arXiv subjects

Z. Yasemin Kalender

Publications and source records attributed to Z. Yasemin Kalender.

5 recordsLinked to original sources

arXiv2024

Predictors and Socio-Demographic Disparities in STEM Degree Outcomes: A UK Longitudinal Study using Hierarchical Logistic Regression

Socio-demographic disparities in STEM degree outcomes impact the diversity of the UK's future workforce, particularly in fields essential for innovation and growth. Despite the importance of institution-level, longitudinal analyses in understanding degree awarding gaps, detailed multivariate and hierarchical analyses remain limited within the UK context. This study addresses this gap by using a multivariate binary logistic model with random intercepts for STEM subjects to analyse predictors of first-class degree outcomes using a nine-year dataset (2014 to 2022) from a research-intensive Russell Group university. We find that prior academic attainment, ethnicity, gender, socioeconomic status, disability, age, and course duration are significant predictors of achieving a first-class degree, with Average Marginal Effects calculated to provide insight into probability differences across these groups. Key findings reveal that Black students face a significantly lower likelihood of achieving first-class degrees compared to White students, with an average 16 percent lower probability, while students graduating from 4-year degree programmes have an average 24 percent higher probability of achieving a first-class degree relative to those on 3-year programmes. Although male students received a higher proportion of first-class degrees overall, our multivariate hierarchical model shows higher odds for female students, underscoring the importance of model choice when quantifying awarding gaps. Baseline odds for first-class outcomes rose considerably from 2016, peaking in 2021, indicating possible grade inflation during the COVID-19 pandemic. Interaction effects between socio-demographic variables and graduation year indicate stability in ethnicity, disability, and socioeconomic awarding gaps but reveal a declining advantage for female students over time.

physics.ed-ph↗

arXiv2023

Data Dialogue with ChatGPT: Using Code Interpreter to Simulate and Analyse Experimental Data

Artificial Intelligence (AI) has the potential to fundamentally change the educational landscape. So far, much of the physics education research relating to AI has focused on lecture-based assessment and the ability of ChatGPT to answer conceptual surveys and traditional exam-style questions. In this study, we shift the focus by investigating ChatGPT's ability to complete an introductory mechanics laboratory activity by using Code Interpreter, a recent plugin that allows users to generate and analyse data by writing and running Python code `behind the scenes'. By uploading a common `spring constant' lab activity using Code Interpreter, we investigate the ability of ChatGPT to interpret the activity, generate realistic model data, produce a line-fit, and calculate the reduced chi square statistic. By analysing our interactions with ChatGPT, along with the Python code generated by Code Interpreter, we assess how the quality and accuracy of ChatGPT's responses depends on different levels of prompt detail. We find that although ChatGPT is capable of completing the lab activity and generating plausible-looking data, the quality of the output is highly dependent on the detail and specificity of the text prompts provided. We find that the data generation process adopted by ChatGPT in this study leads to heteroscedasticity in the simulated data, which may be difficult for novice learners to spot. We also find that when real experimental data is uploaded via Code Interpreter, ChatGPT is capable of correctly plotting and fitting the data, calculating the spring constant and associated uncertainty, and calculating the reduced chi square statistic. This work offers new insights into the capabilities of Code Interpreter within a laboratory setting and highlights a variety of text-prompt strategies for the effective use of Code Interpreter in a lab context.

physics.ed-ph↗

arXiv2020

Preliminary evidence for available roles in mixed-gender and all-women lab groups

Group work during lab instruction can be a source of inequity between male and female students. In this preliminary study, we explored the activities male and female students take on during a lab session at a university in Denmark. Different from many studies, the class was majority-female, so three of the seven groups were all female and the rest were mixed-gender. We found that students in mixed-gender groups divide tasks in similar ways to mixed-gender groups at North American institutions, with men handling the equipment and women handling the computer more often. We also found that women in single-gender groups took on each of the available roles with approximately equal frequency, but women in single-gender groups spent more time on the equipment than students in mixed-gender groups. We interpret the results through poststructual gender theory and the notion of `doing physics' and `doing gender' in physics labs.

physics.ed-ph↗

arXiv2020

Sense of agency, gender, and students' perception in open-ended physics labs

Instructional physics labs are critical junctures for many STEM majors to develop an understanding of experimentation in the sciences. Students can acquire useful experimental skills and grow their identities as scientists. However, many traditionally-instructed labs do not necessarily involve authentic physics experimentation features in their curricula. Recent research calls for a reformation in undergraduate labs to incorporate more student agency and choice in the learning processes. In our institution, we have adopted open-ended lab teaching in the introductory physics courses. By using reformed curricula that provide higher student agency, we analyzed approximately 100 students in the introductory-level lab courses to examine their views towards the open-ended physics labs. Between the start and the end of the semester, we found a statistically significant shift in students' perceptions about the agency afforded in lab activities. We also examined students' responses to "Which lab unit was your favorite and why?". The analysis showed that majority of the students preferred Project Lab, which had the highest student agency and coding analysis showed that "freedom" was the most frequent response for students' reason for picking Project Lab. Finally, we also examined student views across gender and found no significant gender effect on students' sense of agency.

physics.ed-ph↗

arXiv2020

A mismatch between self-efficacy and performance: Undergraduate women in engineering tend to have lower self-efficacy despite earning higher grades than men

There is a significant underrepresentation of women in many Science, Technology, Engineering, and Mathematics (STEM) majors and careers. Prior research has shown that self-efficacy can be a critical factor in student learning, and that there is a tendency for women to have lower self-efficacy than men in STEM disciplines. This study investigates gender differences in the relationship between engineering students' self-efficacy and course grades in foundational courses. By focusing on engineering students, we examined these gender differences simultaneously in four STEM disciplines (mathematics, engineering, physics, and chemistry) among the same population. Using survey data collected longitudinally at three time points and course grade data from five cohorts of engineering students at a large US-based research university, effect sizes of gender differences are calculated using Cohen's d on two measures: responses to survey items on discipline-specific self-efficacy and course grades in all first-year foundational courses and second-year mathematics courses. In engineering, physics, and mathematics courses, we find sizeable discrepancies between self-efficacy and performance, with men appearing significantly more confident than women despite small or reverse direction differences in grades. In chemistry, women earn higher grades and have higher self-efficacy. The patterns are consistent across courses within each discipline. All self-efficacy gender differences close by the fourth year except physics self-efficacy. The disconnect between self-efficacy and course grades across subjects provides useful clues for targeted interventions to promote equitable learning environments. The most extreme disconnect occurs in physics and may help explain the severe underrepresentation of women in "physics-heavy" engineering disciplines, highlighting the importance of such interventions.

physics.ed-ph↗