I have updated my Firefox Extension, Procrastato. In addition to some small fixes, there’s a new ‘helpful’ mode. While in helpful mode, you’ll get sage advice from Benjamin Franklin rather than the cold, hard stare of Big Brother.
Go the the main page and check it out. I’ve also added it to addons.mozilla.org, but it is still in the sandbox so you’ll need an account to download it.
Here’s a screenshot of Ben in action:
Usability tests can be seen to fall into two general categories, based on their aim: tests which aim to find usability problems with a specific site, and tests which aim to prove or disprove a hypothesis. This test would fall into the former category. A search of the literature will reveal that tests looking to uncover specific usability problems often use a very small number of participants, coming from Nielsen’s (2000) conclusion that five users is enough to find 85 percent of all usability problems. Nielsen derived this formula from earlier work (Nielsen and Landauer, 1993). Although there is much disagreement (Spool and Schroeder, 2001), this rule of thumb has the advantage of fitting the time and money budget of many projects.
In terms of raw data, eye tracking produces an embarrassment of riches. A text export of one test of Mealographer yielded roughly 25 megabytes of data. There are a number of different ways eye tracking data can be interpreted, and the measures can be grouped into measures of search and measures of processing or concentration (Goldberg and Kotval, 1999):
In general, longer, less direct scan paths indicate poor representation (such as bad label text) and confusing layout, and a higher number of fixations and longer fixation duration may indicate that users are having a hard time extracting the information they need (Renshaw, Finlay, Tyfa, and Ward, 2004). Usability studies employing eye tracking data may employ measures that are context-independent such as fixations, fixation durations, total dwell times, and saccadic amplitudes as well as screen position-dependent measures such as dwell time within areas of interest (Goldberg, Stimson, Lewenstein, Scott, and Wichansky, 2002).
Because of the time frame of this investigation, the nature of the study tasks, and the researcher’s inexperience with eye tracking hardware and software, eye tracking data was compiled into “heat maps” based on the number and distribution of fixations. These heat maps are interpreted as a qualitative measure.
Diet can have a great effect on health, but few people keep track of what they eat each day, let alone how much fat, protein, Calcium, or other nutrients. Although most food items have nutrition information printed on the packaging, few people can tell you whether or not the 10 grams of fat in their candy bar is acceptable, or whether it has put them over the edge.
In this project the author assumes that a big part of the reason people do not keep track of their diet is that there is no easy way to do so. The final product of this project is Mealographer, a simple interface that allows users to enter in the foods and meals they eat each day, set simple nutrition goals, and see reports of their progress. Mealographer was created by implementing a large number of improvements to the product of a previous investigation, WhatYouEat. A usability test was conducted to evaluate Mealographer and find specific usability problems.
Mealographer inherits much of its functionality from a previous project, titled WhatYouEat, part of an individual investigation from fall, 2005. The original project had two goals: to create an application that allows users to track their dietary intake, and to make the application as easy to use as possible.
WhatYouEat allowed users to record their meals, set simple goals for different nutrients, and
track their diet through simple reports. Supporting functionality included a simple user sign up and login system, and systems allowing users to indicate favorite foods and “usuals” – foods eaten on a regular basis.
WhatYouEat was demonstrated informally to several groups and an informal usability test was run with four participants. Although this style of evaluation was not rigorous, users were asked to use the site and comment on any confusion or difficulties. Many users also commented on design and additional functionality. Usability issues included difficulty in:
Targeting
Layout
Forms
Labeling
Measurements
Missing items
Note: This was originally created for an information architecture class – the project was to redesign the Kent State School of Library Science web site. You can also see a usability study of the site.
The current Kent State University School of Library Science (SLIS) does not meet the needs of the department. This project outlines a plan and strategy for designing a new site. The new site will better communicate the department’s image and core attributes to the outside world and better meet the needs of users. This report covers the entire process, from research and project goals, through the development of a new design and how to measure success. Major recommendations include the use of a simple content management system (CMS), a new navigation structure and graphic design, and a few new content elements such as news, video, and podcasts.
