Helen Ashman

Introduction. Most university students depend significantly, sometimes exclusively, on the Google search engine for their academic information needs. User satisfaction leads to users’ deeper engagement with an information system that is shown to improve learning in an educational setting. This paper evaluates students’ satisfaction with results from personalised Web search against non-personalised Web search. Method. During semi-structured study sessions, twenty-eight participants (university students) were required to complete a series of search tasks using both personalised and non-personalised Web search. Analysis. Evaluation was based on participants’ explicit feedback as well as their implicit behaviour including search time, number of queries and clicked result links per task, finding the answer and relevance of the search results. Results. There was no apparent significant increase in the participants’ overall level of satisfaction with personalised search results compared to non-personalised results. However, it was found that personalised search reduced the time spent to finish a task and reduced the number of clicks required to arrive at the selected outcome. Conclusions. Personalisation of search results does not increase students' satisfaction with their search results. However, it does reduce the time spent by students in locating information they judged to be satisfactory answers to their questions.

In an era of information overload, people often work together on information‐intensive projects. Group holidaying is such an example where people plan and conduct trips together. In this poster paper we present preliminary findings from an ongoing investigation of collaborative information behaviour of group holidaymakers. Through a grounded‐theory approach, preliminary findings show that not all members of the travel group engage equally in the information search. A role labelled “active followers” is identified and the role‐taker's contribution to the group's information seeking and decision‐making is discussed. This paper calls for attention to heterogeneity within collaborating groups and invites further discussion of research into this topic in information science community.

Profiling and identifying individual users is an approach for intrusion detection in a computer system. User profiles are important in many applications since they record highly user-specific information - profiles are basically built to record information about users or for users to share experiences with each other. This research extends previous research on re-authenticating users with their user profiles. This research focuses on the potential to add psychometric user characteristics into the user model so as to be able to detect unauthorized users who may be masquerading as a genuine user. There are five participants involved in the investigation for formal language user identification. Additionally, we analyze the natural language of two famous writers, Jane Austen & William Shakespeare, in their written works to determine if the same principles can be applied to natural language use. This research used the n-gram analysis method for characterizing user’s style, and can potentially provide accurate user identification. As a result, n-gram analysis of a user's typed inputs offers another method for intrusion detection as it may be able to both positively and negatively identify users. The contribution of this research is to assess the use of a user’s writing styles in both formal language and natural language as a user profile characteristic that could enable intrusion detection where intruders masquerade as real users.

While the phrase 'Fake News' has only recently entered the contemporary vernacular, the problem of other fake content such as reviews or opinion articles is and has been pervasive for many years. False articles and reviews have been used to influence purchasers of goods and services in online commercial and review sites, who have responded by removing such articles and reviews. In response, some promoters employ an alternative promotion method where genuine articles and product reviews are exploited using reader voting mechanisms to promote or suppress the visibility of those articles and reviews. This paper measures the effect of vote manipulation on article visibility and user engagement by comparing sets of threads on Reddit whose visibility is artificially increased. The manipulated threads are compared to sets of control threads. The thread sets were selected from two high-activity subreddits: the apolitical AskReddit and the highly-political The_Donald. The results show that vote manipulation has a significant impact on the visibility of the threads on both subreddits. Clearly, remedial action to detect and deny vote manipulation is needed by sites that allow voting by users.

Advances in technologies including development of smartphone features have contributed to the growth of mobile applications, including dating apps. However, online dating services can be misused. To support law enforcement investigations, a forensic taxonomy that provides a systematic classification of forensic artifacts from Windows Phone 8 (WP8) dating apps is presented in this study. The taxonomy has three categories, namely: Apps Categories, Artifacts Categories, and Data Partition Categories. This taxonomy is built based on the findings from a case study of 28 mobile dating apps, using mobile forensic tools. The dating app taxonomy can be used to inform future studies of dating and related apps, such as those from Android and iOS platforms.

Communication apps can be an important source of evidence in a forensic investigation (e.g., in the investigation of a drug trafficking or terrorism case where the communications apps were used by the accused persons during the transactions or planning activities). This study presents the first evidence-based forensic taxonomy of Windows Phone communication apps, using an existing two-dimensional Android forensic taxonomy as a baseline. Specifically, 30 Windows Phone communication apps, including Instant Messaging (IM) and Voice over IP (VoIP) apps, are examined. Artifacts extracted using physical acquisition are analyzed, and seven digital evidence objects of forensic interest are identified, namely: Call Log, Chats, Contacts, Locations, Installed Applications, SMSs and User Accounts. Findings from this study would help to facilitate timely and effective forensic investigations involving Windows Phone communication apps.

The use of recorded lecture videos (RLVs) in mathematics instruction continues to advance. Prior research at the post-secondary level has indicated a tendency for RLV use in mathematics to be negatively correlated with academic performance, although it is unclear whether this is because regular users are generally weaker mathematics students or because RLV use is somehow depressing student learning. Through the lens of cognitive engagement, a quasi-experimental pre- and post-test design study was conducted to investigate the latter possibility. Cognitive engagement was operationalized using the Revised Two-Factor Study Process Questionnaire (R-SPQ-2F), which measures learning approaches on two major scales: surface and deep. In two mathematics courses at two universities, in Australia and the UK, participants were administered the questionnaire near the course start and finish. Overall findings were similar in both contexts: a reduction in live lecture attendance coupled with a dependence on RLVs was associated with an increase in surface approaches to learning. This study has important implications for future pedagogical development and adds to the sense of urgency regarding research into best practices using RLVs in mathematics.