Models as feedback: Developing representational competence in chemistry. created by Shamin Padalkar & Hegarty, M

[Correction Notice: An Erratum for this article was reported in Vol 107(2) of Journal of Educational Psychology (see record 2015-16952-001). The affiliation of Shamin Padalkar was incorrectly listed in the author note as the Inter-University Centre for Astronomy and Astrophysics (IUCAA). Dr. Padalkar is now a freelance consultant. Dr. Padalkar’s contact information as it appears in the author note otherwise remains the same.] Spatial information in science is often expressed through representations such as diagrams and models. Learning the strengths and limitations of these representations and how to relate them are important aspects of developing scientific understanding, referred to as representational competence. Diagram translation is particularly challenging for students in organic chemistry, and although concrete models greatly help in solving diagram translation problems, most students do not use models spontaneously. In 2 experiments, we examined the effectiveness of instructional interventions for teaching diagram translation using models. In Experiment 1, students drew diagrams and checked their accuracy by attempting to match concrete models to their solutions (model-based feedback). The instruction helped students in the experimental group to identify their mistakes, understand the usefulness of concrete models, and led to large improvements in performance, compared with a control group. To examine whether feedback, the opportunity to match models, or both was the critical aspect of the intervention, in Experiment 2, 1 group was provided only verbal feedback (by a tutor) and another group matched diagrams and concrete models, but not in the context of receiving an evaluation of their pretest performance. Feedback alone did not improve performance relative to a control group, but the opportunity to match models and diagrams improved performance relative to control. The results indicate that using models as feedback is an effective way of training representational competence in the domain of organic chemistry and more generally in science, technology, engineering, and mathematics disciplines.