Recent advances in natural language processing (NLP) have greatly helped educational applications, for both teachers and students. In higher education, there is great potential to use NLP tools for advancing pedagogical research. In this paper, we focus on how NLP can help understand student experiences in engineering, thus facilitating engineering educators to carry out large scale analysis that is helpful for re-designing the curriculum. Here, we introduce a new task we call response construct tagging (RCT), in which student responses to tailored survey questions are automatically tagged for six constructs measuring transformative experiences and engineering identity of students. We experiment with state-of-the-art classification models for this task and investigate the effects of different sources of additional information. Our best model achieves an F1 score of 48. We further investigate multi-task training on the related task of sentiment classification, which improves our model’s performance to 55 F1. Finally, we provide a detailed qualitative analysis of model performance.