Teaching Assistants (TAs) make important contributions to STEM teaching in higher education. While TAs often play both peer and authority figure roles, however, relatively little is known about exactly what students expect from TAs. To fill this gap, the first major goal of this study was to comprehensively understand these expectations from a large population of undergraduate engineering students. In addition, this study sought to understand how these expectations vary with students’ gender, race/ethnicity, and international status during distinct time periods associated with the recent COVID-19 pandemic (pre-COVID, during COVID, and post COVID). Student expectations were measured via a short-answer survey question in a cross-sectional dataset at a single, large institution comprised of sophomore to senior level students (n = 1,678) enrolled in engineering courses between 2016 and 2023. Thematic analyses were used to analyze student expectations and statistical, quantitative techniques were used to identify demographic differences. While no single majority theme emerged, many (42.9%) students thought that interactions were most important for TAs to emphasize while 37.6% believed TA preparation to be most important. A smaller but noteworthy percentage (7.61%) of students expected TAs to be caring and hospitable. Significant differences emerged in different time periods and across students’ race/ethnicity, international status, and gender. The results of this study indicate that students have a wide range of expectations of TAs and that these expectations are different for different times and classroom conditions. The results of this study can directly inform TA professional development as well as faculty guidance and supervision of TAs.

Teaching Assistants (TAs) make important contributions to STEM teaching in higher education. While TAs often play both peer and authority figure roles, however, relatively little is known about exactly what students expect from TAs. To fill this gap, the first major goal of this study was to comprehensively understand these expectations from a large population of undergraduate engineering students. In addition, this study sought to understand how these expectations vary with students’ gender, race/ethnicity, and international status during distinct time periods associated with the recent COVID-19 pandemic (pre-COVID, during COVID, and post COVID). Student expectations were measured via a short-answer survey question in a cross-sectional dataset at a single, large institution comprised of sophomore to senior level students (n = 1,678) enrolled in engineering courses between 2016 and 2023. Thematic analyses were used to analyze student expectations and statistical, quantitative techniques were used to identify demographic differences. While no single majority theme emerged, many (42.9%) students thought that interactions were most important for TAs to emphasize while 37.6% believed TA preparation to be most important. A smaller but noteworthy percentage (7.61%) of students expected TAs to be caring and hospitable. Significant differences emerged in different time periods and across students’ race/ethnicity, international status, and gender. The results of this study indicate that students have a wide range of expectations of TAs and that these expectations are different for different times and classroom conditions. The results of this study can directly inform TA professional development as well as faculty guidance and supervision of TAs.

The 10,000 Worlds Employee Dataset is a comprehensive dataset designed for analyzing workforce trends, employee performance, and organizational dynamics within a large-scale company setting. This dataset contains information on 10,000 employees, spanning various departments, roles, and experience levels. It is ideal for research in human resource analytics, machine learning applications in employee retention, performance prediction, and diversity analysis.

Key Features of the Dataset: Employee Demographics:

Age, gender, ethnicity Education level, degree specialization Years of experience Employment Details:

Department (e.g., HR, Engineering, Marketing) Job title and seniority level Employment type (full-time, part-time, contract) Performance & Productivity Metrics:

Annual performance ratings Work hours, overtime details Training programs attended Compensation & Benefits:

Salary, bonuses, stock options Benefits (healthcare, pension plans, remote work options) Employee Engagement & Retention:

Job satisfaction scores Attrition and turnover rates Promotion history and career growth Workplace Environment Factors:

Team collaboration metrics Employee feedback and survey results Work-life balance indicators Use Cases: HR Analytics: Identifying patterns in employee satisfaction, retention, and performance. Predictive Modeling: Forecasting attrition risks and promotion likelihoods. Diversity & Inclusion Analysis: Understanding representation across departments. Compensation Benchmarking: Comparing salaries and benefits within and across industries. This dataset is highly valuable for data scientists, HR professionals, and business analysts looking to gain insights into workforce dynamics and improve organizational strategies.

