Revealing the Hidden Curriculum: Analyzing Emotional Responses Using Advanced Computational Sentiment Analysis Techniques

This innovative practice full paper analyzed over 900 responses related to awareness of the hidden curriculum to explore the relationships between expressed emotions among students and faculty members. The hidden curriculum refers to the implicit values, behaviors, and norms that are not formally included in educational programs but are learned through the social and cultural environment of an institution. Using both emotional and demographic data collected from a larger national survey of engineering educators and students across the U.S., we examined correlations between participants' responses and the sentiment classifications observed. Our analysis employed two distinct tools: VADER for sentiment analysis and a pre-trained Recurrent Neural Network (RNN) model capable of classifying six specific emotions. Through detailed analysis and comparison, we uncovered significant insights into the emotional dynamics within engineering education. Overall, we found a predominance of positive sentiment, with “joy” emerging as the most frequently expressed emotion among both students and faculty. However, we also identified nuanced variations in emotional expression, influenced by factors such as gender, engineering disciplines, and the sentiment analysis methods employed. These findings contribute to the ongoing discourse on the hidden curriculum's impact on emotional experiences, emphasizing the importance of considering both sentiment and distinct emotional states in educational research and practice. By providing a deeper understanding of emotional patterns, this research offers valuable perspectives on how emotions shape the educational experience in engineering.