1/26 Caitlin Scientific practices

Nersessian and diSessa are two researchers who are interested in how computational modeling and representations are used. Nersessian’s study focused on how scientists use models as they build a new hypothesis and create conclusions. DiSessa focused on how students can learn mathematical and scientific concepts through computational modeling.

There are several practices that are important to science and engineering work, mentioned by the NGSS, but that were not particularly prominent in the Nersessian and diSessa papers. A couple of these practices include the building of an experiment and collection and usage of data and observations. Creating an experiment is the first step in a long process for studying a scientific question, and a researcher needs to decide what data is relevant or important to answer the question that is asked. Nersessian described how the researchers in the case study decided on how the experiment should be set up and what they needed to observe. Computational modeling was used to set up the experiment.

NGSS discusses analyzing and interpreting data and observations as an important practice that students should be able to perform. NGSS argues that that organizing the data into a form (i.e. creating a model or representation) is a necessary skill. For the Nersessian and diSessa papers, this is where the practice of computational modeling comes in. It is the focus of the Nersessian paper, and diSessa discusses how students can use computational models to create and organize information for themselves to see and make sense of concepts.

Another practice, discussed in the NGSS, that was prominent in the readings was revision of hypotheses and the experiment. It was not the focus of the diSessa paper, but it was a large practice in the Nersessian paper. For example, the computational modeling the case study in the paper showed the researchers how an event that they originally thought was something to minimalize was actually something that was important to their study. Modeling information observed from data collection can lead to findings that were not originally predicted, leading to adjustment and revision of the concepts or hypotheses.

After information or observations are analyzed or tested, revisions are made, and an answer to a question or problem is found, scientists will then use models and representations to explain, and defend, findings or design solutions. In the diSessa paper, it is argued that the computational models act as an explanation for concepts. The researchers in the case study will use the models they created for their data to explain, and possibly defend, their findings to the scientific community. Then hopefully, other scientists will found their own studies on those models.