can analysis be worthwhile?

Even very young children revise the theories they construct to help them understand the world, as they gain in experience. But they do so without awareness. Inquiry learning, in contrast, involves the intentional coordination of existing theories and new evidence, a hallmark of scientific thinking (Kuhn, 2011).

Intentional coordination of theory and evidence requires that the evidence be represented in its own right, distinct from the theory, and its implications for the theory contemplated. Although older children, adolescents, and even adults continue to have trouble in this respect, young children are especially insensitive to the distinction between theory and evidence when they are asked to justify simple knowledge claims (Kuhn & Pearsall, 2000).

In corresponding studies of older children, adolescents, and adults engaged in inquiry learning, the task assigned is to analyze a multivariable database to determine which of a set of varying factors do and do not make a difference to an outcome and to explicitly justify one's claims. Although the context is simple, the goals of predicting and explaining variations in outcome by means of causal analysis parallel those of professional scientists engaged in authentic scientific inquiry. In contrast to the task described above, in which preschoolers are asked to recognize evidence to support an event claim, the task in this case is the more complex one of bringing evidence to bear on causal (and noncausal) claims of how two variables (an antecedent and an outcome) are related to one another. An example is the earthquake problem, in which students engage in self-directed investigation of a database to determine which of a set of varying factors do and do not make a difference to earthquake risk, so as to be able to accurately predict risk based on these factors. Following students' progress via the microgenetic method yields insight into their strategies of investigation, analysis, and inference and the ways in which these strategies change as students become more proficient inquiry learners.

The analysis phase of inquiry learning poses multiple challenges. The first is to recognize the relevance of the available data, to understand that it bears on the claims (regarding causal effects) that the learner believes can be made. Once the data are attended to, a second challenge is to represent them in their own right, distinct from the data. To the extent this challenge is not met, the potential for analysis remains limited. Instead, only a form of pseudo-analysis may occur, in which discrepant data are ignored and consistent data highlighted, not to support but simply to illustrate a claim that the learner takes as self-evidently true.

A further challenge, and one that is met only by sustained practice in examining evidence and bringing it to bear on one's theories, is to develop and maintain rigorous and consistent standards of analysis and inference. Comparable evidence cannot be taken to mean one thing in one context (when it is compatible with theory) and something different in another (when it is incompatible with theory). To engage in the rigorous causal analysis expected in inquiry learning, students need to have a mature mental model of causality, in which multiple factors have consistent and additive (or, in more complex systems, interactive) effects on an outcome.

The left side of the KNOWING diagram places the strategies of analysis and inference in the context of meta-level processes that regulate them. Microgenetic research shows that a student possesses a range of different analysis strategies of differing levels of effectiveness. With exercise of inquiry and analysis skills, gradual increases in the frequency of usage of more effective strategies, and equally important, decreases in the frequency of usage of less effective strategies, can be observed. Meta-level understanding is critical in determining whether newly acquired strategies will disappear when an instructional context is withdrawn and students resume meta-level management of their own behavior.

Sources for further reading:

de Jong, T., & van Joolingen, W.R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68 (2), 179-201.

Kuhn, D. (2010). What is scientific thinking and how does it develop? In U. Goswami (Ed.), Handbook of childhood cognitive development. (Blackwell).

Kuhn, D., & Pearsall, S. (2000). Developmental origins of scientific thinking. Journal of Cognition and Development, 1, 113-129.

Kuhn, D., Garcia-Mila, M., Zohar, A., & Andersen, C. (1995). Strategies of knowledge acquisition. Society for Research in Child Development Monographs, 60 (4), Serial no. 245.