“Evaluating Usability Throughout Design and Development” – Joseph Dumas & Janice Redish

Date : October 18, 2009

Joseph Dumas & Janice Redish analyze the potential benefits of combining various evaluation methods as complementary tools for usability testing in the context of usability engineering.

Getting experts to review the design

Calling in an Expert allows for a heuristic evaluation of a product’s features. The authors give the example Nielsen and Molich (1990) who, given statistical finding recommend that an interface be evaluated by a small group of 3 to 5 engineers, and provided a set of guidelines for evaluating usability. Those are: Use simple and natural language; Speak the user’s language; Minimize user memory load; Be consistent; Provide feedback; Provide clearly marked exits; Provide shortcuts; Provide good error messages; and Prevent errors. (p.65)

The advantages of heuristic evaluations are that they reveal local problems, whereas usability testing reveals global problems that users may encounter. The authors suggest using both methods to eliminate the most usability problems.

Having peers or experts walk through the design

Walkthroughs are somewhat similar to cognitive walkthroughs, as they involve a group of peers and usability experts analyzing the steps-requirement for accomplishing a task and find solutions for minimizing the time-cost and simplifying the complexity of that task. To distinguish the two variations: walkthroughs help revisit a design concept and determine programming alterations and layout, whereas cognitive walkthroughs are usually conducted before any prototype implementation. Cognitive walkthroughs focus on the human factor in terms of user expectations and goals.

For Redish and Dumas, “walkthroughs are less effective at finding usability problems than other evaluation methods such as heuristic evaluations”. (p.69)

Having users work with prototypes (static or interactive)

Prototypes usually come in two basic forms: static (paper-based) or interactive (software-based). Those have their advantages and disadvantages.

Using static prototypes will mean that one person will be playing the computer or doing a screenplay mimicking the interactivity of a finished product. This interplay between participant, and paper helps moderators find hierarchical issues and language interpretation in terms of features available and the wording assigned to them in the sketches.

Interactive prototypes allows participants to interact with a ‘seemingly’ working interface. The prototypes facilitate the incorporation of user feedback, exploration of several design concepts, and the evaluation of several iterations. (p.72)

Prototypes can also be tested in 3 ways: horizontal (surface interface), vertical (small number of paths available), or scenario prototypes (selected tasks made fully functional).

Getting user edits on early versions of documentation

Conducting a usability test in the early stages of a design helps save time and money in the long run for both designers and engineers, as rapid changes can be made and less work-load will be put on design alterations and programming modifications. However, it is important to test the intended end-users in order to make the right decisions in due time.

As seen in the Atlas (1981), Redish and Dumas define user edit as involving a user, a specific task, a set of product instructions, and an observer. A variation of this method is usability edit (Soderston, 1985) defined as involving a user, written material, a camcorder and/or observer, and a system (ie a computer). Here, users not only look at the material but also interact with the information while thinking aloud. This method of having real text helps moderators evaluate the effectiveness and efficiency of both “commission” and “omission.” (p.77)

Comparing usability testing with other usability evaluation methods

Finally, it is suggested that you combine usability testing with other evaluation methods to augment complement the advantages found in different approaches and diminish the risk of leaving out local problems that can make your product inadequate for your end-user.

After comparing different styles of combining methods, the authors conclude that: (p.82)
– Usability testing uncovers more usability problems, finds more global problems, and more unique problems than other evaluation methods.
– Usability testing uncovers less local problems and takes more hours to conduct than other methods, but is cost effective in the long-run.
– Heuristic evaluations are better at uncovering usability problems than walkthroughs, are more effective when conducted by several experts working independently, and are better at uncovering local problems.

Again, they emphasize the need to conduct both usability tests and heuristic evaluations, as together they help uncover the most usability problems.

related articles

Frog’s Research Learning Spiral (How the Why of data wins over the What)

Category : Designing for Usability, Interface Research, Research in Design Practice

As clients are becoming increasingly aware of ethnographic-ish research being an essential and integral part of the design process for a high quality user experience offer, it is important for us (designers) to learn an appropriate format for approaching and customizing research on a project basis.

Frog’s Research Learning Spiral, as David Sherwin names it in A Five-Step Process for Conducting User Research, allows us to think of research as not only a set of insight-focused methodologies and collaborative practices, but also very much so as a process of articulating and defining the focus area and scope of the research itself through its 5 learning stages: Objectives, Hypotheses, Methods, Conduct, and Synthesis.

I would like to focus on the the 3 early stages of the spiral. These are fundamental in situating the research area and addressing design questions with methodologies geared towards feeding our knowledge lexicon of people and things in their habitual contexts according to pre-defined objectives and hypotheses.

1 • Objectives focus on the framing of questions following the 3 Ws and an H structure: Who What When Where Why and How. These together help us define who the demographic user base is, what activities they might be involved in while using our service or product, when they would be engaged in such activities, where these activities would take place, under which emotional or rational states (why), and using which processes. These questions are in turn reformulated into simple statements of research objectives, which outline the scope of the research effort.

2 • Hypotheses are assumption packed opinions or suppositions we have about a product or service, its users, and the contextual settings in which the product acts, which are meant to be tested and challenged. Sherwin lists 3 types of hypotheses: attitude (what users would like to get out of a service), behavior (what users would like to do with the service), and feature (which feature users would most enjoy using).

