Using sequence mining to reveal the efficiency in scientific reasoning during STEM learning with a game-based learning environment. (April 2018)

• Record Type: Journal Article
• Authors: Taub, Michelle; Azevedo, Roger; Bradbury, Amanda E.; Millar, Garrett C.; Lester, James
• Is Part Of: Learning and instruction. Volume 54(2018)
• Page Start: 93
• Page End: 103
• Publication Date: 2018-04
• Subjects: Metacognition -- Self-regulated learning -- Scientific reasoning -- Game-based learning -- Sequence mining -- Process data -- Log files; Learning -- Periodicals; Teaching -- Periodicals; Apprentissage -- Périodiques; Enseignement -- Périodiques; Learning; Teaching; Periodicals; Electronic journals; 370.1
• DOI: 10.1016/j.learninstruc.2017.08.005
• Languages: English
• ISSNs: 0959-4752

Abstract

Abstract: The goal of this study was to assess how metacognitive monitoring and scientific reasoning impacted the efficiency of game completion during learning withCrystal Island, a game-based learning environment that fosters self-regulated learning and scientific reasoning by having participants solve the mystery of what illness impacted inhabitants of the island. We conducted sequential pattern mining and differential sequence mining on 64 undergraduate participants' hypothesis testing behavior. Patterns were coded based on the relevancy of what items were being tested for, and the items themselves. Results revealed that participants who were more efficient at solving the mystery tested significantly fewer partially-relevant and irrelevant items than less efficient participants. Additionally, more efficient participants had fewer sequences of testing items overall, and significantly lower instance support values of thePartiallyRelevant --Relevant toRelevant --Relevant andPartiallyRelevant --PartiallyRelevant toRelevant--Partially Relevant sequences compared to less efficient participants. These findings have implications for designing adaptive GBLEs that scaffold participants based on in-game behaviors. Highlights: Use of sequence mining to examine self-regulated learning with a game-based learning environment. Examined hypothesis testing behavior based on the efficiency at solving the game. More efficient students tested fewer partially-relevant and irrelevant food items. More efficient students had fewer sequences with fewer partially-relevant items.