Defining Learning: What Research Says—and Why It Matters for Instructional Design
- Elizabeth Calzada
- Jan 8
- 6 min read
Updated: Jan 16
If learning were as simple as listening, watching, or choosing the “right” learning style, we wouldn’t still be debating how people learn. Yet despite decades of research, many instructional practices continue to rely on assumptions, trends, or personal preferences rather than evidence. As educators and instructional designers, this raises an important question: what does it actually mean to learn, and how should that understanding shape the way we design instruction?
Background: Why This Matters to Me
I am a kindergarten teacher and a graduate student in the Learning Design and Technology program. My work lives at the intersection of classroom instruction and instructional design, which means I see firsthand how learning theories play out in real environments. I’ve also seen how easily popular practices, like learning styles or minimally guided discovery, can become normalized even when they lack research support.
As I build my instructional design portfolio, I want my work to reflect more than creativity or good intentions. I want it to be grounded in what we actually know about how people learn. Understanding learning theory is not an abstract academic exercise, it directly informs the decisions we make about content sequencing, scaffolding, feedback, and assessment.
What Is Learning, Really?
Learning is a process that leads to change, resulting from experience and increasing the potential for improved performance. (Lovett et al., 2023, p. 2)
Across the learning sciences, learning is consistently defined not as a product or event but as a process (Lovett et al., 2023). Lovett and colleagues describe learning as a process that leads to change, results from experience, and increases the potential for future performance improvement. As they explain, “learning is not something done to students, but rather something students themselves do” (Lovett et al., 2023, p. 2). Importantly, learning is not something done to learners, but something learners actively do themselves.
This definition immediately challenges surface-level views of learning. Simply completing a task, engaging in an activity, or performing well in the moment does not necessarily indicate learning. True learning involves changes in long-term memory, which allow knowledge and skills to be retained and transferred to new contexts.
Learning theories help us understand different dimensions of this process (Anderman & Anderman, 2009). Behaviorism emphasizes observable changes in behavior shaped by reinforcement. Cognitive theories focus on internal mental processes such as memory, schema formation, and cognitive load. Social cognitive theory highlights learning through observation, modeling, and self-efficacy. Sociocultural theory situates learning within social interaction, cultural tools, and guided participation. No single theory explains all learning, but together they provide a more complete picture.
Principle 1: Learning Is Active and Effortful
One of the most consistent findings across learning theories is that learning requires active engagement (Lovett et al., 2023). From a cognitive perspective, learners must actively process information in order to encode it into long-term memory. From a sociocultural perspective, learners actively participate in meaning-making through interaction with others and with tools.
This principle has direct implications for instructional design. Simply presenting information, through lectures, videos, or slides, is rarely sufficient. Designers must create opportunities for learners to practice, apply, reflect, and receive feedback. Active learning does not mean unstructured activity; it means intentional engagement aligned with learning goals.
Principle 2: Prior Knowledge Shapes Learning
Learners do not enter learning environments as blank slates (Bransford et al., 2000). Their prior knowledge, beliefs, and experiences shape how they interpret new information. When prior knowledge is accurate and activated appropriately, it supports learning. When it is incomplete or incorrect, it can interfere with learning.
This principle underscores the importance of assessing and activating prior knowledge in instructional design. It also explains why the same instruction can produce different outcomes for different learners, not because of learning styles, but because of differences in what learners already know and believe.
Principle 3: Practice and Feedback Drive Mastery
Research consistently shows that developing mastery requires more than exposure to information (Lovett et al., 2023). Learners must practice component skills, integrate them, and learn when and how to apply them. Feedback plays a critical role in this process by helping learners understand the gap between their current performance and desired outcomes.
From a behaviorist perspective, feedback functions as reinforcement. From a cognitive perspective, it helps learners refine mental models. From a sociocultural perspective, feedback often occurs through guided interaction with more knowledgeable others. Effective instructional design leverages feedback strategically rather than treating it as an afterthought.
Myth 1: Learning Styles
One of the most persistent myths in education is the idea that learners have preferred learning styles, such as visual or auditory, and that instruction should be tailored accordingly (Kirschner, Sweller, & Clark, 2006). While this idea is intuitively appealing, research does not support it. Studies consistently show no significant improvement in learning outcomes when instruction is matched to learning style preferences.
The problem with learning styles is not that learners are identical, but that human cognitive architecture is largely shared. What matters more than preference is the nature of the content and the cognitive processes required to learn it. Designing instruction around learning styles can distract from more impactful, evidence-based strategies like managing cognitive load, providing worked examples, and supporting retrieval practice.
In my own instructional design practice, this means prioritizing how information is processed, not how it is preferred. For example, when designing lessons or modules, I manage cognitive load by breaking content into smaller chunks, using clear visual signaling, and removing unnecessary decorative elements that do not support learning. I also model problem-solving through worked examples before asking learners to attempt tasks independently, which helps reduce overload and supports schema development, especially for novice learners.
Myth 2: Minimal Guidance Is Best
Another common misconception is that learners learn best through discovery or problem-solving with minimal guidance (Kirschner et al., 2006). While exploration has a role in learning, research shows that minimally guided instruction is often ineffective, especially for novice learners. Without sufficient guidance, learners may overload their working memory and struggle to distinguish relevant information from irrelevant details.
Cognitive load theory explains why structured support, such as worked examples, scaffolding, and chunking, is critical during the early stages of learning. Sociocultural theory reinforces this idea through the concept of the Zone of Proximal Development, which emphasizes learning through guided participation rather than independent struggle.
In practice, I apply this research by intentionally scaffolding learning experiences rather than relying on open-ended discovery. For example, I begin with guided instruction and worked examples, followed by structured practice with support, and only later move toward more independent application. I also incorporate frequent opportunities for retrieval practice, such as quick checks, discussion prompts, and low-stakes practice activities, so learners actively recall and apply knowledge rather than simply recognize it. These design choices support learning more effectively than expecting learners to “figure it out” on their own.
Why Evidence-Based Instructional Design Matters
Instructional design decisions have real consequences. Poorly designed instruction can waste time, frustrate learners, and reinforce misconceptions. Evidence-based design, on the other hand, increases the likelihood that learning experiences will lead to durable understanding and transferable skills.
Grounding instructional strategies in research allows designers to move beyond trends and assumptions. It encourages intentional choices about sequencing, scaffolding, feedback, and assessment. It also supports equity by recognizing that learners benefit from structured support rather than being left to “figure it out” on their own.
For me, embracing evidence-based design means questioning familiar practices and translating research into concrete design decisions, such as how I sequence content, manage cognitive load, model skills through worked examples, and build in opportunities for retrieval and feedback.
Conclusion: Designing for Learning, Not Myths
Learning is complex, contextual, and deeply human (Lovett et al., 2023). It cannot be reduced to preferences, shortcuts, or one-size-fits-all solutions. By understanding learning through evidence-based theories, instructional designers can create experiences that respect how learning actually works.
The key takeaway is this: effective learning design starts with evidence. When we ground our instructional choices in research, we move closer to designing learning experiences that are not only engaging but also meaningful, equitable, and lasting.
References
Anderman, E. M., & Anderman, L. H. (2009). Psychology of classroom learning: An encyclopedia. Macmillan Reference USA.
Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school. National Academy Press.
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86. https://doi.org/10.1207/s15326985ep4102_1
Lovett, M. C., Meyer, O., & Thille, C. (2023). How learning works: Eight research-based principles for smart teaching (3rd ed.). Jossey-Bass.