In UX research, task analysis helps us to learn how users traverse our systems to understand where we might make essential improvements and improve product performance.
According to the Nielsen Norman Group, “Task analysis is the systematic study of how users complete tasks to achieve their goals. This knowledge ensures products and services are designed to efficiently and appropriately support those goals.”
Cognitive task analysis vs. hierarchical task analysis
We recently posted an article about hierarchical task analysis, which, at first glance, looks very similar to what we’re discussing today. Both options require researchers to study how users navigate tasks, presenting them as easily understood visual models, typically a flow chart or structured pathway.
However, there are significant differences between the two processes:
- Cognitive task analysis focuses on the user’s decision-making, assessments, problem-solving, expertise, memory, attention and attention span, and judgements to assess how well a product aligns with their needs.
- Hierarchical task analysis focuses on deconstructing high-level tasks into layers of subtasks to assess system performance.
The word cognitive (derived from cognition) relates to how we think and process information:
Cognition: the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses.
As you’ll have already figured, cognitive task analysis studies are created to uncover the elements that explain our decision-making as we navigate a task, query, or need. We use it to uncover both expert and novice users’ mental models and strategies.
Both hierarchical task analysis and cognitive task analysis can be used in physical situations and environments to determine ideal ways to perform a task in the best way possible. This leads us nicely to the reasons we use cognitive task analysis.
Why do we use cognitive task analysis?
In UX, as in almost all cases, we use cognitive task analysis to investigate the shortfalls of products to improve or enhance them.
It can do far more than that, though. As a psychological research method, it’s often used to explore how the best in their field attack a problem and the mental workload required to complete that task. With a comprehensive flow chart of the process and a detailed explanation of the expert’s mental processes, we can pass that knowledge or experience onto a novice, newcomer, or stranger to the environment. This helps deliver onboarding models and training materials based on the expert knowledge of high performers.
How do we do that? We conduct observational interviews and other think-aloud protocols to explore their cognitive processes and how they frame problems and consider an appropriate solution. We ensure valuable qualitative data collection by asking the right questions during each operational study. Also, our observations show us first-hand any additional tools or resources the experts or other users utilise to achieve goals faster and more efficiently.
This information unlocks the hidden secrets of successfully performing users, allowing us to streamline processes or add new elements that reduce the number of necessary tasks or simplify them for others.
Advantages and disadvantages of cognitive task analysis:
- Provides detailed and precise information on how expert knowledge affects problem-solving, navigation, alternative outcomes, and task success.
- Shows crucial differences between novice and expert performance.
- Uncovers the cognitive demands of task goals and the human factors essential to successful task completion.
- Produces crucial sources of essential information.
- Delivers well-defined systematic procedures for specific tasks.
- Uncovers areas of human error and the cognitive aspects that drive problematic processes.
- It can be time-sensitive and resource-heavy.
- It isn’t guaranteed to uncover the necessary data where non-cognitive attributes aren’t easy to determine, for example, where a user’s physical capability or access to resources affects their performance.
- Results can be misleading when expert users have skillsets unique to their level of performance, for example, where applying specific thinking processes can’t help novice users because they can’t reproduce a mental, physical, or motor skill or skillset that derives from natural talent or years of practice.
When should you carry out cognitive task analysis?
Cognitive task analysis helps us to identify problem points and operational issues at various stages of the design and development processes. It shows us where additional steps or resources would aid operation and areas where we could navigate human error by automating some of those tasks and subtasks by the system.
Therefore, it can be used during early design stages but is more likely used during prototyping or improving an existing design.
How to prepare and perform a cognitive task analysis study
Wherever you are in the UX research process, uncovering essential data is how we drive positive change. To prepare, you must understand what that data looks like and how to obtain and present it.
- What are the triggers that dictate the start of the task journey?
- How do we decide when a user has completed the task successfully?
- What information and knowledge do users already possess before starting a task?
- What information and knowledge do users require to complete a task?
- What tools and resources do users use to complete the task?
Researchers must investigate procedural skills before the practical part of the study, educating themselves on goal structures, decision-making processes, and other tacit knowledge areas before delving into the practices surrounding complex systems and their operations.
2. Performing practical studies or ‘knowledge elicitation’
There are many ways to gather the information required for cognitive task analysis. These include informal observational and interviewing methods that allow plenty of flexibility, process tracing that captures experts’ and/or novices’ performances through a thinking-aloud protocol, or conceptual techniques allowing for more structured interviews around concepts and representations.
Popular knowledge elicitation methods for cognitive task analysis include:
- Critical decision method / critical incident technique
- Cognitive interviewing
- Contextual inquiry
- Team communications analysis
- Cognitive function models
- Systematic collection of verbal reports
- Skill-based CTA frameworks
- Task knowledge structures
- Applied cognitive task analysis
Knowing which methods best suit each application or study comes from experience and expert knowledge.
We recently posted articles on the critical incident technique (how recalling specific events with positive or negative outcomes helps uncover actions, routines, flaws, and common and less common issues) and contextual inquiry (a qualitative observational study carried out in the user’s usual environment), both of which deliver the kind of conversational studies suitable for cognitive task analysis.
3. Identifying tasks and splitting main tasks into subtasks
To define specific goals, we need to identify the personas, scenarios, and tasks we expect to deliver valuable data, specifically each user’s goals, motivations, and thought processes. Identifying decisions relating to specific tasks allows us to assess the mental workload and cognitive demands placed on the user.
4. Analysing the data – creating a visual representation and narrative of the task and subtasks
Having carried out observations, conversations, interviews, or otherwise, we must analyse that data for patterns between both experts and novices. This requires each task performance to be broken into tasks and subtasks to assess where decision-making is applied.
Presenting the results complies with standard formats of task analysis practices, typically with charts, graphs, flow diagrams, etc. However, a parallel narrative should be provided with the visual representation to explain why each user carried out their actions and specific behaviours.
5. Validate your findings
To ensure the accuracy and relevance of the results, they must be checked by someone who wasn’t involved with the study but has an appropriate understanding of the system or product. Where cognitive task analysis studies can be conducted using a single user, introducing multiple users will provide more data for comparison and clarification, adding to the output and final results. Alternatively, researchers can utilise questionnaires, surveys, focus groups, and more to validate their results.
Presenting mental processes and task performance in an appropriate visual format
There are many ways to present task analysis, visually representing the user’s mental process and situation awareness. Graphs, flow charts, and sequence diagrams are appropriate options, including specific graphical references to crucial elements. Whether colour-coded or differently shaped information boxes, these elements allow us to easily spot human factors and their cognitive processes.
- System actions
- User actions
- User decision making
Flow diagrams include directional paths highlighting where different choices take the user, whether that’s back to the beginning of a particular set of subtasks or to an alternative option and outcome. With an abundance of decision-making and user action references, these provide areas for investigation, and with suitable design alternatives, automating or transferring tasks to the system creates a simpler journey and preferable UX.
Task analysis is a valuable tool for UX research and user-centred design as it offers valuable insights into key areas of UX: what users think and feel while identifying, considering, and completing tasks. Following users along their chosen path allows us to understand their critical decision-making and thought processes while revealing additional tips, tricks, and tools—all highly advantageous for successful UX design.
With various ways to carry out this research, it’s essential to understand the goals of each study and what they need to achieve. While cognitive task analysis is only one of many task analysis practices, it’s a great option for identifying problem areas that perform poorly and would benefit from improvement.