Abstract:

Promoting excellence and enhancing undergraduate experience for STEM majors is necessary to ensure that the nation has the STEM-literate workforce required to solve the societal and technological challenges of the twenty-first century and beyond. Over the last decade, colleges and universities have demonstrated a sustained focus on improving the quality of undergraduate education, especially science, technology, engineering, and mathematics (STEM). However, their efforts have proved to be highly varied in their capacity to meet effectively the needs of underprepared students as more students aspired to postsecondary education.

In 2018, Elizabeth City State University (ECSU) received funding from the National Science Foundation (NSF) under the Historically Black Colleges and Universities – Undergraduate Program (HBCU-UP) initiative for an Implementation Projects track. The goal was to design, implement, study, and assess comprehensive institutional efforts to increase the number of students receiving undergraduate degrees STEM and enhance the quality of their preparation. ECSU’s HBCU-UP Implementation Project addressed the retention and persistence of STEM majors through early contact, beginning in the last semester of high school and continuing throughout the sophomore year. The project consisted of three key components: mentoring, research, and education/training. The project activities included, the STEM Boot Camp, Self-Regulated Learning, Sophomore Bridge Program, STEM Faculty Journal Club, Course Redesign, Faculty Development, Pedagogical Lab, and STEM Innovation Research Lab.

Both quantitative and qualitative methods were employed to gauge the effectiveness of project activities. In this paper, we will focus on the qualitative data such as the student responses to open-ended interviews. Unlike quantitative assessments, student feedback provides an authentic narrative of their experiences, capturing the emotional and cognitive dimensions of learning. Text analytics can efficiently process volumes of identifying sentiments and recurring themes that can inform program improvements, curriculum adjustments, and instructional strategies, ensuring that future iterations of the program are more effective. The insights derived through text analytics techniques such as sentiment analysis and frequency analysis provided a nuanced understanding of the student’s learning experiences, challenges, and overall perception of the program. Further, by valuing student feedback, the project team demonstrated a commitment to student-centered learning, which can enhance student motivation and engagement.

In this paper, general natural language processing (NLP) techniques, text analytics pipeline, data preprocessing, sentiment analysis using SpaCy/textBlob, and frequency analysis will be discussed.

Keywords:

Text Analytics, Sentiment Analysis, Student Feedback, Program Effectiveness, Assessment.