Knowledge Engineering of PhD Stories: A Preliminary Study

Abstract

Support for PhD students and their advisors in decision-making before and along their PhD journeys requires providing them with a deep understanding and knowledge of the life-cycle of a PhD. This means giving them access to a thorough understanding of causal relations between events, decisions, and the possible outcome. This knowledge can be attained primarily from insider stories, study reports, communications threads with advisors and colleagues, interviews, and scholarly databases. However, it is unclear how to give this knowledge a reasonable structure (due to the heterogeneity of concepts and data sources) so that we can use it for decision-making during the PhD journey. In this paper, we explore how to analyze and model PhD stories to uncover and extract causal relationships found within each story to get insights into the co-occurrences and causalities. We analyze these stories with thematic analysis to understand their main points and we use concept maps to create semi-formal graphs of connected events and objects where the relationships are being emphasized from the perspective of cause and effect. Our results at this point are a collection of PhD stories in the form of concept maps, thematic codes, a proposed approach for goal-directed PhD story modeling which we describe in this paper.