Emergent behaviors in computer-based learning environments: Computational signals of catching up

Erica L. Snow, G. Tanner Jackson, Danielle McNamara

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)62-70
Number of pages9
JournalComputers in Human Behavior
Volume41
DOIs
StatePublished - 2014

Fingerprint

Learning
Aptitude
Students
Individuality
Reading
Entropy
Intelligent systems
Information Systems
Computational
Learning Environment
Interaction
Individual Differences
Controlled
Reading Ability
Skill Acquisition
Posttests
Tutoring
Methodology
High School Students
Fluctuations

Keywords

  • Agency
  • Dynamic analyses
  • Individual differences
  • Intelligent tutoring systems
  • Log data
  • Self-regulated learning

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Psychology(all)
  • Arts and Humanities (miscellaneous)

Cite this

Emergent behaviors in computer-based learning environments : Computational signals of catching up. / Snow, Erica L.; Jackson, G. Tanner; McNamara, Danielle.

In: Computers in Human Behavior, Vol. 41, 2014, p. 62-70.

Research output: Contribution to journalArticle

@article{6bc55dc024444040960d088a71abedda,
title = "Emergent behaviors in computer-based learning environments: Computational signals of catching up",
abstract = "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.",
keywords = "Agency, Dynamic analyses, Individual differences, Intelligent tutoring systems, Log data, Self-regulated learning",
author = "Snow, {Erica L.} and Jackson, {G. Tanner} and Danielle McNamara",
year = "2014",
doi = "10.1016/j.chb.2014.09.011",
language = "English (US)",
volume = "41",
pages = "62--70",
journal = "Computers in Human Behavior",
issn = "0747-5632",
publisher = "Elsevier Limited",

}

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

PY - 2014

Y1 - 2014

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

UR - http://www.scopus.com/inward/record.url?scp=84907797333&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84907797333&partnerID=8YFLogxK

U2 - 10.1016/j.chb.2014.09.011

DO - 10.1016/j.chb.2014.09.011

M3 - Article

AN - SCOPUS:84907797333

VL - 41

SP - 62

EP - 70

JO - Computers in Human Behavior

JF - Computers in Human Behavior

SN - 0747-5632

ER -