Computational learning analytics that integrate statistical modeling, logistic regression, and natural language processing (NLP) offer scalable approaches for understanding student experiences in engineering education. This study examines first-year engineering students at a Hispanic-Serving Institution (HSI) to investigate attitudinal development and predictors of STEM persistence, a critical factor in improving retention and equity. A multi-phase analytical framework was employed. Paired-samples t-tests showed significant gains in engineering confidence and sense of belonging, with medium-to-large effect sizes. Sequential logistic regression demonstrated that post-course attitudes predict STEM persistence with substantially higher accuracy than demographics or pre-course measures, with engineering value and intent to major as the strongest predictors. NLP analysis of open-ended responses identified math integration as the most dominant and recurring theme across all questions. Sentiment analysis indicated primarily neutral and positive student feedback. Findings consistently show that course-driven attitudinal shifts rather than demographic factors drive persistence. This work contributes to a replicable, data-driven framework that integrates quantitative and qualitative analytics to generate actionable insights for improving first-year engineering experiences, particularly in HSI contexts.
http://orcid.org/0000-0002-9940-4405
University of Texas at El Paso
[biography]
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