Andy Kurtz

Aesthetics and Usability

Sun, 01 Aug 2010 00:00:00 +0000

The philosophical study of aesthetics has been around for centuries, but within human-computer interaction (HCI), aesthetics has been largely ignored until recently. Interface design has traditionally been interested in improving the functionality, efficiency, and effectiveness of applications. In fact, developers have been explicitly warned to not allow functionality to suffer because of aesthetic design.

Background

It is well documented that when a person is observed, the perception of that person is influenced by the person’s appearance. These opinions are formed very quickly, at the first observation, and they tend not to change after more exposure or interaction with the person. Dion et al. proposed that there exists a “what is beautiful is good” stereotype between people in social interaction and most HCI research on aesthetics and HCI, cite the paper by Dion et al. as the basis for the proposition that one’s perceptions of a computer interface can be influenced by aesthetics.

Since humans are susceptible to the influence of the aesthetics of another person, it is not surprising that the influence also pertains to objects. Bloch makes a strong statement about aesthetic product design, saying that the “physical form or design of a product is an unquestioned determinant of its marketplace success.” Similarly, Postrel says that “in a crowded marketplace, aesthetics is often the only way to make a product stand out.” These sentiments are echoed by Coates who says, “the expressions, postures, and gestures arising from a product’s shapes, colors, textures, and all other aspects of its visible form shape what and how we feel about it more than any other factor.”

Research Focus

In HCI research, most definitions of usability center around effectiveness, efficiency, and satisfaction. The categories may be named differently, but in general, most evaluation categories relate to those three standard concepts. Satisfaction only receives a passing note in most definitions and with the exception of a few definitions , aesthetics or other user experience attributes are left out entirely.

This focus on functionality over form has caused designers to ignore or downplayed the importance of aesthetics. But it is important for designers to study aesthetics because it has a strong influence on the opinions people form about the products they use.

Current and past research in the area of aesthetics and usability, centers around aesthetics’ influence on perceptions and how those perceptions change before and after use. Research shows that the aesthetics of an interface can influence pre-use perceptions and in many cases influences perceptions during or after use. Some research has shown that this influence can overshadow usability problems that exist in products and can cause the user to perceive the product as better than it really is.

These results from studies conducted by Tractinsky et al. led them to propose the existence of a ``what is beautiful is usable” stereotype that parallels the work by Dion et al. on the influence of aesthetics on perceptions in social settings.

It is clear that designers should improve the aesthetics of their interfaces to achieve these gains in user perception. But what about experienced usability?

There has been little or no research into the influence aesthetics has on actual use. This is an important question, especially with the increased prominence aesthetics has in design considerations. Most notably, there has been no work in the area of aesthetics and learnability, memorability, and errors, which are important aspects of usability. Also, the components of efficiency and effectiveness have not received sufficient study. Clearly, there needs to be more research into aesthetics and its influence on interaction. My work extends the work done in the area of aesthetics and usability by going beyond perceptions to the previously unexplored area of aesthetics and experienced usability.

Future Work

Most methodologies used to study aesthetics revolve around gathering opinions and preferences from users. This is problematic since asking users about their behavior, opinions, or preferences can influence their thought process and may alter their responses. It is also the case that many times people do not recognize that they are being influenced and cannot explain what caused them to behave the way they did. These are problems that are inherent in opinion-based methods. This research will use measurement and monitoring of the actual use of a system to determine whether aesthetics has an influence on usability. The usability attributes of efficiency, effectiveness, and errors are easy to track through measurement of the users’ performance. Based on previous research that showed how errors and efficiency change over time as users learn how to use a system, learning and memorability can be evaluated through monitoring errors and efficiency. In addition to these measurable attributes that have not been studied before, satisfaction is important and will be studied in this research. While it is not the focus of the research, it will be interesting to see if the previous results related to perceptions can be replicated.

This work will extend the work done in the area of aesthetics and usability by going beyond perceptions to the previously unexplored area of aesthetics and experienced usability. In reverence to the social stereotype of ``what is beautiful is good” that started this chain of research, this future research will ask if beauty is only skin deep.

