TY - GEN
T1 - Gamed-based iSTART practice
T2 - 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
AU - Brunelle, Justin F.
AU - Jackson, G. Tanner
AU - Dempsey, Kyle
AU - Boonthum, Chutima
AU - Levinstein, Irwin B.
AU - McNamara, Danielle S.
PY - 2010
Y1 - 2010
N2 - MiBoard (Multiplayer Interactive Board Game) is an online, turnbased board game that was developed to assess the integration of game characteristics Opoint rewards, game-like interaction, and peer feedback) and how that might affect student engagement and learning efficacy. This online board game was designed to fit within the Extended Practice module of iSTART (Interactive Strategy Training for Active Reading and Thinking). Unfortunately, preliminary research shows that MiBoard actually reduces engagement and does not benefit the quality of student self-explanations when compared to the original Extended Practice module. Consequently the MiBoard framework has been revamped to create Self-Explanation Showdown, a faster-paced, less analytically oriented game that adds competition to the creation of self-explanations. Students are evaluated on the quality of their self-explanations using the same assessment algorithms from iSTART Extended Practice module (this includes both word-based and LSA-based assessments). The technical issues involved in development of MiBoard and SelfExplanation Showdown are described. The lessons learned from the MiBoard experience are also discussed in this paper.
AB - MiBoard (Multiplayer Interactive Board Game) is an online, turnbased board game that was developed to assess the integration of game characteristics Opoint rewards, game-like interaction, and peer feedback) and how that might affect student engagement and learning efficacy. This online board game was designed to fit within the Extended Practice module of iSTART (Interactive Strategy Training for Active Reading and Thinking). Unfortunately, preliminary research shows that MiBoard actually reduces engagement and does not benefit the quality of student self-explanations when compared to the original Extended Practice module. Consequently the MiBoard framework has been revamped to create Self-Explanation Showdown, a faster-paced, less analytically oriented game that adds competition to the creation of self-explanations. Students are evaluated on the quality of their self-explanations using the same assessment algorithms from iSTART Extended Practice module (this includes both word-based and LSA-based assessments). The technical issues involved in development of MiBoard and SelfExplanation Showdown are described. The lessons learned from the MiBoard experience are also discussed in this paper.
UR - http://www.scopus.com/inward/record.url?scp=77957870792&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77957870792&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:77957870792
SN - 9781577354475
T3 - Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
SP - 480
EP - 485
BT - Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
Y2 - 19 May 2010 through 21 May 2010
ER -