Characterizing and Developing Chemistry Students' Data Analysis and Interpretation of Chemical Data

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Chemistry; Data Analysis; Data Interpretation; Undergraduate Students; Science Education; Graphs; Prior Learning; Simulation; Peer Evaluation; Feedback (Response); Reflection; Graduate Students; Decision Making Skills

979-83-8234-247-4

All chemistry students must develop competency in analyzing and making sense of data. However, there are many difficulties that chemistry students may experience while analyzing data. Many students may not use relevant prior knowledge to aid in making sense of data, or they may not form conclusions using all the data provided. Additionally, prior knowledge seems to influence one's data analysis, but little is known about how students use it to make sense of data. Thus, I interviewed undergraduate students as they analyzed graphical data for a task and characterized how they used their prior knowledge throughout. My findings suggest that students' prior knowledge helped to form a frame for students. This frame is then used throughout students' sensemaking to help search for and identify relevant data and evaluate data against their frame to aid in decision-making. Because there are limited classroom interventions designed to help develop undergraduate students' data analysis competencies, I designed a study in which undergraduate students compared their data analyses to pre-constructed sample responses via a simulated peer review. My findings suggest that providing students with the opportunity to compare their analyses against other responses, practice giving feedback, and reflect on their work may provide opportunities to generate internal feedback on their performance. Depending on the nature of the internal feedback (i.e., if it is critical or not), students may revise to improve their work. Finally, as part of contributing new knowledge to their field, chemistry graduate students must learn how to best respond to data that is discrepant with their expectations. Yet, there is little research on how chemistry graduate students analyze data, and none that explores how they respond to unexpected data. For this reason, I interviewed chemistry graduate students as they analyzed multiple data sets to explain a chemical phenomenon, and I characterized how students responded to unexpected data using Data-Frame Theory. My findings indicate that students respond to discrepant data in several ways, and each response is capable of progressing students' sensemaking to achieve the goals of the analysis. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

As Provided

2024

ED653845