Knowledge digraph contribution analysis of protocol data

Abstract

A knowledge digraph defines a set of semantic (or syntactic) associative relations among propositions in a text (e.g., the conceptual graph structures of Graesser & Clark, 1985, and the causal network analysis of Trabasso & van den Broek, 1985). This article introduces the Knowledge Digraph Contribution (KDC) data analysis methodology for quantitatively measuring the degree to which a given knowledge digraph can account for the occurrence of specific sequences of propositions in recall, summarization, talk‐aloud, and question‐answering protocol data. KDC data analysis provides statistical tests for selecting the knowledge digraph that “best fits” a given data set. KDC data analysis also allows one to test hypotheses about the relative contributions of each member in a set of knowledge digraphs. The validity of specific knowledge digraph representational assumptions may be evaluated by comparing human protocol data with protocol data generated by sampling from the KDC distribution. Specific concrete examples involving the use of actual human recall protocol data are used to illustrate the KDC data analysis methodology. The limitations of the KDC approach are also briefly discussed.