Automated Learning Content Generation from Knowledge Bases in the STUDYBATTLES Environment

Autor: Alexander Felfernig, Amal Shehadeh, Michael Jeran, Martin Stettinger, Stefan Reiterer
Rok vydání: 2017
Předmět:
Zdroj: International Journal of Software Engineering and Knowledge Engineering. 27:1387-1408
ISSN: 1793-6403
0218-1940
DOI: 10.1142/s0218194017400022
Popis: E-learning environments provide an orthogonal approach to transfer relevant knowledge. For example, sales representatives can improve their sales knowledge more independently from related courses offered. Major challenges for successfully establishing e-learning technologies in a company are to develop learning content in an efficient fashion, to recommend only relevant content to system users, and to motivate them to utilize the learning environment in a sustainable fashion. In this paper, we present the gamification-based e-learning environment STUDYBATTLES. We provide an overview of STUDYBATTLES functionalities including content creation, gamification techniques, learning performance analysis, and automated question generation. We show how STUDYBATTLES can be utilized for different learning purposes in academic and professional environments. In addition, we introduce an approach to automatically generate product and sales domain learning content from recommender knowledge bases to be exploited in STUDYBATTLES. Finally, we report the results of an initial qualitative study related to the applicability of STUDYBATTLES in different domains, the potential improvements that StudyBattles can achieve, and additional functionalities that should be integrated.
Databáze: OpenAIRE