This report will cover the overall strategy for the redesign of the Kent State University SLIS web site, including the site’s audiences, the vision for the site, and analysis of the content and maintenance. Finally, recommendation are made for the content, information architecture, and design of the new site. The ultimate goal of this project is to create a coherent analysis and plan for the SLIS department to execute. The result will be a site that better projects the image of the department, better serves the users, and, if possible, makes the staff’s job a little bit easier.
Site content has been updated, but the organization and design of the site has been the same since 2000. The web has changed a great deal in the last 5 years, and the Kent SLIS site look and feel is not exactly cutting edge. The faculty and staff have voiced a desire to update the site, and there is anecdotal evidence that at least some students find the site lacking. Any new design must better address the needs of the site’s audiences and should better project the image of the department to the outside world. Also, the process used to update the current site is slow and unwieldy. The new site will solve three main problems: poor ease of use, an image that does not fit the department, and difficulty updating the site and communicating with users.
The process followed in creating this report has included requirements-gathering meetings with SLIS faculty and staff, content analysis of the current site, analysis of server logs, brainstorming sessions with Information Architecture Knowledge Management (IAKM) students, analysis of similar sites, academic usability research, the creation of persona, card sorting exercises, wireframing, prototyping and other techniques. The report will recommend additional steps such as formal usability testing be taken as well.
Note: this was a project for a graduate course in Knowledge Organization Systems
Metadata schema for radiological terrorism research (MSRTR)
Terrorism research is a complex field dealing with a number of entities, each with their own metadata requirements. This document is an introduction to the kinds of schema that will be necessary for proper cataloging, identification, and retrieval in the radiological terrorism subfield. Schema for radioactive material sources and radiological terrorism responses are presented below, followed by sample records and a crosswalk between the two scheme and the Dublin Core. Schema were made as simple as possible (8 and 6 main fields, with several qualifiers, respectively) in order to make application quick, easy and consistent.
Fields are described in the following format:
Domain
The ontology was created from the Radiological Terrorism Research Thesaurus, specifically constrained to the portions under the term “material sources” and “consequence management” (now called response). Other classes not found in these areas, but referenced by fields in these areas, are included, but not developed—this includes Organization, Event, Expertise, Person, and Material and their subclasses.
Background
Terrorism is an incredibly important issue, and agencies within the US and worldwide need to meet the challenge of compiling and organizing research in a number of fields in order to counter this very real threat. In addition, agencies have been criticized in the past for not sharing information, or maintaining knowledge organization systems (KOS) which are incompatible with each other. Work is often duplicated, and often vital information will be unavailable to some agencies even though it has already been archived by others.
Clearly, there is a need for a large-scale KOS that can be used to organize information efficiently and correctly, allow for complex analysis of information, and allow for easy knowledge sharing between agencies. The most flexible and powerful KOS, and therefore the most appropriate, is an ontology. Classes, subclasses and relationships are developed and then appropriate fields are created for each. This allows for faceted search and display, automated search, hierarchical organization of information, and interoperability with other systems.
Users
This is just a sample of the larger, more complete ontology. The complete ontology would be useful for virtually any person or agency dealing with anti-terrorism, counterterrorism, intelligence or consequence management. The ontology will allow risk assessment officers, for example, to see a list of every high-level material source in the United States and Canada and their coordinates. Medical first responders could use it to catalog and retrieve proper treatments for specific bioterrorism agents. And if widely-adopted, it would greatly reduce the barriers to efficient knowledge-sharing. If the Department of Energy we to license a new Uranium mine in Montana, the information would be immediate available to risk-assessment officers, instead of requiring time for the paperwork to make its way over to the Department of Homeland Security.
Changes in this Edition
A number of changes have been made in this revision. Changes to scope notes, terms, and related terms are highlighted throughout this document. These changes should clarify the precise meaning and use. Sturctural changes to broader and narrower term relationships are explained below.
One of the major structural changes is the removal of “radiological terrorism” as a root word for the entire thesaurus. Putting everything under one term was not my initial idea, but the use of the hierarchical display for both input and output lead me to think that was the preferred structure. I have removed “combating radiological terrorism,” “environmental effects,” “radiation protection,” “radioactive isotopes,” “radioactive material sources,” and “radiological injuries” from under “radiological terrorism.”
