skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Search for: All records

Creators/Authors contains: "Li, L"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract Research-integrated instruction is widely used to place inquiry-oriented work inside required engineering courses. In practice, multi-course initiatives often rely on similar formats across departments, such as projects, teamwork, and iterative tasks, even though fields differ in how problems are framed and how evidence is evaluated. This paper reports descriptive results from a college-wide initiative at a historically Black university that embedded research-integrated activities into ten upper-level courses in Civil and Architectural Engineering (CAE), Mechanical Engineering (ME), and Electrical and Computer Engineering (ECE). Students completed a Course Elements survey in the first and final weeks of the semester and a Learning Gains survey at the end of the semester. Results show learning-gain composites above the scale midpoint in all three departments, with no statistically significant difference at the composite level. Clearer differences appeared in selected course elements tied to ownership and communication, including studentdesigned work, documentation, oral presentation, and data collection. Across courses, higher endof- semester engagement with research-integrated elements was associated with higher reported learning gains. Overall, the findings support program-level coordination around shared elements while leaving room for discipline-specific enactment. 
    more » « less
  2. Low-income students remain underrepresented in STEM (Science, Technology, Engineering, and Mathematics) fields, with high attrition rates posing challenges to educational equity and workforce development. This study examined how participation in the S-STEM program at Tennessee State University (TSU) influences students’ sense of belonging (SOB). A survey of 101 undergraduate STEM students (61 S-STEM scholars, 40 non-scholars) was analyzed using descriptive statistics, t-tests, ANOVA, and linear regression. Results showed no statistically significant differences in overall SOB between S-STEM scholars and non-scholars; however, regression analysis revealed that campus environment and students’ willingness to choose the department again were significant positive predictors of SOB, whereas S-STEM participation, faculty support, faculty inclusion of diverse perspectives, and administrative support were not significant. These findings suggest that while S-STEM provides important financial and community resources, perceptions of a welcoming campus environment and overall departmental satisfaction are stronger determinants of belonging, highlighting the importance of broader institutional integration and supportive environments for low-income STEM students. 
    more » « less
  3. This study employed an empirical research design to examine factors influencing engaged learning among undergraduate engineering students. The participants consisted of 43 undergraduates enrolled in the college of engineering at Tennessee State University. Data were collected using Schreiner and Louis’s engaged learning survey, which assesses students’ demographic characteristics, academic performance, learning satisfaction, and levels of engaged learning. Following data cleaning and imputation, a feature selection procedure was conducted. Hierarchical linear regression analyses were first performed to examine the incremental contributions of demographic variables, academic performance indicators, and satisfaction-related factors to engaged learning. Based on the full model, key predictors were subsequently entered into simplified hierarchical regression models to identify the most influential variables. The selected predictors were then entered into a final linear regression model to evaluate their overall effects. The results indicated that overall satisfaction was the strongest and most consistent positive predictor of engaged learning among engineering undergraduates. Learning satisfaction and critical thinking gain showed weaker, though positive, correlations with engaged learning, whereas ethnicity exhibited a negative relationship. These findings emphasize the importance of students’ overall educational experience in fostering engaged learning in engineering education and suggest that efforts to enhance student satisfaction may contribute to improved engagement and learning outcomes in engineering programs. 
    more » « less