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If an organic chemistry student explains that she represents a mechanistic step because ''it's a productive part of the mechanism,'' what meaning could the professor teaching the class attribute to this statement, what is actually communicated, and what does it mean for the student? The professor might think that the explanation is based on knowledge of equilibria of alternative steps. The professor might also assume that the student implies information about how one of the alternatives influences the energetics of subsequent steps or how subsequent steps influence the equilibria of the alternatives. Meanwhile, the student might literally mean that the step is represented simply because it leads to the product. Reasoning about energetic influences has much greater explanatory power than teleological reasoning taking the consequence of mechanistic steps as the reason for their prediction. In both cases, however, the same backward-oriented reasoning is applied. Information about subsequent parts in the mechanism is used to make a decision about prior parts. To qualitatively compare the reasoning patterns and the causality employed by students and expected by their professor, we used a mechanistic approach from philosophy of science that mirrors the directionality of a mechanism and its components: activities, entities, and their properties. Our analysis led to the identification of different reasoning patterns involving backward-oriented reasoning. Participants' use of properties gave additional insight into the students' reasoning and their professor's expectations, which supports the necessity for clear expectations in mechanistic reasoning in organic chemistry classrooms. We present a framework that offers a lens to clarify these expectations and discuss implications of the framework for improving student mechanistic reasoning in organic chemistry.

Students often struggle with solving mechanism problems in organic chemistry courses. They frequently focus on surface features, have difficulty attributing meaning to symbols, and do not recognize tasks that are different from the exact tasks practiced. To be more successful, students need to be able to extract salient features, map similarities to problems seen previously, and extrapolate while solving problems. In short, students must be able to recognize and generate abstractions. To help students in learning to solve problems, we need a better understanding of the nature of students' capacity for abstraction. Building upon an exploratory study (Sevian H., Bernholt S., Szteinberg G. A., Auguste S. and Pérez L. C., (2015), "Use of representation mapping to capture abstraction in problem solving in different courses in chemistry," "Chem. Educ. Res. Pract.," 16(3), 429-446), we applied the representation mapping model of Hahn and Chater (1998a) to characterize the abstraction employed by students while solving mechanistic problems in organic chemistry, and to measure students' growth in abstraction capacity across a semester. This model operationalizes abstraction by considering (a) the ways in which students match existing knowledge to new instances (abstracting) and (b) the level of abstractness of students' representations. We describe characteristic indicators of abstracting and abstractness. Trends were observable in the abstraction present in the reasoning of successful and unsuccessful problem solvers. Students who proposed plausible solutions used both strict or partial matching, but students who proposed implausible solutions tended to use strict matching. Students who proposed plausible solutions utilized higher levels of abstractness. This indicates that flexibility in abstraction processes may be important to successfully solve problems. The findings have implications for developing instructors' assessment practices in ways that build students' abstraction capacity.

The central goal of this study was to analyze the complexity of students' explanations about how and why chemical reactions happen in terms of the types of causal connections students built between expressed concepts and ideas. We were particularly interested in characterizing differences in the types of reasoning applied by students with different levels of training in the chemistry, from college to graduate school. Using a qualitative research approach, we identified diverse modes of reasoning expressed by students when engaged in the analysis of different sets of chemical reactions selected to produce a targeted compound. Main findings indicate that dominant modes of reasoning varied with educational level and the nature of the task. Although participants applied diverse modes of reasoning, linear causal reasoning was prevalent across educational levels and types of tasks. Many students tended to generate explanations based on the identification of a single agent that caused a sequential chain of events. Advanced undergraduate students in our sample generated the most complex explanations. The results of our study have important implications for the development of causal mechanistic reasoning in chemistry.

The central goal of this qualitative research study was to uncover major implicit assumptions that students with different levels of training in the discipline apply when thinking and making decisions about chemical reactions used to make a desired product. In particular, we elicited different ways of conceptualizing why chemical reactions happen (chemical causality), how these processes occur (chemical mechanism), and how they can be controlled (chemical control). In each of these areas we characterized conceptual modes with different explanatory power and explored how they were applied by participants when facing different types of questions. Our findings suggest potential paths in the development of understanding about chemical reactions in the context of making specific substances. Our study also highlights the benefits of analyzing students' understanding not only by focusing on implicit cognitive elements, but by using disciplinary crosscutting concepts as lenses of analysis. This articles contains notes and references.