3 • Methods — such as contextual enquiry, surveys, interviews, and benchmarking — can help prove or disprove these hypotheses by revealing key data about a demographic user, their contextual environments, and identify leverage points wherein design can affect their everyday and provide positive change or support. Other more participatory activities which involve probing users — such as diary studies, card sorting, and paper prototyping — can serve as experiential idea generating methods with a capacity for drawing design solutions and concepts that meet the user’s needs and mental models. Finally, evaluative methods — such as usability testing, heuristic evaluations, and cognitive walkthroughs — will demonstrate whether these ideas are effective, useful, and desirable.

[F]inding meaning in your data […] can mean reading between the lines and not taking a quote or something observed at face value. The why behind a piece of data is always more important than the what.” — Lauren Serota, Associate Creative Director at Frog Design

According to Sherwin, data tells us what and when users do things, but not why. Context is in fact king. Integrating such framework for user research helps provide us with the contextual understanding — the understanding of given demographics’ everydayness — for making more informed design decisions.

I am particularly interested in the name “learning spiral”: a looping process that doesn’t need to be lengthy, costly, and a unique event. It is spiral and has the potential of being a cyclical and iterative process, which can be applied as needed at different stages of a design process and with a different scope. That spiral from which I can learn allows me to investigate more specific areas of my users’ everyday by defining learning objectives.

While every research endeavour has a plan and objectives, i particularly found this interpretation because it gives importance to the planning and framing of research and integrates objectives definition as part of the research itself. Typically research seems to begin with contextual inquiry and interviewing right away as a recourse to inform the design approach and concept which does not necessarily end up being a solution that is desirable. Involving research participants in the framing of the research seems to be a more inclusive and humane approach that is bound to have a worthwhile and desirable quality.

— source: http://bit.ly/1eCoUyH

“A Human-Centered Technology.” – Don Norman

Category : Designing for Usability

When we speak of technology what we are often referring to are communication platforms, such as computers and cellular phones. For Don Norman (1993), anything that is created by human beings is a technological artifact, whether physical (tangible objects, i.e. paper) or mental (information structures, i.e. language). Norman divides technological innovations into good and bad, those that “make us smart” by extending human capabilities and those that “make us stupid” by frustrating, captivating, enslaving. His focus involves the understanding of both technologies that extend cognition and those that manipulate cognition.

While innovation has a historical contribution in terms of providing appropriate and efficient tools for society that help enrich human knowledge and enhance mental capabilities (memory, thought, reflection), it has however created modes of entertainment that increasingly promote a consuming rather than a productive people and is accountable for the apparent social divider between the haves and havenots of technology.

Moreover, new technologies create new social practices, specializations and knowledge requirements. This in turn adds a new set of social dividers between those who possess the required skills and those who don’t. The societal implications of new technologies have become more and more complex with the advent of computational systems. Norman’s aim is as follows: “If we learn the reasons for and the properties of these various technologies, then maybe we can control the impact.” His aim is to influence a human-centered approach to technology; a quest for technologies that demonstrate humane properties.

“Toward a Human-Centered View of Technology and People”

As technology becomes increasingly immaterial, its function concerns more and more the fulfillment of abstract needs, such as time-management, decision-making, methodologies, etc. Norman’s critique is that rather than aiding human cognition, technologies today add confusion. He notes that “distractibility”, “illogical” behaviour, and other concurrent characterizations are responses to the rigidness of the way in which computers operate. Those characterizations are related to the constant need for technology to impose a certain type of behavioral and mental effort (i.e. to pay attention, to speak grammatically, etc.). In addition, the information overload creates a feeling of inadequacy in society; such as, the inadequacy of the mind the memorize a certain amount of information, the incapacity to cope with innovation, etc. His premise is that “people err,” and computers don’t, instead they are faulty and the blame is made on the human.

‘To say that people often act illogically is to say that their behavior doesn’t fit the artificial mathematics called “logic” and “decision theory.” ‘

Hence, this adds yet another social divide: machine-centered behavior and human-centered behavior. For Norman, the problem lies in the way technology inadequacy to adapt to the human. Technology would benefit society if it understood more the way in which human cognition works. Norman also criticizes technology for creating an information saturation wherein humans are exposed to too much information that results in a so-called mental “burnout”. To illustrate this side-effect, Norman distinguishes between what technology is able to measure (i.e. hours worked per week) and what it can’t (i.e. satisfaction, rupture, experience), which he respectively refers to as “hard sciences” and ‘ “soft” sciences.’ The “hard sciences” make simple and objective measurements, leaving out what it cannot measure; whereas the ‘ “soft” sciences’ concerns itself with the complexity of subjective measurements, thereby making a link between technology and the living system.

“Two Kinds of Cognition”

The author identifies two kinds of cognition: experiential and reflective. Experiential cognition refers to the way we acquire expert skills through the use of our bodily skills; reflective cognition refers to mental thought processes that inform the development of new concepts and appropriate structures that serve as guides for action. Those are complementary and essential in the sense that they include both inner (mental) and outward (physical) practices.

The point here is that technologies that excite the mind (such as, TV) cannot be a substitute for experience, and hence cannot provide adequate modes of cognitive enhancement. “Experiential thought” is only possible through an active participation and engagement of the world.

Source:
Norman, Don. “A Human-Centered Technology.” Things That Make Us Smart: Defending Human Attributes in the Age of the Machine. Perseus Books, U.S.A., 1993.

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