DOI: 10.5061/dryad.47d7wm3s3
title: Survey data on student expectations from teaching assistants in engineering education

description: 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 gender,
 race/ethnicity, and country of origin within 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%) of students overall
 thought that teaching practice was most important for TAs to emphasize,
 while 37.6% believed teacher preparation to be most important. A smaller
 but noteworthy percentage (7.61%) of students expected TAs to be caring
 and hospitable. Significant differences emerged across time period,
 race/ethnicity, and country of origin, but gender differences were not
 significant. The results of this study indicate that students have a wide
 range of expectations of TAs, that these expectations vary over time, and
 that their expectations are largely consistent with cognitivism approaches
 to teaching and learning. While the results of this study can directly
 inform TA professional development and faculty guidance and supervision of
 TAs, it is also important that both faculty and TAs introduce other
 learning theories into their teaching (e.g., constructivist, humanistic,
 connectivist) to further develop deep learning skills and better prepare
 engineers for the workforce. 
description: # Survey data on student expectations from teaching assistants in
 engineering education Dataset DOI:
 [10.5061/dryad.47d7wm3s3](10.5061/dryad.47d7wm3s3) ## Description of the
 data and file structure **Data Description** This dataset contains
 de-identified qualitative responses from undergraduate students about how
 teaching assistants (TAs) can best support their learning. The data was
 collected as part of a study investigating student support needs in
 engineering education. Along with open-ended responses, the dataset
 includes demographic information such as major, gender, race, income
 bracket, and family education background. Responses were coded using
 thematic analysis. Blank cells indicate that the participant did not
 supply an answer for that item or that the question was not applicable.
 **Data Collection** Data was collected through an online survey
 distributed to students enrolled in undergraduate engineering courses.
 Students provided short-answer responses to the question: &gt; "What
 one action can your TAs at take to best support
 you in your classes (please be as specific as possible)?"
 Participants were informed that their anonymized responses would be used
 for research and made publicly available. Written informed consent was
 obtained. The research protocol was reviewed by the University of
 Washington Human Subjects Division and determined to be exempt under
 Category 1. IRB exemption number: STUDY00000378. ### Files and variables
 #### File: Data_WhatDoStudentsExpectFromTeachingAssistants.xlsx
 **Description:** This spreadsheet contains one row per participant
 response. The following variables are included: ##### Variables *
 **Participant_ID:** An anonymized respondent identifier in the format
 P0001, P0002, etc. These identifiers are sequential and do not encode any
 course, section, or term information. * **Subject_Group:** An anonymized
 instructional context label in the format Group_###. The same label is
 used across the dataset and within the verbatim responses when
 participants referenced a specific course or section. These labels cannot
 be linked back to institutional course numbers. * **TimePeriod:** Time
 when the course and data collection occurred. Values: Pre-COVID (before
 March 2020), ERT (Emergency Remote Teaching during the initial pandemic
 response), Post-COVID (after the return to in-person instruction). *
 **Major:** Student’s declared academic major. Values: Civil and
 Environmental Engineering, Electrical and Computer Engineering, Materials
 Science and Engineering, Mechanical Engineering, Other Engineering, Other
 Non-Engineering. * **Gender:** Student’s self-reported gender. Values:
 Female, Male, Other. * **CountryOfOrigin:** Domestic or international
 classification. Values: Domestic Student, International Student. *
 **Race:** Student’s self-reported race or ethnicity. Values: Asian,
 Asian/White, Black, Latino, Other URM, White. *
 **Family_of_Origin_Income:** Family income range. Values: $0–$10k,
 $10k–$20k, $20k–$40k, $40k–$60k, $60k–$80k, $80k–$100k, $100k–$150k,
 Greater than $150k. * **GPA:** Student’s self-reported grade point average
 on a 4.0 scale. * **Fathers_Education:** Highest education level of the
 student’s father. Values: Did not finish high school, High school,
 Attended college but did not complete a degree, Associate’s degree,
 Bachelor’s degree, Master’s degree, Doctoral degree. * **What one action
 can your TAs at take to best support you in your
 classes (please be as specific as possible)?** Open-text student response.
 Personal names have been replaced with “TA,” and any mention of a specific
 course or section has been replaced with the corresponding Subject_Group
 label to preserve anonymity. * **PrimaryTheme1:** First main theme derived
 from manual qualitative coding. Values: Hospitality, Interactions,
 Preparation, No Response, Other (unable to code). *
 **SecondaryTheme_for_PrimaryTheme1:** Sub-theme associated with
 PrimaryTheme1. Values: active learning, assessing performance, contact,
 diverse ways of learning, enhancing transfer, feedback, gaining attention,
 guidance, informing learning objectives, presentation of content,
 stimulating recall. * **PrimaryTheme2:** Second main theme if identified.
 Values: Hospitality, Interactions, Preparation. *
 **SecondaryTheme_for_PrimaryTheme2:** Sub-theme associated with
 PrimaryTheme2. Values: active learning, assessing performance, contact,
 diverse ways of learning, feedback, guidance, inform learning objectives,
 presentation of content, stimulating recall. ## Code/software No code or
 scripts are included with this dataset submission. Thematic coding was
 conducted manually using spreadsheet software. The dataset can be viewed
 and analyzed using Microsoft Excel, Google Sheets, or any CSV-compatible
 viewer. Descriptive and statistical analyses (e.g., chi-square tests,
 inter-rater reliability) were performed as part of the associated study,
 but no proprietary software or custom code is required to interpret the
 dataset. ## Access information Other publicly accessible locations of the
 data: * *None. This dataset is not available in any other public
 repository.* Data was derived from the following sources: * *Not
 applicable. The data was originally collected by the authors through
 student surveys.* ## Human subjects data This dataset contains
 de-identified survey responses from students collected as part of a study
 on instructional support. All identifying information (such as names,
 student IDs, email addresses) has been removed. Indirect identifiers such
 as demographic variables are included in general categories (e.g., income
 brackets, broad majors) to prevent re-identification. Participants were
 informed that their anonymized responses would be used for research and
 made publicly available. Written informed consent was obtained from all
 participants. The research protocol was reviewed by the University of
 Washington Human Subjects Division (HSD), which determined it to be exempt
 under Category 1. The exemption number is STUDY00000378.