What Learning Really Looks Like
What if the way we design learning is based on myths instead of science?
If you have ever heard someone say, “I’m a visual learner,” or believed that students learn best when they are left to discover knowledge on their own, you are not alone. These ideas are everywhere—in classrooms, corporate training, YouTube videos, and even well-meaning instructional design conversations. The problem is not that these beliefs sound unreasonable. The problem is that many of them are not supported by learning science.
As instructional designers, our work directly influences how people learn, perform, and transfer knowledge into real-world contexts. That responsibility requires more than intuition, personal preference, or trend-following. It requires evidence-based decision-making.
The purpose of this blog is to clarify what learning actually is, explain how major learning theories help us understand it, and show why instructional design must be grounded in scientific research. Along the way, I will debunk two persistent myths about learning and highlight why evidence-based teaching strategies are essential for effective, ethical learning design
Why Learning Science Matters to what I currently do
I am an instructional designer and UX-focused learning designer with experience designing digital learning experiences across education, technology, and AI-driven platforms. Throughout my work, I have seen how easily learning experiences can fail when they are designed around assumptions rather than evidence—whether that is overloading learners with information, prioritizing engagement over learning, or relying on popular but unsupported learning theories.
My interest in learning science comes from a practical need: I want learning experiences to work. Studying established learning theories and research-based principles has helped me design instruction that supports real understanding, long-term retention, and meaningful transfer—not just completion or surface-level engagement.
This blog reflects my commitment to applying learning research, particularly the principles outlined in How Learning Works (Lovett et al., 2023), alongside foundational learning theories such as cognitive, social cognitive, and sociocultural perspectives.
What Is Learning, Really?
Learning is often mistaken for exposure, engagement, or performance in the moment. In reality, learning is a process of lasting change in knowledge, skills, beliefs, or behaviors that results from experience (Shuell, 2013).
How Learning Theories Help Us Understand Learning
Several prominent learning theories offer complementary lenses for understanding how learning happens:
1. Cognitive Learning Theory
Cognitive theories emphasize that learning involves mental processes such as attention, memory, prior knowledge, and sense-making. Learners are not empty vessels; they actively interpret information based on what they already know.
Design implications:
Prior knowledge strongly influences learning—for better or worse (Lovett et al., 2023).
Instruction must manage cognitive load and support meaningful organization of information.
Practice and retrieval strengthen memory and understanding.
2. Social Cognitive Theory
Social cognitive theory highlights the role of observation, modeling, motivation, and self-efficacy in learning (Denler et al., 2010). Learners develop skills and beliefs by seeing others perform tasks and by receiving feedback.
Design implications:
Learning environments should include modeling, worked examples, and opportunities for guided practice.
Motivation and self-belief significantly affect persistence and performance.
Feedback should be timely, specific, and actionable.
3. Sociocultural Theory
Sociocultural theory emphasizes that learning is inherently social and situated within cultural and contextual factors (Scott & Palincsar, 2012). Knowledge is constructed through interaction, language, and shared activity.
Design implications:
Learning should be contextualized and authentic.
Collaboration and dialogue can deepen understanding.
Scaffolding is essential, especially for novice learners.
Together, these theories reinforce a critical truth: learning is active, effortful, contextual, and deeply influenced by prior experience and social interaction.
Debunking Common Myths About Learning
Despite decades of research, several myths continue to shape instructional decisions. Two of the most persistent are the learning styles myth and the belief that learners always know what is best for their own learning.
Myth 1: “People Learn Best When Instruction Matches Their Learning Style”
The idea that learners are visual, auditory, or kinesthetic learners is one of the most widespread myths in education. However, extensive research has found no credible evidence that matching instruction to a learner’s preferred style improves learning outcomes (Kirschner & van Merriënboer, 2013).
The Veritasium video You Are Not a Visual Learner reinforces this point by explaining that effective learning depends on the nature of the content, not personal preference. For example, visual information is best for spatial relationships, while verbal explanations may be more effective for abstract concepts.
What the evidence actually shows:
Learning is improved by dual coding, meaningful practice, and retrieval—not style matching.
Preferences do not equal learning effectiveness.
Designing for “learning styles” can distract from strategies that actually support learning.
Myth 2: “Learners Know Best How They Learn”
Another common belief is that learners should fully direct their own learning because they know what works for them. While learner agency is important, research shows that novices often misjudge their own understanding and learning progress (Kirschner & van Merriënboer, 2013).
This misconception is closely related to the illusion of competence, demonstrated in cognitive psychology research and illustrated by the Monkey Business Illusion video (Simons, 2010). People frequently believe they are learning when they are merely recognizing information or feeling fluent.
What the evidence actually shows:
Novices benefit from structure, guidance, and scaffolding.
Discovery learning without support often overloads working memory.
Expert-designed instruction outperforms minimally guided approaches.
Why Evidence-Based Instructional Design Matters
Designing instruction without evidence is not just ineffective—it is risky. Evidence-based teaching methods ensure that learning experiences are aligned with how the brain actually learns, not how we wish it did.
Research-Based Principles That Should Guide Design
Lovett et al. (2023) outline eight research-based principles of learning. Several are especially critical for instructional designers:
Prior knowledge can help or hinder learning: Instruction must diagnose and address misconceptions.
Motivation matters: Learners persist when they see value, relevance, and achievable challenge.
Practice and feedback drive mastery: Learning requires effortful practice, not passive consumption.
Transfer requires varied and contextualized practice: Knowledge does not automatically transfer without intentional design.
The Cost of Ignoring Evidence
When instructional strategies are based on myths or trends rather than research, the result is often:
Shallow learning
Poor retention
Low transfer to real-world tasks
Frustrated learners
In contrast, evidence-based design supports equity, accessibility, and effectiveness by creating learning environments that work for diverse learners—not just those who already know how to learn.
Conclusion: Designing for Learning, Not Illusions
Learning is complex, effortful, and deeply human. It is not about preferences, entertainment, or intuition—it is about cognitive processes, social interaction, and purposeful practice.
As instructional designers, we have both a responsibility and an opportunity. By grounding our work in learning science, questioning popular myths, and applying evidence-based strategies, we can design learning experiences that truly make a difference.
Key takeaway: Effective instructional design does not start with what feels right—it starts with what research shows actually works.
If we want learning to be meaningful, transferable, and lasting, we must design for how people really learn.
