Game-based Configuration Task Learning with ConGuess: An Initial Empirical Analysis

Andreas Hofbauer, Alexander Felfernig*

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

Abstract

The concepts and semantics of constraint solving and configuration need to be understood in order to be able to develop one’s own configuration knowledge bases. Developing a related basic understanding is in many cases quite challenging. Consequently, further support is needed that makes the learning of configuration knowledge representation practices and semantics less effortful. In this paper, we provide a short overview of ConGuess which is a game-based learning environment for constraint-based configuration tasks. In this context, we report the results of a user study which focused on an analysis of the perceived complexity of different constraint types and on a corresponding usability analysis.

Original languageEnglish
Pages34-37
Number of pages4
Publication statusPublished - 16 Oct 2023
Event25th International Workshop on Configuration: ConfWS 2023 - E.T.S. Ingeniería Informática, Universidad de Málaga, Spain, Málaga, Spain
Duration: 6 Sept 20237 Sept 2023
https://confws.github.io
https://confws.github.io/

Workshop

Workshop25th International Workshop on Configuration
Abbreviated titleConfWS 2023
Country/TerritorySpain
CityMálaga
Period6/09/237/09/23
Internet address

Keywords

  • A. Felferni
  • Constraint Solving
  • E-Learning
  • Gamification
  • Knowledge-based Configuration

ASJC Scopus subject areas

  • General Computer Science

Fingerprint

Dive into the research topics of 'Game-based Configuration Task Learning with ConGuess: An Initial Empirical Analysis'. Together they form a unique fingerprint.

Cite this