Before we delve into the intricacies of HCI research, it is important to consider a fundamental question: What is the overarching goal of an HCI research project? The understanding and engagement with this are key to the learning process.
To count as HCI research, a project must go beyond solving a specific problem. It should produce new knowledge that others in the field can learn from and apply elsewhere.
Thus, the fundamental criterion to consider in your HCI research is whether it contributes new knowledge to the field of human-computer interaction by advancing understanding or offering clear insights. This criterion takes precedence over other objectives, such as developing cutting-edge tools or refining user interfaces. Without meeting this core requirement of knowledge contribution, your work — despite whatever practical utility it may offer — will not qualify as research in the academic sense.
Understanding these fundamental points about HCI research and knowledge contribution is not trivial. The rest of this chapter will help build this understanding by exploring:
Everyday problems are about quick fixes — like putting on a sweater when you're cold.
HCI research problems, however, aim to uncover general lessons
— like learning how people experience temperature, so we can design better indoor environments in the future.
When considering what makes something a research problem in HCI, we need to focus on its potential to generate useful knowledge. This means the problem should help us learn something new that advances our understanding of how humans interact with computers. A research problem in HCI typically:
★ Key idea 1: The goal of a research project is to produce (useful) knowledge.
Having established that research problems in HCI are distinct due to their focus on generating broader knowledge, an important question arises: what types of knowledge contributions are considered valuable in HCI, and how has our understanding of these contributions evolved?
For over four decades, no one has clearly defined what 'counts' as a valuable contribution in HCI — especially for new researchers trying to figure it out.
It took nearly 30 years after CHI began for the HCI community to start classifying what "counts" as a contribution. Before that, it was like trying to write a recipe without agreeing on the ingredients. A significant milestone was reached in 2012 when Wobbrock documented the community's collective understanding, capturing contribution categories that had evolved over decades of CHI conferences. This classification was a crucial step in establishing a common language and understanding within the HCI community. It was later refined and formally published in a 2016 paper with Kientz, systematically identifying seven main types of research contributions in HCI ( Wobbrock & Kientz, 2016 ): empirical, artefact, methodological, theoretical, dataset, survey, and opinion ( see their Table 3 , right side of the original figure, though the figure itself is not reproduced here for brevity).
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Suggested Reading: " Research Contributions in Human-Computer Interaction " |
The classification of HCI research contributions into seven distinct types, developed through a " bottom-up approach ," represented a significant step forward. A bottom-up approach involves starting with specific observations (e.g., analyzing existing papers) and gradually identifying broader patterns to form categories. Wobbrock's methodology involved examining decades of CHI papers to identify common types of contributions, including novel systems, user studies, and theoretical frameworks.
While this approach effectively documented the what of HCI community activities, it fell short in explaining the field's fundamental why —its purpose and direction. To illustrate, consider trying to understand a library's purpose by categorizing its books by color or size. These categories describe physical attributes but miss the library's core mission.
Imagine organizing a library. A 'bottom-up' approach might sort books by colour or size — helpful, but missing the point. A 'top-down' approach asks: 'What’s the library for?' — leading to meaningful categories like reference materials or fiction. This is where Oulasvirta and Horbaek's seminal work "HCI Research as Problem Solving" ( Oulasvirta & Hornbæk, 2016 ) makes its crucial contribution. Taking a top-down approach, they examined how different types of contributions serve the broader goals of HCI research, addressing key questions the bottom-up summary could not fully answer:
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Suggested Reading: " HCI Research as Problem-Solving " |
By adopting this problem-solving lens, Oulasvirta & Horbaek provided a more cohesive understanding of HCI's purpose. A core insight from their perspective is that the overarching goal of HCI research is to enhance our capability to solve important problems in human interaction with computers ( ★ Key idea 2). This reframing offers a much-needed focus and direction.
