Students will very often write incomplete explanations when asked to do so. This may be due to various reasons, including but not limited to a lack of understanding of the content or a lack of motivation. Developing an understanding of how students form and orally express scientific explanations (i.e., participating in scientific discourse) and comparing that to written explanations will give insight into how students communicate the construction of their scientific explanations. This dissertation used sociocultural theory as the framework to study investigate the transcribed conversations of student work groups as they complete Model-Evidence Link (MEL) diagram activities and then write evaluative explanations of the evidence to model relationships (Lombardi, Bailey et al., 2018).Transcripts coded with the sociocultural discourse analysis (SDA) framework and the Lombardi, Brandt et al. (2016) evaluation rubric provided the primary data for this project, as well as data previously collected as part of the MEL Project. Structural equation modeling investigated the impact of the Quality of Conversation (QoC) construct on final MEL Project structural equation model (tSEM). Multivariate analysis was used to determine the trends of change over time for the evaluation scores. Finally, an adapted text analysis framework was used to make comparisons between transcripts and the written explanation tasks. Analysis found that the pathway from QoC to post-plausibility through evaluation in the experimental SEM was significant with a good model fitness (Tenenhaus GoF= 0.349, medium > 0.25). Analysis of the evaluation scores of the students showed no particular growth trends and qualitative analysis supported previous findings that the students use descriptive levels of evaluation when talking about and writing about the relationships between explanatory models and lines of evidence. This project found that the use of the MEL diagram may enhance student levels of evaluation when investigating the links between lines of evidence and explanatory models. However, a qualitative analysis of the student transcripts finds that students tend to use the basest level of evaluation (i.e., descriptive) in their discourse. This is consistent with the level of evaluation found in the written explanation tasks. This led me to suggest to teachers that they continue to support their students’ group conversations with discourse scaffolds. / Teaching & Learning