A word-space visualization approach to study college of engineering mission statements

Most higher education institutions have a mission statement that is developed strategically by the institutions and often reflect the college's unique mission which sets it apart from peer institutions. Through this study, we describe the use of a Machine Learning and Natural Language Processing based textual data analytics to understand the word choices in the mission statements of U.S. based colleges of engineering. Our purpose is to understand the key similarities and differences between the choice of words used in the mission statements of the two groups: public colleges and private colleges of engineering. We were specifically interested in studying the terms related to diversity and inclusion and see the trends in the use of specific terms relating to diverse communities, intersections, minority populations, and the like. In this research study, we used a Word2Vec approach to visualize the words from mission statements for 59 colleges of engineering in the United States. The contribution of this research is in the form of a visualization mapping the vector space model for word usage and complete vocabulary of pertinent words from the statements analyzed. The preliminary results of this study will help inform current state of vocabulary used in mission statements in the colleges of engineering across the United States. Ultimately, such analyses can help administrators in the development of strategies on the formation of mission and vision statements for universities by allowing insight into vocabulary currently used, to understand what words/terms may not be adequately addressed. Additionally, we are excited to present a contemporary textual data analytical technique of Natural Language Processing using a word vector representation tool such as word2vec for analyzing textual data in the field of engineering education.