The goal of the present research is to develop machine-assisted methods that can assist in the analysis of students’ written compositions in ethics courses. As part of this research, we analyzed Social Impact Assessment (SIA) papers submitted by engineering undergraduates in a course on engineering ethics. The SIA papers required students to identify and discuss a contemporary engineering technology (e.g., autonomous tractor trailers) and to explicitly discuss the ethical issues involved in that technology. Here we describe the ability of three machine tools to discriminate differences in the technical compared to ethical portions of the SIA papers. First, using LIWC (Language Inquiry and Word Count) we quantified differences in analytical thinking, expertise and self-confidence, disclosure, and affect, in the technical and ethical portions of the papers. Next, we applied MEH (Meaning Extraction Helper) to examine differences in critical concepts in the technical and ethical portions of the papers. Finally, we used LDA (Latent Dirichlet Allocation) to examine differences in the topics in the technical and ethical portions of the papers. The results of these three tests demonstrate the ability of machine-based tools to discriminate conceptual, affective, and motivational differences in the texts that students compose that relate to engineering technology and to engineering ethics. We discuss the utility and future directions for this research.Cockrell School of Engineerin