Still, I think “radiological terrorism goals,” “radiological terrorism scenarios,” and “radiological terrorism requirements” are necessary parts of “radiological terrorism,” so I have kept the first two in the hierarchy and added the third. This leads to multiple inheritance for “radiological terrorism requirements,” which is both a necessary part of “radiological terrorism” and “intelligence.”
Introduction
The CTRS Radiological Terrorism Thesaurus contains descriptive terms used throughout radiological terrorism literature. The terms, their relationships, and their use were culled from several documents, including:
The thesaurus is presented in three forms: first, an alphabetical display of all included terms, including scope notes, preferred terms and synonyms, broader, narrower and related terms, and any scope notes; second, a hierarchical display of preferred terms only; and third, a rotated display of all terms.
Several relationships may be defined for any term in the thesaurus. Scope Notes (SN) are more detailed descriptions of a term’s use when necessary. A preferred term (USE) is a synonym for the term that has been selected for most uses—non-preferred terms do not show up in the hierarchical view. A non-preferred term (UF) is a synonym that may be found in the literature but is not used in the hierarchy. Broader terms (BT) are terms that represent more general classes of the current term. Narrower terms (NT) represent more specific instances or parts of the current term. Finally, related terms (RT) are related to the current term but not in any of the ways already noted.
Note: this was a project for a graduate course in Knowledge Organization Systems
Introduction
The goal of this project is to create a Knowledge Organization System (KOS) for a Greeting Card Company Studio archive so that designers are able to find source artwork and previous designs. This is no small task–Greeting Card Company has been in operation for nearly 100 years and has at least partial archives from the entire period, and today the company employs hundreds of designers and produces thousands of products. There is no question that without an inclusive, accurate, and easy-to-use archive, designers are unable to build on each others ideas and a great deal of work is being duplicated. Also, intellectual property needs to be properly managed and licensed artwork needs to be tracked and protected from accidental misuse.
Currently, all archives are stored in protective containers in the Studio, shelved by year. In addition a vast number of digital files have been compiled on the Studio’s serves and CD and tape backups. This project does not address the physical process of collection and digitization, but instead offers a road map to how items will be classified as they are entered into the system. This KOS also provides a framework for the database and the ultimate user interface.
Below is an analysis of the users and groups, followed by a description of the overall structure of the KOS. After that is a description of each facet, followed by pick lists, synonym rings, and taxonomies for each where applicable.
Users
In this analysis three distinct user groups were identified: Archivists, Designers, and Management/Administration. Archivists include the companies current information professionals as well as the interns and temp workers who will be doing the digitization and data entry under their supervision. The KOS has been set up under the assumption that most data entry personnel will be able to properly classify perhaps 80 to 90 percent of all items within each facet, forwarding the rest to more skilled information professionals. The professionals include skilled librarians, art historians, and other researchers who should be adequately prepared to train data entry personnel and classify more difficult items.
The designer group includes artists and graphic designers of varying skill and experience. Nearly all, however, have completed at least a two-year program and the majority have completed a four-year college degree. Taxonomies were developed with this level of expertise in mind. Designers were surveyed and a wide range of thinking about art objects and designs were found. The facets below were designed to cover virtually every way in which a designer might want to look for a piece.
Management and administration also have specific needs. It is for them primarily that the Designer entity described below as well as most facets dealing with licensing and sales have been created.
Organization
The archive needs to be broken down into four different logical entities: Art Elements (such as clip art, photographs, sculptures, etc.), Products (such as individual greeting cards, e-cards, etc.), Digital Files, and Designers. Each entity will have a number of associated facets which roughly correspond to the fields in the database and will allow multiple methods of search and organization.
The entity relationships will be defined in the database so that searches will cascade upward. For example, some searching for art elements will be able to find those done by a specific AG department, because Art Elements are related to products which are related to Designers, who have the Department/Team facet. All of this is relatively simple to do with SQL and can be hidden in the interface to make searching easier.
Each facet has an associated type, whether that be a simple constraint on an open text field, a pick list, or a taxonomy. Where lists and taxonomies have been developed the list’s page number is noted as well.
View the KOS, including the entities and their facets, pick lists, and taxonomies [pdf]
