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Retrieving files on personal computers is a fundamental component of interaction, yet there is surprisingly little empirical data characterising how it is carried out in realistic settings. We developed software, called FileMonitor, to dynamically record users׳ file retrieval activities, including data describing the files retrieved and the tools used to retrieve them. We then deployed the system in a four week log study of 26 participants׳ actual file retrievals on their personal computers. Follow-up interviews contextualised the findings. Results are presented in two sections focusing on the files (the number of files, patterns of revisitation, file types, etc.) and on the interface mechanisms used to retrieve them (file browsers, search tools, ‘recent files’ lists, etc.). We conclude by discussing implications for the design of next-generation file retrieval interfaces.

Navigating to files through a hierarchy is often a slow, laborious, and repetitive task. Recent lab studies showed that file browser interface augmentations, such as Icon Highlights and Search Directed Navigation, have the potential to reduce file retrieval times. However, for this potential to be realised in actual systems, further study is necessary to address two important issues. First, there are important design and implementation challenges in advancing the research prototypes previously evaluated into complete interactive systems that can be used for real work. Second, it is unknown how real users would employ these systems while engaged in actual work; would the potential performance improvements suggested by the earlier lab studies be realised? We therefore describe the design, implementation, and longitudinal field study evaluation of Finder Highlights, a file browser plugin for the OS X 'Finder' that adds support for Icon Highlights and Search Directed Navigation. Study results confirm that the augmentations are effective in reducing real-world file retrieval times, with retrieval times 13% faster when using Finder Highlights compared to the standard tool (10.6 s versus 12.2 s), while also emphasising important differences between lab and field studies. In summary, the paper strongly suggests that large-scale deployment of interface augmentations to file browsers, particularly Icon Highlights, will have a marked effect in improving users' real-world file retrieval.

Navigating through a file hierarchy is one of the most common methods for accessing files, yet it can be slow and repetitive. New algorithms that predict upcoming file accesses have the potential to improve navigation-based file retrieval, but it is unknown how best to present their predictions to users. We present three design goals aiming to improve navigation-based file retrieval interfaces: minimise the time spent at each hierarchical level en route to the target file; reduce the number of levels traversed by providing shortcuts; and promote rehearsal of the retrieval mechanics to facilitate expertise. We introduce three interfaces that augment standard file browsers based on each of these goals: Icon Highlights give greater prominence to predicted items in the current folder; Hover Menus provide shortcuts to predicted folder content; and Search Directed Navigation uses predictive highlighting to guide users through the hierarchy in response to query terms. Results from a user evaluation show that all three interfaces improve file retrieval times, with Icon Highlights and Hover Menus best suited for frequently accessed items and Search Directed Navigation best suited for infrequent ones. We also show that the benefits are larger when folder content is spatially unstable. Finally, we discuss how the interfaces could be combined and deployed in existing file browsers.

Flashcard systems typically help students learn facts (e.g., definitions, names, and dates), relying on intense initial memoriztion with subsequent tests delayed up to days later. This approach does not exploit the short, sparse, and mobile opportunities for microlearning throughout the day, nor does it support learners who need the motivation that comes from successful study sessions. In contrast, our MemReflex system of adaptive flashcards gives fast-feedback by retesting new items in quick succession, dynamically scheduling future tests according to a model of the learner's memory. We evaluate MemReflex across three user studies. In the first two studies, we demonstrate its effectiveness for both audio and text modalities, even while walking and distracted. In the third study of second-language vocabulary learning, we show how MemReflex enhanced learner accuracy, confidence, and perceptions of control and success. Overall, the work suggests new directions for mobile microlearning and "micro activities" in general.

We introduce AccessRank, an algorithm that predicts revisitations and reuse in many contexts, such as file accesses, website visits, window switches, and command lines. AccessRank uses many sources of input to generate its predictions, including recency, frequency, temporal clustering, and time of day. Simulations based on log records of real user interaction across a diverse range of applications show that AccessRank more accurately predicts upcoming accesses than other algorithms. The prediction lists generated by AccessRank are also shown to be more stable than other algorithms that have good predictive capability, which can be important for usability when items are presented in lists as users can rely on their spatial memory for target location. Finally, we present examples of how real world applications might use AccessRank.