A Self-Organising Systems Approach to History-Enriched Digital Objects

Abstract

In discussing distributed cognition as a foundation for research on human–computer interaction, Hollan and colleagues note how digital objects or artifacts (e.g. electronic texts) can maintain histories of interaction. Histories-of-use can be based on users’ explicit actions (e.g. annotating or highlighting) or implicit actions (e.g. time spent reading). These actions can then be processed and used to support indirect social interaction. For instance, the digital object could be augmented with information about how it had been used by others. This creates history-enriched digital objects, or what may be called stigmergic artifacts. These artifacts change to reflect the community’s developing consensus and the actions of future users’ are in turn affected by that emerging consensus. This positive feedback loop leads to the development of a stable consensus, which eventually emerges as a by-product of individual use of the artifact. The history can also be mined for the purpose of gaining a perspective on how the community of users interacted with the artifact over time. When the users are students, this type of data mining may provide an instructor with valuable insights. We designed CoREAD, a software application, to capture readers’ text highlighting and to use participant highlighting to modify the text for subsequent users. The text is modified by adding typographical text signals (CoREAD signals by using colour, whereas authors typically signal text by employing bold and italics). This software maintains a history of users’ highlighting actions at the word level resulting in very large data sets (e.g. 100,000 unique entries for 40 users reading a 2,500-word text). A study of 40 undergraduate students using CoREAD was conducted. The quality of the students’ highlights and their written summaries was determined by comparing these “documents” with the original text and a model summary using Latent Semantic Analysis.