Data2Game: Towards an Integrated Demonstrator

The Data2Game project investigates how the efficacy of computerized training games can be enhanced by tailoring training scenarios to the individual player. The research is centered around three research innovations: (1) techniques for the automated modelling of players’ affective states, based on exhibited social signals, (2) techniques for the automated generation of in-game narratives tailored to the learning needs of the player, and (3) validated studies on the relation of the player behavior and game properties to learning performance. This paper describes the integration of the main results into a joint prototype.

[1]  Frank Goldhammer,et al.  Evaluation of Online Information in University Students: Development and Scaling of the Screening Instrument EVON , 2020, Frontiers in Psychology.

[2]  Begoña García Zapirain,et al.  A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee , 2012, Sensors.

[3]  Walter Daelemans,et al.  Pattern for Python , 2012, J. Mach. Learn. Res..

[4]  Maren Scheffel,et al.  Awareness Is Not Enough: Pitfalls of Learning Analytics Dashboards in the Educational Practice , 2017, EC-TEL.

[5]  Ecnica De Lisboa,et al.  MULTI-MODAL BEHAVIORAL BIOMETRICS BASED ON HCI AND ELECTROPHYSIOLOGY , 2008 .

[6]  Robert Dale,et al.  Building applied natural language generation systems , 1997, Natural Language Engineering.

[7]  Stephen Jacobs,et al.  Game writing : Narrative skills for videogames , 2007 .

[8]  Herre van Oostendorp,et al.  A meta-analytic review of the role of instructional support in game-based learning , 2013, Comput. Educ..

[9]  Roger G. Mark,et al.  An open-source algorithm to detect onset of arterial blood pressure pulses , 2003, Computers in Cardiology, 2003.

[10]  Benjamin Van Durme,et al.  Annotated Gigaword , 2012, AKBC-WEKEX@NAACL-HLT.

[11]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[12]  Thomas de Groot,et al.  Devilish Dilemma Games: Narrative Game Engine and Accompanying Authoring Tool , 2019, AHFE.

[13]  Geert-Jan Houben,et al.  Follow the successful crowd: raising MOOC completion rates through social comparison at scale , 2017, LAK.

[14]  Wiebke Wagner,et al.  Steven Bird, Ewan Klein and Edward Loper: Natural Language Processing with Python, Analyzing Text with the Natural Language Toolkit , 2010, Lang. Resour. Evaluation.

[15]  Ilya Sutskever,et al.  Language Models are Unsupervised Multitask Learners , 2019 .

[16]  Roger A. Light.  Mosquitto: server and client implementation of the MQTT protocol , 2017, J. Open Source Softw..

[17]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[18]  Eric Buiel,et al.  Serious gaming for complex decision making: training approaches , 2014 .

[19]  Bernard P. Veldkamp,et al.  Information Literacy Skills Assessment in Digital Crisis Management Training for the Safety Domain: Developing an Unobtrusive Method , 2020, Frontiers in Education.

[20]  Mariët Theune,et al.  Churnalist: Fictional Headline Generation for Context-appropriate Flavor Text , 2019, ICCC.

[21]  Cleotilde Gonzalez,et al.  Instance-based learning in dynamic decision making , 2003 .

[22]  Cleotilde Gonzalez,et al.  Instance-based learning in dynamic decision making , 2003, Cogn. Sci..