A new integrated model for multitasking during web searching

Investigating multitasking information behaviour, particularly while using the web, has become an increasingly important research area. People s reliance on the web to seek and find information has encouraged a number of researchers to investigate the characteristics of information seeking behaviour and the web seeking strategies used. The current research set out to explore multitasking information behaviour while using the web in relation to people s personal characteristics, working memory, and flow (a state where people feel in control and immersed in the task). Also investigated were the effects of pre-determined knowledge about search tasks and the artefact characteristics. In addition, the study also investigated cognitive states (interactions between the user and the system) and cognitive coordination shifts (the way people change their actions to search effectively) while multitasking on the web. The research was exploratory using a mixed method approach. Thirty University students participated; 10 psychologists, 10 accountants and 10 mechanical engineers. The data collection tools used were: pre and post questionnaires, pre-interviews, a working memory test, a flow state scale test, audio-visual data, web search logs, think aloud data, observation, and the critical decision method. Based on the working memory test, the participants were divided into two groups, those with high scores and those with lower scores. Similarly, participants were divided into two groups based on their flow state scale tests. All participants searched information on the web for four topics: two for which they had prior knowledge and two more without prior knowledge. The results revealed that working memory capacity affects multitasking information behaviour during web searching. For example, the participants in the high working memory group and high flow group had a significantly greater number of cognitive coordination and state shifts than the low working memory group and low flow group. Further, the perception of task complexity was related to working memory capacity; those with low memory capacity thought task complexity increased towards the end of tasks for which they had no prior knowledge compared to tasks for which they had prior knowledge. The results also showed that all participants, regardless of their working memory capacity and flow level, had the same the first frequent cognitive coordination and cognitive state sequences: from strategy to topic. In respect of disciplinary differences, accountants rated task complexity at the end of the web seeking procedure to be statistically less significant for information tasks with prior knowledge compared to the participants from the other disciplines. Moreover, multitasking information behaviour characteristics such as the number of queries, web search sessions and opened tabs/windows during searches has been affected by the disciplines. The findings of the research enabled an exploratory integrated model to be created, which illustrates the nature of multitasking information behaviour when using the web. One other contribution of this research was to develop new more specific and closely grounded definitions of task complexity and artefact characteristics). This new research may influence the creation of more effective web search systems by placing more emphasis on our understanding of the complex cognitive mechanisms of multitasking information behaviour when using the web

Timeout Reached, Session Ends?

Die Identifikation von Sessions zum Verständnis des Benutzerverhaltens ist ein Forschungsgebiet des Web Usage Mining. Definitionen und Konzepte werden seit über 20 Jahren diskutiert. Die Forschung zeigt, dass Session-Identifizierung kein willkürlicher Prozess sein sollte. Es gibt eine fragwürdige Tendenz zu vereinfachten mechanischen Sessions anstelle logischer Segmentierungen. Ziel der Dissertation ist es zu beweisen, wie unterschiedliche Session-Ansätze zu abweichenden Ergebnissen und Interpretationen führen. Die übergreifende Forschungsfrage lautet: Werden sich verschiedene Ansätze zur Session-Identifizierung auf Analyseergebnisse und Machine-Learning-Probleme auswirken? Ein methodischer Rahmen für die Durchführung, den Vergleich und die Evaluation von Sessions wird gegeben. Die Dissertation implementiert 135 Session-Ansätze in einem Jahr (2018) Daten einer deutschen Preisvergleichs-E-Commerce-Plattform. Die Umsetzung umfasst mechanische Konzepte, logische Konstrukte und die Kombination mehrerer Mechaniken. Es wird gezeigt, wie logische Sessions durch Embedding-Algorithmen aus Benutzersequenzen konstruiert werden: mit einem neuartigen Ansatz zur Identifizierung logischer Sessions, bei dem die thematische Nähe von Interaktionen anstelle von Suchanfragen allein verwendet wird. Alle Ansätze werden verglichen und quantitativ beschrieben sowie in drei Machine-Learning-Problemen (wie Recommendation) angewendet. Der Hauptbeitrag dieser Dissertation besteht darin, einen umfassenden Vergleich von Session-Identifikationsalgorithmen bereitzustellen. Die Arbeit bietet eine Methodik zum Implementieren, Analysieren und Evaluieren einer Auswahl von Mechaniken, die es ermöglichen, das Benutzerverhalten und die Auswirkungen von Session-Modellierung besser zu verstehen. Die Ergebnisse zeigen, dass unterschiedlich strukturierte Eingabedaten die Ergebnisse von Algorithmen oder Analysen drastisch verändern können.The identification of sessions as a means of understanding user behaviour is a common research area of web usage mining. Different definitions and concepts have been discussed for over 20 years: Research shows that session identification is not an arbitrary task. There is a tendency towards simplistic mechanical sessions instead of more complex logical segmentations, which is questionable. This dissertation aims to prove how the nature of differing session-identification approaches leads to diverging results and interpretations. The overarching research question asks: will different session-identification approaches impact analysis and machine learning tasks? A comprehensive methodological framework for implementing, comparing and evaluating sessions is given. The dissertation provides implementation guidelines for 135 session-identification approaches utilizing a complete year (2018) of traffic data from a German price-comparison e-commerce platform. The implementation includes mechanical concepts, logical constructs and the combination of multiple methods. It shows how logical sessions were constructed from user sequences by employing embedding algorithms on interaction logs; taking a novel approach to logical session identification by utilizing topical proximity of interactions instead of search queries alone. All approaches are compared and quantitatively described. The application in three machine-learning tasks (such as recommendation) is intended to show that using different sessions as input data has a marked impact on the outcome. The main contribution of this dissertation is to provide a comprehensive comparison of session-identification algorithms. The research provides a methodology to implement, analyse and compare a wide variety of mechanics, allowing to better understand user behaviour and the effects of session modelling. The main results show that differently structured input data may drastically change the results of algorithms or analysis

