Thick Data
Qualitative data that provides insights into the context and human aspects behind quantitative data. Crucial for gaining deep insights into user behaviors and motivations.
Qualitative data that provides insights into the context and human aspects behind quantitative data. Crucial for gaining deep insights into user behaviors and motivations.
A cognitive approach that involves meaningful analysis of information, leading to better understanding and retention. Crucial for designing educational and informational content that promotes deep engagement and learning.
Natural Language Processing (NLP) is a field of AI focused on the interaction between computers and humans using natural language. Essential for developing applications like chatbots, language translation, and sentiment analysis.
A qualitative research method that studies people in their natural environments to understand their behaviors, cultures, and experiences. Crucial for gaining deep insights into user behaviors and contexts.
The process of making predictions about future trends based on current and historical data. Useful for anticipating user needs and market trends to inform design decisions.
A cognitive bias where people ignore the relevance of sample size in making judgments, often leading to erroneous conclusions. Crucial for designers to account for appropriate sample sizes in research and analysis.
The ability to intuitively understand what makes a product successful, including market needs, user experience, and competitive landscape. Important for making informed decisions that lead to successful product development.
A collaborative tool used to visualize what a user thinks, feels, says, and does to better understand their experiences and needs. Essential for gaining deep insights into user behavior and guiding design decisions.
A mathematical framework used to analyze strategic interactions where the outcomes depend on the actions of multiple decision-makers. Useful for designing systems and processes that involve competitive or cooperative interactions.
The practice of deeply understanding and sharing the feelings of users to create products and services that truly meet their needs. Crucial for creating user-centered designs that resonate with users' emotions and experiences.
A field research method where researchers observe and interview users in their natural environment to understand their tasks and challenges. Crucial for gaining authentic insights into user behavior and needs.
Critical Incident Technique (CIT) is a method used to gather and analyze specific incidents that significantly contribute to an activity or outcome. This method is important for identifying key factors that influence performance and user satisfaction.
A type of model architecture primarily used in natural language processing tasks, known for its efficiency and scalability. Essential for state-of-the-art NLP applications.
A usability testing method where participants verbalize their thoughts while interacting with a product. Essential for understanding user thought processes and identifying usability issues.
Quantitative data that provides broad, numerical insights but often lacks the contextual depth that thick data provides. Useful for capturing high-level trends and patterns, but should be complemented with thick data to gain a deeper understanding of user behavior and motivations.
A philosophical approach to culture and literature that seeks to confront the social, historical, and ideological forces and structures that produce and constrain it. Valuable for analyzing and addressing power dynamics and biases in design.
A user-centered design process that involves understanding users' needs and workflows through field research and applying these insights to design. Essential for creating designs that are deeply informed by user contexts and behaviors.
A research technique that explores the context in which users interact with a product, service, or environment to understand their needs and behaviors. Crucial for gaining deep insights into user contexts and designing more relevant solutions.
A research method that focuses on understanding phenomena through in-depth exploration of human behavior, opinions, and experiences, often using interviews or observations. Essential for gaining deep insights into user needs and behaviors to inform design and development.
Needs and expectations that are not explicitly stated by users but are inferred from their behavior and context. Crucial for identifying and addressing unarticulated user needs.
A research method that involves forming a theory based on data systematically gathered and analyzed. Useful for developing design theories and solutions that are directly grounded in user research and data.
A component in neural networks that allows the model to focus on specific parts of the input, improving performance. Essential for developing advanced AI models, particularly in natural language processing.
A qualitative research method involving direct conversations with users to gather insights into their needs, behaviors, and experiences. Essential for gaining deep insights into user perspectives and informing design decisions.
An economic theory that explains why some necessities, such as water, are less expensive than non-essentials, like diamonds, despite their greater utility. Useful for understanding consumer behavior and designing pricing strategies.
A type of artificial intelligence capable of generating new content, such as text, images, and music, by learning from existing data. Important for automating creative processes and generating novel outputs.
A research method where participants record their activities, experiences, and thoughts over a period of time, providing insights into their behaviors and needs. Important for gaining in-depth, longitudinal insights into user experiences.
The process of distinguishing a product or service from its competitors in a way that is meaningful to the target market. Important for creating a unique value proposition and gaining a competitive edge.