Examination of Information Problem Decomposition Strategies: A New Perspective for Understanding Users' Information Problems in Search as Learning

When searching for information in search as learning (SAL), users with anomalous state of knowledge often encounter the difficulty in clearly clarifying their information needs. However, it might be easier for them to express their information problems rather than information needs. In this study, we proposed the concept of "information problem decomposition strategies (IPDS)" to help searchers explicitly express their information problems (IPs) as well as to facilitate their information problem solving (IPS) process. To unfold users' dynamic cognitive process within information problem decomposition process during search, we conducted an experiment, and collected searchers' sub-information problems through pre-search and post-search questionnaires, experiment recordings, and post-experiment interview. Furthermore, we also proposed a new measurement of learning performance, information problem solving degree, to evaluate how each sub-information problem has been solved during search in order to assess the search performance from the process perspective. Through experiment, we have successfully collected searchers' decomposed sub-information problems and found that the number of their queries was significantly positively correlated with the number of decomposed sub-information problems. Thus, it is possible to reveal the dynamic cognitive process within users' information search by using sub-information problems. We also summarized 4 typical sub-information problems decomposed categories, namely "What type", "How type", "Why type" and "When type", and identified specific decomposed pathways within each category. It is found that current search systems are not good at supporting sub-information problems on higher levels of cognitive complexity in SAL. However, some of the identified five information problem decomposition strategies (IPDSs) within the decomposed sub-information problems clusters could help increase IPS degree and shed lights on the recommendation terms or directly returned answers of search systems.