STEM identity, a disciplinary identity that reflects an individual's self-understanding in connection with science, technology, engineering, and mathematics (STEM), impacts students ' integration into and retention within STEM fields. This study aimed to develop a STEM identity model, called the BioCheM-ID, to measure STEM identity across biology, chemistry, and mathematics among students enrolled in an introductory-level biology course at a large post-secondary public institution in Texas, United States. The study explored how student-centered factors, such as gender, race, student major, and pre-college informal STEM learning experiences, influence STEM identity. Additionally, the study investigated the relationships between students' educational aspirations, expectations, and STEM identity, and the connections between STEM identity and how undergraduates process, use, and interpret the slope-intercept concept of a simple linear graph (y = mx + b). The BioCheM-ID model comprised five latent factors: biology perceived competence and interest, chemistry perceived competence and interest, mathematics perceived competence and interest, biology and chemistry beliefs, and mathematics beliefs. Students' major and pre-college informal STEM learning experiences, particularly mentoring and tutoring, were significant factors of STEM identity. Positive correlations were observed between educational aspirations, expectations, and STEM identity. Students with high STEM identities demonstrated proficiency in providing productive responses regarding the slope-intercept concept of a simple linear graph, showcasing high graphical literacy.

Data visualization has the power to portray an informative message when designed with the end user in mind. Energy data visualizations must be tailored to the resident's energy, graphical, and data literacy level. A resident's energy, graphical, and data literacy level depicts their understanding and life experience with energy. Current utility companies standardize data visualization formats for all customers, regardless of their literacy level. My aim for this dissertation is to evaluate how data visualization mediums (2D chart types and virtual reality visual aids) aid residents when reading, working with, analyzing, and arguing energy consumption data of household appliance pairs. The data visualization chart types explored include the area, bar, and circular column charts. The visual aids displayed in the virtual environment explored include color coding, electricity flow, and the power meter. The energy data of the household appliances is embedded within the visual aids without displaying energy metrics. The household appliances include lighting (LED vs incandescent bulb), cooking (air fryer and stove), and heating appliances (heat pump and space heater). The participants included 32 graduate students from Virginia Tech engineering programs. Results from the study showed that some participants had a hard time interpreting axis unit metrics energy such as watts, watt*minutes, and kWhs in all three 2D chart types. If participants could not read and work with the units on charts, their ability to analyze and argue about the energy data was diminished quickly. In addition, when participants were interacting with the visual aids, researchers discovered that the power meter was the easiest to convey because it provided participants with a way to qualitatively and quantitatively answer the questions presented by the questionnaire. This dissertation provides insights for researchers, utility companies, and policymakers to move away from standardized data visualizations and utilize alternative visuals for reading, working with, analyzing, and arguing residential energy consumption data. Researchers can utilize the dissertation insights to explore other data visualization mediums that have the potential to convey energy insights. Utility companies can begin implementing these alternative data visualizations in pilot programs to test their effectiveness with the public. And lastly, policymakers can enforce utility companies to prioritize customer literacy levels when administering utility bills. / Doctor of Philosophy / Data visualization has the power to tell a wonderful, meaningful, and thoughtful story when created with the end user in mind. The same thing can be said about visualizations on utility bills when tailored to a resident's energy, graphical, and data literacy level. A residents' literacy level results from their life experiences and circumstances dealing with energy data. Currently, data visualizations are standardized by utilizing companies, meaning that all individuals receive the same energy data visualization no matter their literacy level. My aim for this dissertation is to evaluate which data visualization chart types and VR visual aids enable reading, working with, analyzing, and arguing energy consumption data of household appliance pairs for residents. The data visualization chart types explored include the area, bar, and circular column charts. The visual aids explored include color coding, electricity flow, and the power meter. The household appliances include lighting (LED vs incandescent bulb), cooking (air fryer and stove), and heating appliances (heat pump and space heater). The participants included 32 graduate students from Virginia Tech engineering programs. Results from the study showed that some participants had difficulty reading and working with energy metrics on all three 2D chart types. If participants did not understand the units when reading and working with the charts their ability to analyze and argue the energy data was insufficient. In addition, when participants were interacting with the visual aids, researchers discovered that the power meter was the easiest to convey because it provided participants with a way to qualitatively and quantitatively answer the questions presented by the questionnaire. This dissertation provides insights for researchers, utility companies, and policymakers to move away from standardized data visualizations and utilize alternatives that enable reading, working with, analyzing, and arguing residential energy consumption data.

A carefully constructed set of 16 graphical tasks related to key biochemistry concepts was designed and administered to a group of 82 students in their final year of B.Sc. study. The test mean score of 48,3% ( 12,1) was low and characterised by gender and ethnic differences. There was a moderate linear relationship between biochemistry grades obtained by the students over two years of study and their graphical literacy (r = 0,433). The majority of the students exhibited slope/height confusion and only seven students (8,5%) were able to answer the two items corresponding to Kimura‘s Level F, the most complex and difficult level of graphical literacy. Eye tracking data gave valuable insights into different strategies used by students while interpreting graphs and is a valuable tool for assessing graphical literacy. These findings confirmed other studies where researchers have found a widespread lack of graph comprehension among biological science students. / Institute of Science and Technology Education / M. Sc. (Science Education)

A carefully constructed set of 16 graphical tasks related to key biochemistry concepts was designed and administered to a group of 82 students in their final year of B.Sc. study. The test mean score of 48,3% ( 12,1) was low and characterised by gender and ethnic differences. There was a moderate linear relationship between biochemistry grades obtained by the students over two years of study and their graphical literacy (r = 0,433). The majority of the students exhibited slope/height confusion and only seven students (8,5%) were able to answer the two items corresponding to Kimura‘s Level F, the most complex and difficult level of graphical literacy. Eye tracking data gave valuable insights into different strategies used by students while interpreting graphs and is a valuable tool for assessing graphical literacy. These findings confirmed other studies where researchers have found a widespread lack of graph comprehension among biological science students. / Institute of Science and Technology Education / M. Sc. (Science Education)