Developing a conceptual framework to explain emergent causality: Overcoming ontological beliefs to achieve conceptual change
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Bibliographic record
Abstract
Developing a conceptual framework to explain emergent causality: Overcoming ontological beliefs to achieve conceptual change Elizabeth S. Charles* and Sylvia T. d’Apollonia** *College of Computing, Georgia Institute of Technology, 801 Atlantic Drive, Atlanta, GA 30332-0280 Tel: 404-385-4035, Fax: 404-894-5041, echarles@cc.gatech.edu ** Dawson College, 3040 Sherbrooke West, Montreal, QC, H3Z 1A7, sapollonia@education.concordia.ca explanatory frameworks for a certain class of science concept. The ontological category at the heart of this inquiry is that of emergent causal processes. It describes the behavior of phenomenon that rely on the interactions of multiple agents, all operating under the same constraints, without centralized control, influenced by flows of information with feedback loops and selection mechanisms, which generate multiple levels of organization within a system. The nonlinear and probabilistic nature of these complex systems is responsible for the seemingly magical transformations that occur between levels of the system. Put simply, emergence is characterized as the higher-level system’s behavior, which arises, but cannot be predicted, from the behavior of individual lower-level entities in the system. Abstract One approach to conceptual change suggests that ontological barriers may impose beliefs that contribute to learners’ misconceptions and misunderstanding of many science concepts. Overcoming this hurdle requires ontological training, which we argue may be possible using concepts and behaviors related to the discipline of complexity. We investigated the difficulties related to learning complex systems concepts, specifically systems exhibiting emergent causal processes. Results showed that all students acquired the following three concepts: Multiple Levels of Organization, Local Interactions, and Probabilistic Behavior. However, all but one student remained unable to develop and use a sophisticated understanding of the concepts of Nonlinearity and Randomness. This suggests that these latter concepts may be the most deeply rooted and robust of the ontologically based misconceptions. Further research is required to investigate if this tendency toward “causal determinacy” may be modified using other types of interventions. Conceptual Challenges of Emergence Although we know a lot about emergent causal processes, we continue to be challenged by why these concepts pose obstacles to learners. Duit, Roth, Komorek and Wilbers (1998), and Penner (2000), among others, have studied what students learn about complex systems when provided with different types of models. From their work we know that it is possible to learn some aspects of emergent behaviors, but these studies have not articulated the dimensions nor have they looked at the potential for transfer of this explanatory framework to achieve conceptual change. Although students may be exposed to the behaviors and functioning of complex systems in general course work (e.g., diffusion of gases), it appears that many do not understand the concepts deeply; and they do not transfer these explanations to other instances of emergence (Jacobson, 2000). In fact, Jacobson’s work shows that novice learners do not correctly attribute emergent causation to explain the behavior of complex systems whereas experts in fields such as biology and economics do so readily. Therefore we know that it is possible to use this as a generic framework as a generic to explain novel emergent phenomena. Additionally, Jacobson’s results provide evidence to support the claim that expertise in certain fields may be built on a deep understanding of this emergent ontological category. Introduction Beliefs are thought to have substantial affects on how we interact with and interpret the world. Recent studies in fields such as theories of self (Dweck, 1999) and epistemological beliefs (Hofer & Pintrich, 2002) suggest that these ways of thinking also may affect learners’ ability to perform certain tasks or construct certain types of knowledge. It is therefore reasonable to propose that ontological beliefs may play a significant role in learners’ misunderstanding of concepts whose mechanisms are unfamiliar or completely unknown. Chi, Slotta and deLeeuw (1994) put forward the argument that robust misconceptions associated with the learning of certain key science concepts 1 may be the result of assigning these concepts to incorrect ontological categories. It is possible also that lacking knowledge of a specific ontological category limits learners’ ability to construct Conceptual change difficulties reported in learning some important science concepts such as electricity in physics (Chi, Feltovich, & Glaser, 1981; White, 1993), gas laws and equilibrium in chemistry (Wilson, 1998), and in the biological sciences such concepts as diffusion, osmosis (Odom, 1995; Settlage, 1994), and evolution (Anderson & Bishop 1986; Brumby, 1984; Jacobson & Archodidou, 2000).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it