Would you like any additional details or a sample schema for the dataset?

There are two data sets of eye scans available. The first of these is a set fundus images of which the are c. 7.0 million. The other is a set of OCT scans of which there are c. 440, 000.

This dataset contains routine clinical ophthalmology data for every patient who have been seen at Queen Elizabeth Hospital and the Birmingham, Solihull and Black Country Diabetic Retinopathy screening program at University Hospitals Birmingham NHS Foundation Trust, with longitudinal follow-up for 15 years. Key data included are: • Total number of patients. • Demographic information (including age, sex and ethnicity) • Past ocular history • Intravitreal injections • Length of time since eye diagnosis • Visual acuity • The national screening diabetic grade category (seven categories from R0M0 to R3M1) • Reason for sight and severe sight impairment

Geography University Hospitals Birmingham is set within the West Midlands and it has a catchment population of circa 5.9million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.

Data source: Ophthalmology department at Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic screening scheme in Europe.

Pathway: The routine secondary care follow-up in the hospital eye services for all ophthalmic diseases at Queen Elizabeth Hospital. The Birmingham, Solihull and Black Country dataset is representative of the patient pathway for community screening and grading of diabetic eye disease.

Teaching Assistants (TAs) make important contributions to STEM teaching in higher education. While TAs often play both peer and authority figure roles, however, relatively little is known about exactly what students expect from TAs. To fill this gap, the first major goal of this study was to comprehensively understand these expectations from a large population of undergraduate engineering students. In addition, this study sought to understand how these expectations vary with students’ gender, race/ethnicity, and international status during distinct time periods associated with the recent COVID-19 pandemic (pre-COVID, during COVID, and post COVID). Student expectations were measured via a short-answer survey question in a cross-sectional dataset at a single, large institution comprised of sophomore to senior level students (n = 1,678) enrolled in engineering courses between 2016 and 2023. Thematic analyses were used to analyze student expectations and statistical, quantitative techniques were used to identify demographic differences. While no single majority theme emerged, many (42.9%) students thought that interactions were most important for TAs to emphasize while 37.6% believed TA preparation to be most important. A smaller but noteworthy percentage (7.61%) of students expected TAs to be caring and hospitable. Significant differences emerged in different time periods and across students’ race/ethnicity, international status, and gender. The results of this study indicate that students have a wide range of expectations of TAs and that these expectations are different for different times and classroom conditions. The results of this study can directly inform TA professional development as well as faculty guidance and supervision of TAs.

Teaching Assistants (TAs) make important contributions to STEM teaching in higher education. While TAs often play both peer and authority figure roles, however, relatively little is known about exactly what students expect from TAs. To fill this gap, the first major goal of this study was to comprehensively understand these expectations from a large population of undergraduate engineering students. In addition, this study sought to understand how these expectations vary with students’ gender, race/ethnicity, and international status during distinct time periods associated with the recent COVID-19 pandemic (pre-COVID, during COVID, and post COVID). Student expectations were measured via a short-answer survey question in a cross-sectional dataset at a single, large institution comprised of sophomore to senior level students (n = 1,678) enrolled in engineering courses between 2016 and 2023. Thematic analyses were used to analyze student expectations and statistical, quantitative techniques were used to identify demographic differences. While no single majority theme emerged, many (42.9%) students thought that interactions were most important for TAs to emphasize while 37.6% believed TA preparation to be most important. A smaller but noteworthy percentage (7.61%) of students expected TAs to be caring and hospitable. Significant differences emerged in different time periods and across students’ race/ethnicity, international status, and gender. The results of this study indicate that students have a wide range of expectations of TAs and that these expectations are different for different times and classroom conditions. The results of this study can directly inform TA professional development as well as faculty guidance and supervision of TAs.

Race/ethnicity of HB students from 1998–2018 and public secondary school students.