication status	Published - 23 Sept 2016

Keywords

automatic game adaptation
stealth assessment
adaptation algorithm
RAGE

Access to Document

TypeScriptv1Accepted author manuscript, 8.9 KB

https://github.com/rageappliedgame/HATAsset_TSLicence: Other

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

@misc{727aa40f1d1741779d894900767d3b72,

title = "Adaptation and Assessment (TwoA) asset in TypeScript (v1.0)",

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 TypeScript language.",

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

author = "Enkhbold Nyamsuren",

year = "2016",

month = sep,

day = "23",

language = "English",

publisher = "Open Universiteit Nederland",

}

TY - ADVS

T1 - Adaptation and Assessment (TwoA) asset in TypeScript (v1.0)

AU - Nyamsuren, Enkhbold

PY - 2016/9/23

Y1 - 2016/9/23

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 TypeScript 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 TypeScript language.

KW - automatic game adaptation

KW - stealth assessment

KW - adaptation algorithm

KW - RAGE

M3 - Software

PB - Open Universiteit Nederland

CY - Heerlen

ER -

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

Abstract

Keywords

Access to Document

Fingerprint

Projects

Rage: Realising an Applied Gaming Eco-system

Cite this