Lillypad

Mon, 01 Jan 2007 00:00:00 +0000

The LillyPad project explores the use of a PDA for data entry and learning in a group setting. We are working with the Center for Earth and Environmental Science at Indiana University ~ Purdue University, Indianapolis. We created a PDA application to facilitate data entry and learning for their Lilly ARBOR Project. The Lilly ARBOR project is a wetlands restoration project where they are monitoring tree growth and invasive plants and animals.

The LillyPad PDA application performs data entry as well as provides information about the tree and related plants and animals. The data can be used to help identify the trees for measurement and for learning about wetlands reclamation.

Data entry screen

Data entry with number keypad

Tree information

Information about similar trees

Statistics about previous measurements

Graph of tree growth over time

Information about animal damage

Information about invasive vines

Information about good and bad trees that grew on their own

Wireless messaging between groups

Published

Yvonne Rogers, Kay Connelly, Lenore Tedesco, William Hazlewood, Andrew J. Kurtz, Robert E. Hall, Josh Hursey, and Tammy Toscos. Why It’s Worth the Hassle: The Value of In-Situ Studies When Designing Ubicomp. In Proceedings of the 9th International Conference on Ubiquitous Computing, pages 336-353. Springer-Verlag Berlin Heidelberg, 2007 (Nominated for the Best Paper Award)

Why It’s Worth the Hassle: The Value of In-Situ Studies When Designing Ubicomp

Mon, 01 Jan 2007 00:00:00 +0000

Yvonne Rogers, Kay Connelly, Lenore Tedesco, William Hazlewood, Andrew J. Kurtz, Robert E. Hall, Josh Hursey, and Tammy Toscos. Why It’s Worth the Hassle: The Value of In-Situ Studies When Designing Ubicomp. In Proceedings of the 9th International Conference on Ubiquitous Computing, pages 336-353. Springer-Verlag Berlin Heidelberg, 2007 (Nominated for the Best Paper Award)

How should Ubicomp technologies be evaluated? While lab studies are good at sensing aspects of human behavior and revealing usability problems, they are poor at capturing context of use. In-situ studies are good at demonstrating how people appropriate technologies in their intended setting, but are expensive and difficult to conduct. Here, we show how they can be used more productively in the design process. A mobile learning device was developed to support teams of students carrying out scientific inquiry in the field. An initial in-situ study showed it was not used in the way envisioned. A contextualized analysis led to a comprehensive understanding of the user experience, usability and context of use, leading to a substantial redesign. A second in-situ study showed a big improvement in device usability and collaborative learning. We discuss the findings and conclude how in-situ studies can play an important role in the design and evaluation of Ubicomp applications and user experiences.

View at the ACM Digital Library.

Visualizing Dynamic Topic Analysis

Sat, 22 Apr 2006 00:00:00 +0000

Susan C. Herring and Andrew J. Kurtz. Visualizing Dynamic Topic Analysis. In Proceedings of the Social Visualization: Exploring Text, Audio, and Video Interactions Workshop. CHI, 2006.

In this paper we describe a technique for analyzing textual conversations, Dynamic Topic Analysis, and a tool, VisualDTA, for automatically creating visualizations of data coded according to this technique.

View the PDF article.

VisualDTA

Sat, 22 Apr 2006 00:00:00 +0000

Within a conversation, even when there are only two participants, the discussion topic changes over time. It is interesting to analyze the evolution of topics within different types of conversation-such as IRC, IM, and message board conversations-to observe how they are similar or different. Herring (2003) has created a technique for analyzing the coherence of conversation within computer-mediated discourse. This technique is called Dynamic Topic Analysis (DTA) and it provides a way to quantify and visualize the structure of the topic flow within a conversation. A coding scheme is used to quantify the topic drift within a conversation and a visualization based on the coding may be created to make it easier to see the flow of the topics within the conversation.

This research developed an interactive Java application, called VisualDTA, which is used to automatically create a visualization of a DTA coding. In addition to displaying the visualization, VisualDTA provides interactive tools to enhance the topic analysis process. This research is was conducted by Andrew Kurtz and Susan Herring and is based on the visualization described in Dynamic Topic Analysis of Synchronous Chat (Herring, 2003).

