TY - GEN
T1 - AutoThinking
T2 - 2nd International Conference on Innovative Technologies and Learning, ICITL 2019
AU - Hooshyar, Danial
AU - Lim, Heuiseok
AU - Pedaste, Margus
AU - Yang, Kisu
AU - Fathi, Moein
AU - Yang, Yeongwook
N1 - Funding Information:
Acknowledgments. This research was partly supported by the European Regional Development Fund through the University of Tartu project ASTRA per ASPERA.
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Computational thinking (CT) is gaining recognition as an important skill set for students, both in computer science and other disciplines. Digital computer games have proven to be attractive and engaging for fostering CT. Even though there are a number of promising studies of games that teach CT, most of these do not consider whether students are learning CT skills or adapt to individual players’ needs. Instead, they boost theoretical knowledge and promote student motivation in CT by usually following a computer-assisted instruction concept that is predefined and rigid, offering no adaptability to each student. To overcome such problems, by benefiting from a probabilistic model that deals with uncertainty, Bayesian Network (BN), we propose an adaptive CT game called AutoThinking. It seeks to engage players through personalized and fun game play while offering timely visualized hints, feedback, and tutorials which cues players to learn skills and concepts tailored to their abilities. The application of BN to AutoThinking not only adaptively provides multiple descriptions of learning materials (by offering adaptive textual, graphical, and video tutorials), similar to the natural way that teachers use in classrooms, but also creatively integrates adaptivity within gameplay by directing the cats (non-player characters) to a specific zone on the game according to players’ ability. Consequently, these adaptive features enable AutoThinking to engage players in an individually tailored gameplay and instill CT concepts and skills.
AB - Computational thinking (CT) is gaining recognition as an important skill set for students, both in computer science and other disciplines. Digital computer games have proven to be attractive and engaging for fostering CT. Even though there are a number of promising studies of games that teach CT, most of these do not consider whether students are learning CT skills or adapt to individual players’ needs. Instead, they boost theoretical knowledge and promote student motivation in CT by usually following a computer-assisted instruction concept that is predefined and rigid, offering no adaptability to each student. To overcome such problems, by benefiting from a probabilistic model that deals with uncertainty, Bayesian Network (BN), we propose an adaptive CT game called AutoThinking. It seeks to engage players through personalized and fun game play while offering timely visualized hints, feedback, and tutorials which cues players to learn skills and concepts tailored to their abilities. The application of BN to AutoThinking not only adaptively provides multiple descriptions of learning materials (by offering adaptive textual, graphical, and video tutorials), similar to the natural way that teachers use in classrooms, but also creatively integrates adaptivity within gameplay by directing the cats (non-player characters) to a specific zone on the game according to players’ ability. Consequently, these adaptive features enable AutoThinking to engage players in an individually tailored gameplay and instill CT concepts and skills.
KW - Adaptive educational game
KW - Adaptive tutorials
KW - Bayesian network
KW - Computational thinking
KW - Timely visualized feedback
UR - http://www.scopus.com/inward/record.url?scp=85076791834&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-35343-8_41
DO - 10.1007/978-3-030-35343-8_41
M3 - Conference contribution
AN - SCOPUS:85076791834
SN - 9783030353421
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 381
EP - 391
BT - Innovative Technologies and Learning - 2nd International Conference, ICITL 2019, Proceedings
A2 - Rønningsbakk, Lisbet
A2 - Wu, Ting-Ting
A2 - Sandnes, Frode Eika
A2 - Huang, Yueh-Min
PB - Springer
Y2 - 2 December 2019 through 5 December 2019
ER -