TY - JOUR
T1 - Emergent behaviors in computer-based learning environments
T2 - Computational signals of catching up
AU - Snow, Erica L.
AU - Jackson, G. Tanner
AU - McNamara, Danielle
N1 - Funding Information:
This research was supported in part by the Institute for Educational Sciences ( IES R305G020018-02 ; R305G040046 , R305A080589 ) and National Science Foundation ( NSF REC0241144 ; IIS-0735682 ). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the IES or NSF. A special thank you to Sidney D’Mello for his software assistance. We would also like to thank Laura Allen for her helpful comments on this manuscript, Chutima Boonthum for her help with programming, and all the other members of the Solet Lab for their assistance with data collection.
Publisher Copyright:
© 2014 Elsevier Ltd. All rights reserved.
PY - 2014/12
Y1 - 2014/12
N2 - Self-regulative behaviors are dynamic and evolve as a function of time and context. However, dynamical fluctuations in behaviors are often difficult to measure and therefore may not be fully captured by traditional measures alone. Utilizing system log data and two novel statistical methodologies, this study examined emergent patterns of controlled and regulated behaviors and assessed how variations in these patterns related to individual differences in prior literacy ability and target skill acquisition. Conditional probabilities and Entropy analyses were used to examine nuanced patterns manifested in students' interaction choices within a computer-based learning environment. Forty high school students interacted with the game-based intelligent tutoring system iSTART-ME, for a total of 11 sessions (pretest, 8 training sessions, posttest, and a delayed retention test). Results revealed that high and low reading ability students differed in their patterns of interactions and the amount of control they exhibited within the game-based system. However, these differences converged overtime along with differences in students' performance within iSTART-ME. The findings from this study indicate that individual differences in students' prior reading ability relate to the emergence of controlled and regulated behaviors during learning tasks.
AB - Self-regulative behaviors are dynamic and evolve as a function of time and context. However, dynamical fluctuations in behaviors are often difficult to measure and therefore may not be fully captured by traditional measures alone. Utilizing system log data and two novel statistical methodologies, this study examined emergent patterns of controlled and regulated behaviors and assessed how variations in these patterns related to individual differences in prior literacy ability and target skill acquisition. Conditional probabilities and Entropy analyses were used to examine nuanced patterns manifested in students' interaction choices within a computer-based learning environment. Forty high school students interacted with the game-based intelligent tutoring system iSTART-ME, for a total of 11 sessions (pretest, 8 training sessions, posttest, and a delayed retention test). Results revealed that high and low reading ability students differed in their patterns of interactions and the amount of control they exhibited within the game-based system. However, these differences converged overtime along with differences in students' performance within iSTART-ME. The findings from this study indicate that individual differences in students' prior reading ability relate to the emergence of controlled and regulated behaviors during learning tasks.
KW - Agency
KW - Dynamic analyses
KW - Individual differences
KW - Intelligent tutoring systems
KW - Log data
KW - Self-regulated learning
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U2 - 10.1016/j.chb.2014.09.011
DO - 10.1016/j.chb.2014.09.011
M3 - Article
AN - SCOPUS:84907797333
SN - 0747-5632
VL - 41
SP - 62
EP - 70
JO - Computers in Human Behavior
JF - Computers in Human Behavior
ER -