Utilizing VisualDTA to analyze a conversation involves creating a DTA coding. Typically the coding would be created in Microsoft Excel and exported to a TAB delimited file. The coding file is loaded into VisualDTA and the visualization is created and displayed. The display is a tree with the root at the top and the children flowing down and to the right. The passing of time in the conversation is represented going down the y-axis. Moving right on the x-axis represents the semantic distance of how off-topic the proposition is from the previous proposition. The propositions are represented by a letter that indicates the relationship between the current proposition and the proposition to which it is responding, called the move (T for “on-topic”, P for “parallel shift”, E for “explanation”, M for “metatalk”, and B for “break”). Propositions that are in reply to a previous proposition are connected to the proposition to which it is responding using a line. A dotted line is used when the connection is tenuous.

When the visualization has been displayed, many interactive options are available.

The visualization may be displayed step-by-step as the conversation unfolds.
A short description for each proposition may be displayed.
Hovering over a proposition with the mouse will display information about that proposition, including any attributes coded such as speaker, gender, and role.
Basic statistics can be shown that includes the average semantic distance and counts for proposition attributes, such as move type, speaker, and gender.
Custom statistics can be shown to relate two attributes. Such as showing gender by role.
Clicking on a proposition allows the user to highlight all of the propositions that match an attribute for the selected proposition. For example, all the propositions by a specific speaker can be highlighted.

The added interactivity provided by the VisualDTA program enhances the analysis that may be performed using the DTA technique.

VisualDTA is freely available for non-commercial use. You can download versions for Mac OS X, Windows, and UNIX.

The main interface

Portion of the screen with a speaker highlighted

Some simple statistics that are generated

Published

Susan C. Herring and Andrew J. Kurtz. Visualizing Dynamic Topic Analysis. In Proceedings of the Social Visualization: Exploring Text, Audio, and Video Interactions Workshop. CHI, 2006.

References

Herring, S. C. (2003). Dynamic Topic Analysis of Synchronous Chat. Paper presented at the Symposium on New Research for New Media, University of Minnesota, Minneapolis.

Newssifter

Tue, 27 May 2003 00:00:00 +0000

This research involved the development and study an intelligent news-sifting client tool that tracks topics in a dynamic online news source. Multilevel interest profiles, utilizing both explicit and implicit feedback, track the user’s interest and are used to customize the news display.

The coarse-grain interest profile tracks interest in the general channels such as “Business” and “Technology”. The fine-grain profile tracks interest in the topics within each channel. Providing the additional fine-grain profile, which is not present in most online news personalization systems, allows the system to more accurately track the users’ interests.

Main Interface

NewsSifter uses the ClariNews online news feed service, distributed through USENET newsgroups. News articles from 384 ClariNews newsgroups are organized into 25 general interest channels and are retrieved and stored for use by NewsSifter. The update frequency of the channels range between 3 and 300 messages a day.

A multilevel interest profile, utilizing both explicit and implicit interest indicators is used to track user interest. When a user starts using the system, they explicitly set their interest level in the channels and the topics within the channels. Over time, the system monitors the user’s behavior and implicitly updates the interest levels in the channels and topics. This allows the system to track the user’s interest without interrupting their news reading experience to get explicit feedback. The user does have the opportunity to give explicit feedback on individual news articles, but this explicit feedback is not required.

The multilevel nature of the profile comes from modeling the users’ interests at the coarse-grain level of the channels and at the fine-grain level of the topics within each channel. The coarse-grain channel profile tracks the users’ interest in the general categories and controls which channels are displayed and the order they are displayed. The fine-grain topic profile tracks the users’ specific interest in the topics within each channel and controls the order the articles are displayed.

The channel profile is created from the 25 general interest categories derived from the ClariNet newsgroups. Categories in the channel profile do not change, but their order may change depending on the explicit and implicit feedback. The channel profile controls the display and order of the channel tabs within the main interface.

The topic profile is dynamically derived from the documents within each channel and controls the display order of the news articles. Vocabulary detection and clustering is performed on the document set to create a profile and is performed using components of the SIFTER system.

