Ahmad Al Doulat

Summative course feedback often leaves instructors with more questions than answers, particularly when interpreting student experiences across diverse populations and determining meaningful improvements. In high-enrollment, introductory computing courses, this challenge is especially pressing. Students and faculty from diverse academic and personal backgrounds amplify the need to understand emotional patterns for equitable instruction and increased student satisfaction. This short paper introduces the use of the emotional trajectory model (ETM) as a computational framework for analyzing student sentiment over time. Using end-of-semester instructional evaluation feedback collected across six semesters in the Using Information Technology course, we applied ETM to examine how students emotionally engaged with the course throughout the selected terms. Our focus is on developing a method that enables faculty to discover patterns obscured in traditional evaluations. We outline a high-level overview of the data preparation and analytical process used to generate sentiment-based emotional analysis that evaluates trends in student satisfaction with greater depth. By exploring how students respond emotionally to different aspects of the course, we aim to support more responsive and reflective pedagogical practices–approaches that foster belonging, persistence, and success for all students.

This study examined the applicability of regularized regression in family science by focusing on fathers’ psychological well-being as an applied example. Using cross-sectional survey data from 277 U.S. fathers, we compared Ordinary Least Squares (OLS) regression with Ridge, LASSO (Least Absolute Shrinkage and Selection Operator), and Elastic Net in regularized regression to evaluate how diverse familial factors—including father involvement, father role identity, parenting competency, parent–child relationships, coparenting relationships, and work–family conflict—concurrently predict fathers’ well-being. Results indicated that while the three regularized approaches produced similar overall performance, they provided advantages over OLS by reducing overfitting, improving generalizability, and enhancing interpretability. We also compared the analysis results of the three approaches in terms of the prediction accuracy and interpretability. The findings reveal how regularized regression can highlight key factors, such as father involvement, coparenting, and parenting competency. This study also illustrates how regularized regression complements traditional regression in family science by addressing multicollinearity, reducing overfitting, and supporting feature selection. These results highlight the potential of computational approaches such as machine learning to strengthen both theory and practice in family research.