We report quantitative and qualitative results of an empirical evaluation to determine whether automated assistance improves searching performance and when searchers desire system intervention in the search process. Forty participants interacted with two fully functional information retrieval systems in a counterbalanced, within-participant study. The systems were identical in all respects except that one offered automated assistance and the other did not. The study used a client-side automated assistance application, an approximately 500,000-document Text REtrieval Conference content collection, and six topics. Results indicate that automated assistance can improve searching performance. However, the improvement is less dramatic than one might expect, with an approximately 20 % performance increase, as measured by the number of userselected relevant documents. Concerning patterns of interaction, we identified 1,879 occurrences of searcher– system interactions and classified them into 9 major categories and 27 subcategories or states. Results indicate that there are predictable patterns of times when searchers desire and implement searching assistance. The most common three-state pattern is Execute Query–View Results: With Scrolling–View Assistance. Searchers appear receptive to automated assistance; there is a 71 % implementation rate. There does not seem to be a correlation between the use of assistance and previous searching performance. We discuss the implications for the design of information retrieval systems and future research directions.

skills. We report on our observations of the learning of browsing skills and the subsequent iterative development and testing of the Ariadne system – intended to investigate and support the collaborative learning of search skills. A key part of this support is a mechanism for recording an interaction history and providing students with a visualisation of that history that they can reflect and comment upon.

The process of learning to use a new software package is incremental. Users begin learning one function and as the need arises, they acquire skills necessary to use another function. This article develops advent models of this process and tests the models on longitudinal data from more than 300 users of an office automation system called PROFS over a three-year period. The models explain the advent of use of certain PROFS commands in terms of the time since the study participant began using the system, and demographic characteristics such as job title, gender, years of work experience at the institution, and departmental affiliation within the organization. The models show that certain functions such as sending and receiving electronic mail are learned earliest, and that function use does depend on many of the demographic variables. the designers, trainers, managers, and users of systems. Designers need to know if the system is meeting the needs of the users and what kind of deficiencies there are to a particular interface and particular command structure. Trainers need to know what the user is actually doing with the system and, specifically, what kind of problems the user is having with using the system. Managers need information on the extent to which the system is meeting the needs of the users, whether usage warrants continuing to fund the system, and what the future plans for the system should be. Users need the functionality in the system that will let them perform their jobs in an efficient way.

human errors in interacting with IR systems from decision