Individual reading types and the effects of automated annotation

An overwhelming mass of data is available in today’s digital world. The fact that certain data exists does not mean that it is accessible; and if it is accessible, it is not necessarily usable. In this work, noting the prevalence of the web interface to read content online, an augmented web solution is presented to help readers with comprehension: a browser extension producing automated annotation based on dictionaries to assist the reader in finding relevant parts of the document more quickly and comprehending them more easily. What kind of an effect does this kind of an extension have on readers? How do readers employing different reading styles benefit (or not) from its use? Does the extension also make the reader to pay more attention to their own reading types and style? A study of twenty-four participants using the browser extension was conducted. Transcripts from the test sessions and following interviews were analyzed under five themes: reading types; changing relationship with the document; own reading and annotation habits; accuracy, problems, and errors; and suggestions. From the analysis of the data, three types of readers with different characteristics emerge: “Careful reader”, “Jumper” and “Searcher”. They are found to have different motivations for reading; using the extension to enhance the reading experience has different effects for each group as well. The annotations (highlightings) produced by the extension are found to attract readers’ attention; especially the “Searchers” found added visual information valuable. Descriptions of how the readers experienced the annotations are analyzed in detail. Several surprises are also noted: many participants seem to sometimes treat highlighted words as something of a concern; and some participants use text highlighting with the mouse as a transient annotation practice which creates an interesting conflict with the annotations produced by the extension. The study offers ideas for future studies about reading, web augmentation, and digital annotation and identifies several possible directions for future research. An earlier experiment with sentiment analysis on Wikipedia is described as well.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Informatics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2021

Author dc:creator

• Tuomela, Mikko S.

Contributors dc:contributor

• Twidale, Michael B.
• Karahalios, Karrie
• Bruce, Bertram C.
• Marshall, Catherine C.

• digital annotation
• reading
• sentiment analysis
• learning
• highlighting

Statement dc:rights

• Copyright 2020 Mikko Tuomela

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/109350

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/109350