Usability trends in an online help system: user testing on three releases of help for a visual programming language

Krull, Robert; Friauf, J.; Brown-Grant, J.; Eaton, Angela

doi:10.1109/ipcc.2001.971547

Cited by 6 publications

(4 citation statements)

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“…Houghton (1984) identified the types of assistance offered in online help systems at that time: command, help and error assistance, prompting, online tutorials, and online documentation. Krull, Friauf, Brown‐Grant, and Eaton (2001) tested different phrases of an online help system and concluded that none of the help system features can thoroughly assist users to successfully accomplish their tasks, and they also identified the problems of help organization and help vocabulary. Grayling (2002) suggested providing a context‐sensitive and embedded user‐assistance system based on a usability study.…”

Section: Related Literaturementioning

confidence: 99%

On extensions of k‐means clustering for automated gating of flow cytometry data

Luta

2010

Cytometry Pt A

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RECENT advances in high-throughput flow cytometry (FCM) technology require the theoretical development and the efficient computational implementation of new methods for automated identification of cell populations. Compared with manual gating methods, the current de facto gold standard, these automated methods are expected not only to be faster, but also to increase the reproducibility of data analysis pipelines.According to a very recent comprehensive survey of FCM data analysis methods by Bashashati and Brinkman (1), automated gating methods can be used to identify both known and unknown cell populations, with the latter including the case of subpopulations that cannot be easily identified using two-dimensional manual gating methods. To be able to properly perform the unsupervised automated gating of FCM data, general clustering methods need to fulfill several criteria, such as computational efficiency (to handle the commonly encountered very large data sets in a practically reasonable amount of time), robustness to the shape of the clusters (from spherical to concave cell populations such as ''banana-shaped'' populations) or the density of the clusters (from very sparse to very dense cell populations, depending on the type of gating to be performed), and ability to identify the (generally unknown) number of populations (1).The article by Aghaeepour et al. (2) published in this issue makes an important contribution to the field by introducing flowMeans, a fast automated gating method based on an extension of k-means clustering. It is important to note that the new method has been specifically developed to address several problematic aspects relating to the application of the kmeans clustering to FCM data. These limitations include the identification of the number of populations (k), the sensitivity of the clustering results to the initial values, and implicit restriction to spherical cell populations, with the last limitation particularly relevant to FCM data. The flowMeans method solves the two problems of identifying k and dealing with concave cell populations by starting with a larger number of clusters (by using a ''reasonable'' upper bound for k) and merging them to allow multiple overlapping clusters to represent the same subpopulation.To go into the specific details of the method, flowMeans starts by estimating the number of modes for each one-dimensional projection of the FCM data, using an approach based on kernel density estimation as described by Duong et al. (3). The total number of modes across all dimensions is used as a maximum for k, given that this sum is an overestimate of the number of subpopulations from the multidimensional space. Because there are more clusters than needed, the resulting clusters have to be further merged to determine the number of subpopulations, and the process is iterated by alternating between calculating the distances between pairs of clusters and merging the closest pair of clusters. The underlying empirical principle is that keeping track of the minimum distance...

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Section: Related Literaturementioning

confidence: 99%

On extensions of k‐means clustering for automated gating of flow cytometry data

Luta

2010

Cytometry Pt A

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“…Houghton (1984) identified the types of assistance offered in online help systems at that time: command, help and error assistance, prompting, online tutorials, and online documentation. Krull, Friauf, Brown-Grant, and Eaton (2001) tested different phrases of an online help system and concluded that none of the help system features can thoroughly assist users to successfully accomplish their tasks, and they also identified the problems of help organization and help vocabulary. Grayling (2002) suggested providing a context-sensitive and embedded user-assistance system based on a usability study.…”

Section: Help Use In Different Ir Systemsmentioning

confidence: 99%

Understanding help seeking within the context of searching digital libraries

Xie

Cool

2008

J. Am. Soc. Inf. Sci.

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To date, there has been little empirical research investigating the specific types of help-seeking situations that arise when people interact with information in new searching environments such as digital libraries. This article reports the results of a project focusing on the identification of different types of help-seeking situations, along with types of factors that precipitate them among searchers of two different digital libraries. Participants (N = 120) representing the general public in Milwaukee and New York City were selected for this study. Based on the analysis of multiple sources of data, the authors identify 15 types of help-seeking situations among this sample of novice digital library users. These situations are related to the searching activities involved in getting started, identifying relevant digital collections, browsing for information, constructing search statements, refining searches, monitoring searches, and evaluating results. Multiple factors that determine the occurrences of each type of help-seeking situation also are identified. The article concludes with a model that represents user, system, task, and interaction outcome as codeterminates in the formation of help-seeking situations, and presents the theoretical and practical implications of the study results.

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“…Automated help systems are referred to by a variety of terms in the IR literature. As summarized by Jansen and McNeese (2005), IR researchers “refer to systems designed to assist the user in overcoming searching issues or better utilizing advanced searching methods by a variety of names including intelligent IR systems, explanation systems, intelligent IR interfaces, contextual help systems, recommender systems and relevance feedback systems (Jansen and McNeese, 2005, pp.1481).” Automated Help has been designed, implemented, and tested (Grayling, 2002; Krull, et al, 2001). Although automated Help has received much attention in a variety of contexts, the Help‐seeking situation has been studied far less.…”

Section: Related Literaturementioning

confidence: 99%