Measuring the Dynamics of Artificial Evolution

Burtsev, Mikhail

doi:10.1007/978-3-540-39432-7_62

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…In other words, a problem-solving discussion can be conceptualized as a temporal sequence of phases. One can use several methods to isolate temporal phases, including measures of genetic entropy (Adami et al 2000), intensity of mutation rates (Burtsev 2003) or, in the case of problem interactions, the classification of coherent phases of interaction. Whether these phases involve genetic mutations or stable interactions, sequences of fluctuations often alternate between stable phases, with chaotic phases interspersed throughout.…”

Section: Future Directionsmentioning

confidence: 99%

Temporality matters: Advancing a method for analyzing problem-solving processes in a computer-supported collaborative environment

Kapur

2011

Computer Supported Learning

103

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This paper argues for a need to develop methods for examining temporal patterns in computer-supported collaborative learning (CSCL) groups. It advances one such quantitative method-Lag-sequential Analysis (LsA)-and instantiates it in a study of problem-solving interactions of collaborative groups in an online, synchronous environment. LsA revealed significant temporal patterns in CSCL group discussions that the commonly used "coding and counting" method could not reveal. More importantly, analysis demonstrated how variation in temporal patterns was significantly related to variation in group performance, thereby bolstering the case for developing and testing temporal methods and measures in CSCL research. Findings are discussed, including issues of reliability, validity, and limitations of the proposed method.Keywords Temporal methods . Lag-sequential analysis . Event-based process analysis . Temporality . Collaborative learningIntroduction I advance a quantitative method for characterizing and analyzing the temporal patterns in computer-supported, collaborative learning (CSCL) and problem solving. This paper comes in response to recent calls for a greater focus on temporality; underpinning this work is the belief that learning in general, and problem solving in particular, is a continuous, dynamic process that evolves over time. In a seminal paper on temporality published in ijCSCL, Reimann (2009) argued that, "Temporality does not only come into play in quantitative terms (e.g., durations, rates of change), but order matters: Because human learning is inherently cumulative, the sequence in which experiences are encountered affects how one learns and what one learns" (p. 1). Understanding how such processes evolve in time and how variation in this evolution explains learning outcomes ranks among the most important challenges facing educational research-and temporal methods that expand the methodological toolkit are needed (Akhras and Self 2000;McGrath and Tschan 2004;Suthers et al. 2007).It is not surprising therefore that there has been a push in CSCL research towards unpacking temporal patterns in group interactions and understanding how these patterns relate to group and individual performance and learning (Stahl 2005;Suthers 2006). Reimann (2009) argued "although CSCL researchers are privileged in the sense that they have direct access to processes as they unfold over time (via recordings), there is comparatively little research that makes use of the information contained in the order and duration of events" (p. 1). This presents a unique challenge to traditional analytical measures and methods for analyzing group processes because, for the most part, existing methods continue to take cumulative accounts of member interactions (e.g., categorization of interactional content, rating of discussion quality, member perceptions, and so on) and relate them to group performance. While these accounts are certainly useful, they fail to fully utilize the temporal information embedded in the data. A failure to utilize t...

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Section: Future Directionsmentioning

confidence: 99%

Temporality matters: Advancing a method for analyzing problem-solving processes in a computer-supported collaborative environment

Kapur

2011

Computer Supported Learning

103

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“…The concept of interdependence has a long, rich history in psychology (Thibaut & Kelley, 1959). It recently has been incorporated into an evolutionary framework via the concept of fitness interdependence (Brown, 1999; Burtsev, 2003; Durham, 1976; Hoekstra et al, 1991; Petersen et al, 2010; Roberts & Sherratt, 2007; Schlosser & Wagner, 2008; Tooby & Cosmides, 2010), or “the degree to which two or more organisms influence each other’s success in replicating their genes” (Aktipis et al, 2018). Fitness interdependence can be negative—when organisms compete for the same finite resources—or positive—when organisms mutually depend on each other for survival and reproduction.…”

Section: Previous Research On Shared Fatementioning

confidence: 99%

Fitness interdependence as indexed by shared fate: Factor structure and validity of a new measure.

Ayers¹,

Sznycer²,

Sullivan³

et al. 2023

Evolutionary Behavioral Sciences

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Assessing interdependence between the self and specific others has a rich history in psychological science. Here, we report a novel scale that measures Shared Fate. Studies 1 and 2 (N 1 = 198, N 2 = 216) show that the Shared Fate Scale has two factors assessing perceived shared fate and emotional shared fate with a target, has good reliability (Cronbach's a = .81 to .91; McDonald's v = .89 to .96), and predicts participants' willingness to help interdependent others. Studies 3 and 4 (N 3 = 695, N 4 = 629) indicate that the Shared Fate Scale has good discriminant, convergent, concurrent, and predictive validity as well as test-retest reliability across a 14-day period. Taken together, our results suggest that the Shared Fate Scale is a useful instrument for work on cooperation, interdependence, and social behavior. However, there are still many open questions about the cognitive architecture underlying perceptions of shared fate and how shared fate interacts with genetic relatedness and other sources of fitness interdependence.

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