Dynamic systems view of learning a three‐tiered theory in physics: robust learning outcomes as attractors

Abstract

The process of learning scientific knowledge from the dynamic systems viewpoint is studied in terms probabilistic learning model (PLM), where learning accrues from foraging in the epistemic landscape. The PLM leads to the formation of attractor‐type regions of preferred models in an epistemic landscape. The attractor‐type states correspond to robust learning outcomes which are more probable than others. These can be assigned either to the high confidence in model selection or to the dynamic evolution of a learner's proficiency, which depends on the learning history. The results suggest that robust learning states are essentially context dependent, and that learning is a continuous development between these context dependent states. © 2016 Wiley Periodicals, Inc. Complexity 21: 259–267, 2016

Complex Dynamic Systems View on Conceptual Change: How a Picture of Students’ Intuitive Conceptions Accrue From Dynamically Robust Task Dependent Learning Outcomes

“…The epistemic landscape constitutes of rational utilities of the targeted explanatory models, where models form a hierarchical system. The utility of an explanatory model can be regarded as a balance between the theoretical complexity of the model and evidence that it explains (Koponen & Kokkonen 2014, Koponen et al 2016. In simple situations (with little evidence) simple models with limited explanatory power are favoured, but in complex situations (with a broader range and possibly structured evidence) complex models with high explanatory power gain utility.…”

“…In this case, foraging proceeds in the direction of the steepest growth of utility, with increased utility thus encouraging the foraging to continue in the direction of growing utility. When a learner applies a given model successfully, it increases the proficiency (learning has taken place, increasing the learner's future chances for success), but if the explanation requires decreased utility, this reduces the proficiency (Koponen et al 2016).…”

“…The selection of explanatory model is assumed to be fully probabilistic but based on comparison of the rational utility of a given model k in comparison to utilities of all the other models j. Then the selection of a given model k from the N models can be viewed as a probabilistic decision -type problem, in which case the decision to select a given model k is given as the probability (Yukalov & Sornette 2014;Koponen et al 2016…”

“…These outcomes of three possible cases defined above are determined in simulations on a stochastic basis, as an event based Monte Carlo simulation method, where probability to realize the event is proportional to its probability of occurrence. The further details of the technical implementation of probabilistic event based simulations is explained in detail elsewhere (Koponen et al, 2016).…”

“…The recently suggested dynamic perspective (Brown 2014;Gupta et al 2010;Hammer et al 2005;Koponen et al 2014Koponen et al , 2016 on students' intuitive conceptions and conceptual change attempts to provide a new picture based on complex dynamical systems thinking. This picture emphasises the nature of students' conceptions as multifaceted structures which depend on the context of their application.…”