User Interface with tabletMany demographic traits distinguish users. In starting a user interface design analysis, a chief task for the designer is to identify those characteristics most critical to the realization of the specific interface goals.

General Characteristics

General traits are broad identifying variables such as age, gender, work experience, education, and culture. Consider an example of designing tax preparation software for a small business. Try to form initial impressions of some design tactics you might use. Would you use a complex one page data entry form, a tabbed interface, or a step-by-step question Wizard? Or perhaps some combination?

At this point, it would be pure guesswork, but would your first idea change if your application customer told you that the users are all novice interns in their early twenties? What if they were elder volunteers? What if they were CPA accountants with experience in many accounting applications? And, finally, the designer’s recurrent trial: What if the users comprise a combination of experts, novices, and paraprofessionals with varied job and software experiences?

With all these variables, the significance of a needs assessment, or goal analysis in UI design should be clear.

Cognition or Learning Styles

Cognition styles are traits that refer to how individuals approach learning tasks and process information. That is, some people find certain methods of learning more appealing than others. For example, rather than attending lectures and reading textual material, some prefer a more visual approach. Yet others may learn from physical activities and the manipulation of objects. Attempting to identify a person’s unique interaction preference might aid planning for user interface design.

Another category that you should not overlook is users with disabilities. This group could include users with both physical and learning disabilities, like loss or impairment of hearing, vision, and speech.

Personal and Social Characteristics

You should consider personal and social characteristics of users when those characteristics might affect the design and delivery of the application. The following audience demographics are helpful to recognize:

  • Age and maturity level
  • Motivation and attitude
  • Expectations of the application
  • Previous or current employment and work experience
  • Special abilities

Considering this list and your recent UI design experiences. Which variables do you feel are most important? Many designers consider motivation to be the most important factor. Users who are apathetic, or worse, are resistant to the purpose of the application are not likely to respond in the same manner to the UI as would more motivated users. Design strategies that create interest and keep attention would be appropriate for the former group.

Attitude and Preconceived Notions

User attitude is unlike motivation. For example, a teenage boy may wish to play a challenging computer game, but he may feel hesitant that he can master it based on former difficulties. This self-fulfilling prophecy breeds failure by expecting failure.

If the designer finds that such negative attitudes are common for the target user groups, she might instead use approaches to build confidence in the users’ abilities as the application progresses.

One possibility is to start with easy content and increase difficulty over time. B. F. Skinner (1954), employed a similar concept, called, “successive approximations,” or “shaping,” when designing instruction. Of late, researchers have studied having learners complete some of the steps in worked examples, which is like Skinner’s shaping and fading methods (Atkinson & Renkl, 2007).

Although it is important during UI planning and analysis to gather and use general information—academic, personal, and social—about all users, you should also give attention to the above added characteristics of the target audience.

Additional information:

Atkinson, R. K., & Renkl, A. (2007). Interactive example-based learning environments: Using interactive elements to encourage processing of worked examples. Educational Psychology Review, 19, 375-386.

Skinner, B. F. (1954). The science of learning and art of teaching. Harvard Educational Review, 24(1), 86-97.