The majority of conceptual change studies have investigated either manipulations of the learning environment or examinedthe effect of individual differences on conceptual change (Cordova, et al. 2014; Taasoobshirazi & Sinatra, 2011). In eithercase, the importance of interactions is left out. The present study investigates whether individual differences in hot andcold cognitive ability and task scaffolding interact with each other in their effect on conceptual change. Participants(n = 299) were tasked with determining how best to categorize whether a fictitious bacteria is oxygen resistant acrossthree learning conditions. The results suggest that a refutational text produces better learning gains than an expositorytext, which outperforms feedback alone. Moreover, hot and cold cognitive factors were found to interact with learnerscaffolding differentially. The results of this research project can be used to improve instructional practices, which, inturn, should aid learners understanding of scientific conceptions.

Researchers argue that dissatisfaction with a misconception is a prerequisite for adopting an alternative conception and that having clear feedback aids learning. The present study investigated the importance of ambiguity (having response options that support both the misconception and target learning category), falsification, and category induction opportunities when overriding a prior conception in favor of a new conception. The results suggest that ambiguity and direct falsification opportunities may aid in learning more than having both direct falsification and induction opportunities, which may be better than ambiguity and providing induction opportunities without direct falsification. Ambiguity may improve learning when coupled with falsification opportunities. Implications are discussed.

Many conceptual change theories posit that change occurs due to a variety of cognitive, social, and emotional factors (Dole & Sinatra, 1998; Ohlsson, 2011), however, few theories have tested these claims via computational models of conceptual change. In this paper, we present a hierarchical Bayesian model that addresses change processes and their effects on re-categorization, a form of concept change. Human data from a study using the re-categorization paradigm (Ramsburg & Ohlsson, 2013) are compared to the computational model. The structure of the human data suggests the ‘non-monotonic’ nature of conceptual change (Ohlsson, 2011) as indicated by the best-fit learning curves. For several such curves, model comparisons suggest good fits between the computational simulations and human data. The nonlinear form of the model’s update functions lends additional support to concept change as a non-monotonic process. The model is discussed as a “proof of concept” for future conceptual change modeling endeavors.