Article by Berit Henriksen, Carey Jewitt, Sara Price and Mona Sakr (MODE node, London Knowledge Lab, Institute of Education). This article appears in the Summer 2013 issue of MethodsNews newsletter (opens a .pdf file).
How people interact with one another, objects, and digital environments, is increasingly multimodal. This raises significant challenges for collecting research data, in part because many features of interaction in digital environments cannot be captured adequately using traditional methods or tools (e.g. audio recording, field notes, or still-cameras). Furthermore, the expanding range of online tools for collection and analysis of social media are primarily focused on collecting text-based data and are limited in terms of accessing multimodal data. Here we briefly introduce some alternative devices and applications being explored at MODE, an NCRM node on methods that extend visual and multimodal data sets.
Video has become a primary data collection tool for those researching multimodal environments. In addition to the 'standard' third person researcher generated video, digital video data can be collected via cameras embedded in professional contexts and tools (e.g. surgical light-handles or laparoscopic cameras); wearable sub-cam glasses; body worn cameras; head mounted cameras; and micro cameras embedded in objects. Each of these generates data from different perspectives, shapes the representation of interaction - what is included, excluded or foregrounded - and tends to be embedded in different theoretical concepts and processes.
MODE is exploring how these different data collection techniques can contribute to researching embodied learning in digital environments, together with investigating the value of digitally generated data and digital apps as data collection tools. For example, the use of a Geographical Position System (GPS) tracker app (on an iPad) was used to provide a cumulative trace of the routes taken by students during the exploration of WWII history and 'condensed time' to produce a spatial narrative of their trail.
Digital apps have also been useful in generating digital data, for example, in the WWII study students used Evernote to create geo-tagged photographs, record audio narratives, and write captions to produce multimodal 'notes' that constituted a narrative linked to time and space. These were used to support student reflection, provide supplementary data to support the analysis of video data, as well as to enrich data sets for analysis.
Dynamic and static screen capture software is proving to be a useful tool for MODE. A study of infant interaction with finger painting Apps on the iPad has used dynamic screen capture (e.g. Quicktime screen record facility, Camtasia) to capture infants' painting process in real-time alongside video recording of their interaction. Other projects on social media have used screen capture tools (e.g. Zotero, Little snapper) to collect whole websites as PDFs or images that can then be analysed in an offline setting. These can be combined with Computer Assisted Qualitative Data Analysis (CAQDAS) packages (e.g. Nvivo) and web browser extensions (e.g. Ncapture or TubeCatcher) to collect webpages, online PDFs and social media content. It can, for example, collect any Twitter-profile page, tweets by a particular user, and tweets that include a particular word, phrase or hashtag, that can then be imported and analyzed using the Nvivo 10 package. Another key resource MODE uses to support the analysis of websites and blogs is 'The Wayback Machine', an archive of websites, containing snapshots of sites linked to dates, which makes it possible to search and analyze the changes made to sites over time. The British Library provides a similar service for with their UK Webarchive.
This brief article indicates the potential of digital technologies for enriching data collection and analysis: explore these further with us at MODE website.