Designing Search User Interfaces for Visually Impaired Searchers: A User-centred Approach

PhDThe Web has been a blessing for visually impaired users as with the help of assistive technologies such as screen readers, they can access previously inaccessible information independently. However, for screen reader users, web-based information seeking can still be challenging as web pages are mainly designed for visual interaction. This affects visually impaired users’ perception of theWeb as an information space as well as their experience of search interfaces. The aim of this thesis is therefore to consider visually impaired users’ information seeking behaviour, abilities and interactions via screen readers in the design of a search interface to support complex information seeking. We first conduct a review of how visually impaired users navigate the Web using screen readers. We highlight the strategies employed, the challenges encountered and the solutions to enhance web navigation through screen readers. We then investigate the information seeking behaviour of visually impaired users on the Web through an observational study and we compare this behaviour to that of sighted users to examine the impact of screen reader interaction on the information seeking process. To engage visually impaired users in the design process, we propose and evaluate a novel participatory approach based on a narrative scenario and a dialogue-led interaction to verify user requirements and to brainstorm design ideas. The development of the search interface is informed by the requirements gathered from the observational study and is supported through the inclusion of visually impaired users in the design process. We implement and evaluate the proposed search interface with novel features to support visually impaired users for complex information seeking. This thesis shows that considerations for information seeking behaviour and users’ abilities and mode of interaction contribute significantly to the design of search user interfaces to ensure that interface components are accessible as well as usable

STOPPING AND RESUMING: HOW AND WHY DO PEOPLE SEARCH ACROSS SESSIONS FOR COMPLEX TASKS?

Cross-session searches (XSS) occur when people look for information online for multiple sessions to complete complex task goals over time. Previous studies explored aspects of XSS, including the reasons that lead to it, like the Multiple Information Seeking Episode (MISE) model, which highlights eight causes. However, less is known about how these reasons manifest in real-life XSS and their relationship with task characteristics. I conducted a diary study with 25 participants engaging in XSS for real-life tasks. Participants reported on at least three search sessions spanning at least two days, and 15 participants attended an interview after they completed the diary study. We used qualitative methods to explore motivations for expected XSS, goal complexity, session resuming and stopping reasons, types of found information, cognitive activities, and the non-search task activities that happened during the XSS process. Our results validated and refined the MISE session resuming and stopping reasons and distinguished subcategories and reasons unique to real-life XSS tasks. We discerned task-oriented and cognition-oriented motivations for XSS. We identified seven types of non-search task activities and three popular modes describing how people intertwine search and non-search activities during XSS. We assessed relationships among factors, including session goal complexity, information types, cognitive activities, session resuming, and stopping reasons using quantitative methods. Our results show significant associations between information types, cognitive activities, session goal complexity, and session resuming and stopping reasons. Furthermore, task stages significantly correlate with perceived overall task difficulty and the difficulty to find enough information. We also identified five XSS-specific challenges. Our results have implications for tailoring future search engines to customize search results according to session resuming reasons and designing tools to assist task management and preparation for session stops. Methodologically, our results have insights into designing tasks and subtasks and controlling the reasons that can lead to successive searches for tasks with varying complexity.Doctor of Philosoph