Name
Abstract | ||
|---|---|---|
The sensemaking process is a complex task in academic research practice. Scholars annotate on their research resources to reflect impressions, insights and facts, as well as reduce the cognitive load in their working memory as a prerequisite for carrying out such sensemaking processes. Given the data-intensive nature of research and the increased collaboration of often interdisciplinary working teams, a vast amount of annotations with a great extent of knowledge are produced daily. However, the annotation tools extant do not usually support sensemaking of those annotations. Using existing research as a foundation, we define a conceptual model for collaborative sensemaking that is based on human-machine collaboration. We introduce concepts to integrate machine intelligence into the collaborative sensemaking process by allowing users to interact with machine-recommended information in favor of exploring knowledge from collective intelligence through interactive visualizations of annotations. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3022198.3026351 | CSCW Companion |
Keywords | Field | DocType |
sensemaking, semantic annotation, software agent | Annotation,Conceptual model,Collective intelligence,Computer science,Sensemaking,Working memory,Knowledge management,Conceptualization,Software agent,Human–computer interaction,Cognitive load | Conference |
Citations | PageRank | References |
1 | 0.37 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming-Tung Hong | 1 | 2 | 1.40 |
| Claudia Müller-Birn | 2 | 34 | 9.95 |