Building on this, they clarified the nature of 'problems' and 'solutions'. They posit that a research problem in HCI is fundamentally a lack of understanding of the interactions between humans and computers ( ★ Key idea 3). For instance, a "lack of understanding of how color schemes affect the aesthetic experience of web page browsing" constitutes such a research problem. Consequently, a 'solution' or contribution is the newly acquired knowledge that addresses this gap and enhances our understanding ( ★ Key idea 4). The problem's importance determines the value of this contribution and how significantly the new knowledge advances our understanding. Importantly, a solution in HCI research need not be definitive or complete; partial insights that offer some new understanding of a significant problem are also valuable ( ★ Key idea 5).
This problem-solving framework also provides a target against which the success of contributions can be measured. Instead of subjective judgments, evaluation can rigorously assess how work advances the goal of enhancing problem-solving capabilities — does it make solving certain problems easier, clarify concepts, or simplify processes? This evaluative framework is essential for advancing the field in a focused and impactful manner.
Now that we have a clearer understanding of HCI contributions through the problem-solving lens, let us explore how these contributions relate to broader disciplinary foundations.
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HCI = Science + Engineering + Design |
HCI is a highly multidisciplinary field, drawing from science, engineering, and design, each contributing unique perspectives:
● Science in HCI focuses on understanding interactions, formulating hypotheses, conducting experiments, and developing theories.
● Engineering in HCI applies scientific principles to design, develop, and evaluate practical software and hardware solutions.
● Design in HCI crafts the user experience, emphasizing aesthetics, usability, and intuitive interfaces.
While some may debate the extent of its scientific nature, the scientific method forms a foundational pillar of rigor within HCI research , ensuring findings are reliable, valid, and generalizable. Cultivating a scientific mindset is crucial for an HCI researcher. However, this does not preclude valuing engineering and design. Integrating scientific rigor with the creativity and practicality of engineering and design leads to robust, innovative, and user-centered solutions.
Therefore, as an HCI researcher, it is highly recommended to:
● Develop a strong scientific foundation (methods, statistics, experimental design).
● Integrate engineering principles (system development, technical considerations).
● Embrace design thinking (UX principles, aesthetics).
★ Key idea 6: HCI research blends science (for rigor), engineering (for implementation), and design (for user experience). Think of it like baking a cake: science gives you the recipe, engineering builds the oven, and design makes sure it tastes great.
Science, as a systematic enterprise, builds knowledge through testable explanations and predictions, characterized by:
● Empirical evidence : Reliance on observable and measurable data.
● Objectivity : Minimizing biases.
● Predictability : Theories making testable predictions.
● Falsifiability : Propositions being disconfirmable.
The scientific discovery process typically involves stages such as Observation, Hypothesis Formulation, Experimentation, Data Collection, Analysis and Interpretation, and ultimately, Theory Development. These stages lead to different kinds of scientific contributions. Larry Laudan, in "Progress and Its Problems," helps clarify these by distinguishing between two fundamental types of problems that scientific contributions aim to solve ( Laudan, 1978 ):
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Suggested Reading: " Progress and Its Problems " |
Addressing both empirical and conceptual problems is crucial for scientific advancement. Empirical contributions provide the evidence base, while theoretical contributions (which address conceptual problems) build the explanatory frameworks.
The relationship between these two types of contributions — and the problems they address — is iterative and symbiotic:
● From Theory/Conceptual Frameworks to Empirical Research: Existing theories and conceptual understandings motivate and guide empirical investigations by identifying phenomena to study, questions to ask, and hypotheses to test.
● From Empirical Research to Theory/Conceptual Frameworks: Empirical findings, in turn, can support, refute, refine, or lead to the creation of new or revised theories and conceptual understandings.
In HCI, this interplay is vital. For example, a conceptual model of user engagement (a theoretical framework) might guide the design of an experiment (empirical research) to test its predictions. The results of this experiment then feed back to validate or refine the model. Empirical research without a solid conceptual or theoretical basis may yield isolated facts without broader meaning or direction, while theories lacking empirical support may not accurately reflect real-world interactions and lack a solid foundation.
While scientific principles yield empirical and theoretical contributions, HCI's practical nature necessitates recognizing contributions from engineering and design. Thus, HCI includes a third category: constructive contributions . These focus on building new tools, systems, or design ideas that address user needs — like designing an eye-tracking interface for accessibility.