The vocabulary detection is performed by calculating tf.idf weights based on the token frequency, sorting them by weight and token, and the tokens that appear in at least D documents and are ranked between 1 and R (R should be a small number to ensure selection of highly weighted terms) are selected.

A heuristic unsupervised clustering algorithm, called Maximin-Distance algorithm, is used to determine the cluster centroids. The centroids are generated in an iterative fashion. The distance between terms is calculated using a cosine similarity based formula.

The NewsSifter system provides a direct manipulation interface where the user sets their interest level in the channels and topics and browses the articles. Before using the system, a user must register with the system, setting up a login name and password to use for each session. The first time a new user logs in, they are prompted to set their interest level in the news channels, which creates initial settings for the channel profile. The user selects the channels they want to read and then sets their interest level in each channel using the sliders.

Edit Channels

Once the user explicitly sets their initial interest in the channels, the system does not prompt them to change their settings. Over time, the system updates the channel profile using implicit feedback. The user may access the settings at any time and explicitly set their current interest levels. When the user selects to explicitly updates the channel profile, the display order of the channels will be sorted based on the current interest levels.

Similarly, the first time a user accesses each channel, the current topic profile is downloaded and the user is prompted to set their initial interest levels in the topics, thus creating a starting point for the topic profile. The word listed as the topic is the token that is the centroid of that cluster. If the user holds the mouse pointer over the topic a tool-tip listing all of the cluster members will be shown.

Edit Channel Interests

As with the channel interest, the user selects the topics they are interested in and sets their interest level using the sliders. The user is prompted to explicitly set their interest only once and over time the system updates the profile based on implicit feedback. The user may decide to explicitly change their topic profile and if they do, the topics are displayed sorted based on the current interest levels.

As time goes on, the user may want to detect new topics and use the new topics to sort the articles. If the user selects the menu item to update the topic profile, the most recent topic profile is downloaded and the user is prompted to set their initial interest level in the new topics.

The channels the user has indicated they are interested in are displayed in a series of tabs with the channel with the highest interest displayed by default and in the left-most tab. The tabs to the right are in decreasing order of interest. Over time, the interest level is updated and the order of the tabs may change. In addition, if the interest level of a channel falls below a threshold it will no longer be displayed in the tabs.

If the user decides they want to read a channel that is not currently displayed, they can either explicitly edit the channel interests through the “Configure” menu or they may select the channel they want to read through the “Channels” menu. When a channel is selected from the “Channels” menu, its interest level is automatically raised above the display threshold and it is added to the tabs. The news articles for the selected channel are classified and sorted based on the topic profile. When an article is double-clicked, it is displayed in a new window.

View Article

The user has the option to give explicit feedback on the article, but they are not required to. If no explicit feedback is given then positive implicit feedback is given since the user chose to view the article. If the user scrolls to view the entire article, the amount of positive implicit feedback is increased.

Requesting explicit feedback when a profile is first accessed provides an accurate starting point for the profile. Updating the profile over time based on implicit feedback allows the profile to evolve with the user’s interest while not interfering with the news reading process by having the system interrupt the user to request explicit feedback. A reinforcing learning algorithm is used for profile update.

Published

Andrew J. Kurtz and Javed Mostafa. Topic detection and interest tracking in a dynamic online news source. In Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries, pages 122-124. IEEE Computer Society, 2003.

Topic detection and interest tracking in a dynamic online news source

Tue, 27 May 2003 00:00:00 +0000

Andrew J. Kurtz and Javed Mostafa. Topic detection and interest tracking in a dynamic online news source. In Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries, pages 122-124. IEEE Computer Society, 2003.

Digital libraries in the news domain may contain frequently updated data. Providing personalized access to such dynamic resources is an important goal. In this paper, we investigate the area of filtering online dynamic news sources based on personal profiles. We experimented with an intelligent news-sifting system that tracks topic development in a dynamic online news source. Vocabulary discovery and clustering are used to expose current news topics. User interest profiles, generated from explicit and implicit feedback are used to customize the news retrieval system’s interface.

View at the ACM Digital Library.