This longitudinal study tracks US and UK participants' shifts in their motivations and forms of engagement with technology and information as they transition between four educational stages. The quantitative and qualitative methods, including ethnographic methods that devote individual attention to the subjects, yield a very rich data set enabling multiple methods of analysis. Instead of reporting general information-seeking habits and technology use, this study explores how the subjects get their information based on the context and situation of their needs during an extended period of time, identifying if and how their behaviors change.
Introduction. A multi-institutional, grant-funded project employed mixed methods to study 175 fourth-grade through graduate school students’ point-of-selection behaviour. The method features the use of simulated search engine results pages to facilitate data collection. Method. Student participants used simulated Google results pages to select resources for a hypothetical school project. Quantitative data on participants’ selection behaviour and qualitative data from their think-aloud protocols were collected. A questionnaire and interviews were used to collect data on participants’ backgrounds and online research experiences. Analysis. This paper reflects on the data collection methods and highlights opportunities for data analysis. The ability to analyse data both qualitatively and quantitatively increases the rigor and depth of findings. Results. The simulation created a realistic yet controlled environment that ensures the comparability of data within and across a wide range of educational stages. Combining data on participants’ behaviour, thoughts and characteristics provides a more complete picture of factors influencing online resource selection. Conclusions. Using simulated results pages in combination with multiple data collection methods enables analyses that create deeper knowledge of participants' information behaviour. Such a complicated research design requires extensive time, expertise and coordination to execute.
The Digital Visitors and Residents (V&R) project developed a mapping exercise to help individuals better understand their engagement with technology and the web. The map creation process and the opportunity for individuals to share their stories in a group setting can be fun, informative and educational. In this paper, we show that collections of V&R maps can be sources of rich and interesting information about similarities and differences in individuals' engagement with technology. Computational methods such as shape analysis and machine learning can be used to distill this information from collections of V&R maps. We present several models, techniques and algorithms for V&R map analysis that may be familiar to many data scientists and software developers, including a shape metric that captures properties of shapes with intuitive meaning and interpretation in the V&R context. Patterns and trends emerging from collections of maps can help a team better understand their similarities and differences in engagement with technology or help inform decisions on the design of information technology or library technology services. We provide examples of these techniques using data collected in various V&R mapping sessions. Finally, we introduce a V&R mapping web application designed for touchscreens that can ease data collection for computational analysis and provide a fun and engaging experience for mapping exercise participants. BACKGROUND Visitors and Residents (V&R) ProjectThe V&R project began as a collaborative effort between OCLC, the University of Oxford and in partnership with the University of North Carolina, Charlotte, with partial funding from JISC. The project attempts to fill in the gap in user behavior studies identified in the JISC Digital Information Seeker Report (2010). In that report, Connaway and Dickey (2010) call for a longitudinal study "to identify how individuals
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