HCI research, therefore, primarily involves three contribution types: empirical, theoretical (often termed "conceptual" in HCI to include broader frameworks and descriptive explanations beyond mathematical formulations), and constructive.
Building on these contribution types, Oulasvirta & Hornbæk (2016) propose a taxonomy of HCI research problems, recognizing that research efforts cluster around three recurring problem types, each with subtypes:
● Subtypes: 1. unknown phenomena, 2. unknown factors, and 3. unknown effects.
● Example: A controlled experiment comparing gesture-based vs. mouse/keyboard navigation in 3D virtual environments to understand effects on cognitive load and user satisfaction.
● Subtypes: 1. no known solution, 2. partial, ineffective, or inefficient solution, and 3. insufficient knowledge or resources for implementation or deployment.
● Example: Developing a novel eye-tracking interface (hardware and software) for individuals with severe motor disabilities.
● Subtypes (from Laudan, 1978 ): 1. implausibility, 2. inconsistency, and 3. incompatibility.
● Example: Developing a new theoretical framework integrating presence, immersion, and interactivity to model user engagement in VR.
This typology, derived from a top-down theoretical basis, contrasts with bottom-up taxonomies like Wobbrock's (2012) , which was based more on community consensus. Oulasvirta & Hornbæk (2016) framework is influential, guiding authors and reviewers, particularly for conferences like ACM CHI.
In reviewing Oulasvirta & Hornbæk's (2016) classification, we identify areas for potential refinement to enhance clarity and utility:
● Empirical Category: While "unknown phenomena" is foundational, "unknown factors" and "unknown effects" are closely linked. A combined "unknown factor/effect" category might be more streamlined.
● Constructive Category: "No known solution" and "partial solution" are key. "Inability to implement or deploy" could potentially be integrated within "partial solutions."
● Enrichment: A distinction could be made between contributions directly advancing HCI capabilities and those enhancing HCI research conduct itself (e.g., new methods, datasets, surveys).
Building on these observations, we propose a thoughtfully refined classification that preserves the strengths of the original framework while incorporating these suggested enhancements (as shown in the revised figure below). This evolution aims to provide an even clearer and more practical framework.
Effective problem-solving in HCI hinges upon evaluating capacities across multiple dimensions. Key criteria include significance, effectiveness, efficiency, transferability, and confidence. Table 1 illustrates a framework for this.
Table 1: Framework for Evaluating Problem-Solving Capacities in HCI Research
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Criterion |
Description |
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Significance |
Importance and relevance of the research problem to the HCI community and society at large |
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Effectiveness |
Extent to which proposed solutions address the identified problem and meet user requirements |
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Efficiency |
Assessment of resource utilization, scalability, and optimization of solutions |
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Transferability |
Applicability of findings across diverse contexts and domains |
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Confidence |
Robustness and reliability of research outcomes, including empirical validity, replicability, and reproducibility |
In the past, HCI evaluation emphasized things like novelty or clever design ideas. But today, researchers are shifting toward evaluating how well research helps solve real-world problems. This means asking: 'Does this help users? Is it reliable? Can others apply it elsewhere?'
Understanding the fundamental principles of problem-solving and research contributions in HCI provides a solid foundation for further exploration into the intricacies of empirical and artifact research methodologies. As we delve deeper into empirical research in the following chapters and later explore artifact research methodologies, we encourage new researchers to embark on a journey of continuous learning about research. Aspiring researchers and seasoned practitioners alike stand to benefit from a comprehensive understanding of the methodologies and principles underpinning HCI research, paving the way for impactful contributions to the field and driving positive change in the user experience of computing technologies.
Wobbrock, J. O., & Kientz, J. A. (2016). Research contributions in human-computer interaction. interactions, 23(3), 38-44.
Oulasvirta, A., & Hornbæk, K. (2016, May). HCI research as problem-solving. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 4956-4967).
Laudan, L. (1978). Progress and its problems: Towards a theory of scientific growth (Vol. 282). Univ of California Press.
Wobbrock, J. O. (2012). Seven research contributions in HCI. Intelligence, 174(12-13), 910-950.