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Digital Gestures



Abhijit Rao

Manipal Institute of Technology

Department of Computer Engineering

Manipal, India



Digital Gestures is one term that has created a lot of confusion amongst several technologists. Digital gestures is nothing but a representation of a group of activities that leads to better understanding of a customer/user when in the digital environment like the World Wide Web. In order to recognize these gestures we need to record their behavior. We start by understanding what recording user behavior is all about. Then we look into some of the Automated Data Collection Methods which help us record user behavior.


Recording User behavior highlights some of the tendencies, habits and preferences of users. The analysis of such behavior will always help companies better serve their customers. This understanding will also lead to personalization and customization of user interface. When a user expresses more interest in one direction, the company without explicitly asking the user can give an improved user experience.

The difficulties of recording this behavior are:

Users of Web sites can take numerous paths to reach their goal. It is difficult to develop a short-hand for identifying so many paths.

Links and buttons that have similar names but different destinations are rampant on the Web. We must record these user choices accurately for later analysis, yet, a short-hand for identifying links and buttons is difficult with so many similar-sounding names.

Users can traverse many individual web pages to reach their destinations, and recording these locations is important for determining where a problem exists. However, recording web page titles is difficult with pages that lack titles or have wordy or awkward titles.

Web-page users often cycle through pages repeatedly, trying to get to their desired destinations. Recording return visits is not only important for identifying where users are getting lost or confused, but also difficult because users tend to speed up when they repeat steps they have already taken.

Recording detailed behavior on dynamically generated pages is a challenge in real time, especially for unanticipated pages created “on the fly.” An accurate recording of events leading to display of that page is crucial to replicating the user’s behavior, should the observer’s notes about that page lack sufficient detail.

Automated Data Collection Methods

Automated methods for recording user behavior are popular with today’s usability professionals. These methods include videotaping, Lotus ScreenCam recording, data-logging software, and server log files. All these methods reduce how much note-taking the usability specialist must perform during the session. However, they do not meet all the criteria for collecting accurate data that will be available for immediate analysis and reporting.


The advantage of videotaping, for both Web-based and non-Web-based software, is it records fast-paced user activity that handwritten notes might miss. It also captures user commentary and cursor-pointing behavior. Thus it promotes accuracy and completeness. However, a single fixed video camera may not capture all user behavior. Most important, this method delays results reporting, because the usability specialist must review tapes to formulate a complete set of data to analyze. Depending on whether you take notes from the videotape or actually transcribe it, compiling data from videotapes can take from one to eight hours per hour of tape.

Screencam recording

Like videotaping, this method records fast-paced user activity accurately and completely. While it captures cursor-pointing behavior, it does not capture user commentary. Thus it is slightly less complete than videotaping. Reviewing recordings also delays reporting of results.

Data-logging software

The advantages of using data-logging software are that the note-taker can record textual notes more quickly than writing by hand, and can code observations into categories such as “Error” or “Observer Comment”. These advantages mean the data is more complete and already somewhat sortable into categories for more immediate analysis. This method still does not solve the problem of labeling user choices at the user’s pace.

Server log files

Server log files are records of web server activity. They provide details about file requests to a web server and the server response to those requests. In the access log, which is the main log file, each line describes the source of a request, the file requested, the date and time of the request, the content type and length of the transferred file, and other data such as errors and the identity of referring pages.

This automated method is unique to Web software, and its advantage is it records a lot of detail—so much so that one might think every keystroke is captured. However, log files in fact miss important information: client-side events such as pages displayed from cache (return visits), cursor-pointing behavior, and JavaScript activity. Equally disadvantageous are the large amounts of time required to synthesize the individual records into episodes. Until automated tools are developed that can convert videotaped sessions or electronically captured user keystroke sequences into organized tabulations of task episodes—complete with timing information and user commentary—additional note-taking will be required, and time will be needed to convert the recorded material into a database of observations for analysis. For now, we seek the simplest method that meets our requirements.

The Ideal Log File

The ideal log file contains data you can use to learn:

Who is visiting your site. You want unique visitor identification so you know whether a visitor is returning to your site.

The path visitors take through your pages. With knowledge of each page a visitor viewed and the order, you can identify trends in how visitors navigate through your pages. You also want to know what element (link, icon) a visitor clicked on each page to go to the next page.

How much time visitors spend on each page. A pattern of lengthy viewing time on a page might lead you to deduce the page is very interesting—or very confusing.

Where visitors are leaving your site. The last page a visitor viewed before leaving your site might be a logical place to end the visit, or it might be a place where the visitor bailed out.

The success of users’ experiences at your site. Purchases transacted, downloads completed, and information viewed are concrete indicators of tasks accomplished.


These are some of the methods of automated data collection. There is always a debate wherein we discuss the credibility and functionality of these collection methods. We always find it difficult to answer questions like- Does automated data collection really benefit the user and the company? What are the procedures of data storage? How can we reduce the amount of data collected? If we can find reasonable answers to these issues then we needn’t hesitate to call it a technological paradigm.


page upload: 10th September 2001