A characterization of chemistry learners’ engagement in data analysis and interpretation

Abstract

Both graph comprehension and data analysis and interpretation are influenced by one’s prior knowledge and experiences. To understand how one‘s prior knowledge and experiences interact with their analysis of a...

“To Be Honest, I Didn’t Even Use the Data”: Organic Chemistry Students’ Engagement in Data Analysis and Interpretation

“…Further, some of these ways (selective, singular, and intuitive) align with the literature about students' data analysis and interpretation that show how students can ignore or manipulate data to maintain a frame. 33,35,41 Finally, these findings confirm other results showing that chemistry students are rather adept at making accurate observations of data, and the difficulty lies in coordinating their frame with these observations to draw conclusions. 41 Importantly, by better understanding the different ways a learner can engage in data analysis and interpretation, we can engage learners effectively to maximally coordinate their prior knowledge and data analysis and interpretation.…”

“…33,35,41 Finally, these findings confirm other results showing that chemistry students are rather adept at making accurate observations of data, and the difficulty lies in coordinating their frame with these observations to draw conclusions. 41 Importantly, by better understanding the different ways a learner can engage in data analysis and interpretation, we can engage learners effectively to maximally coordinate their prior knowledge and data analysis and interpretation. Further research is needed to implement and evaluate interventions to support development of organic chemistry students' competency in science practices.…”

“…This task was highly scaffolded to afford direct observation of the degree of students’ interaction with empirical data; in particular, the participants had a choice about if and how to incorporate the data into their argument, which may have limited the kinds of sensemaking students demonstrated. It is likely that patterns of reasoning and data analysis and interpretation might look different for students who are explicitly prompted to reason from a set of data. , We argue that both decisionsif and how to use data, and what to say about a set of dataare critical to understand if we want to empower science learners to make evidence-based decisions. In addition, due to the high degree of scaffolding and the use of secondary data, the modes of students’ data analysis and interpretation may have limited transferability to a laboratory context where students are tasked to collect and interpret their own primary data.…”

“…It is likely that patterns of reasoning and data analysis and interpretation might look different for students who are explicitly prompted to reason from a set of data. 20,41 We argue that both decisions�if and how to use data, and what to say about a set of data�are critical to understand if we want to empower science learners to make evidence-based decisions. In addition, due to the high degree of scaffolding and the use of secondary data, the modes of students' data analysis and interpretation may have limited transferability to a laboratory context where students are tasked to collect and interpret their own primary data.…”

“…Data analysis and interpretation involves deliberate and purposeful analysis of raw and transformed data to identify and extract important information, establish relevant patterns and relationships, and build or evaluate models. ,, However, students have demonstrated difficulty differentiating between relevant and irrelevant information. , Research has shown that relevant content and prior knowledge and framing plays an important role in students’ capacities to find appropriate features of the data for quality analysis. ,,− In the absence of relevant chemical knowledge or limitations in using chemical models, Heisterkamp and Talanquer found that students faced more challenges in explaining empirical data trends . In addition, Beck, Rupp, and Brandriet noticed students who expressed more confusion in their content knowledge also tended to exclude empirical data in constructing their kinetic rate models .…”