Education is a deceptively simple word. Yet anyone who has ever tried to design learning — whether a curriculum, a classroom, a lesson plan, or a digital product — knows it is not simple at all.
After years of building and thinking deeply about how learners actually succeed, I’ve come to see education through a lens that is both practical and grounded in science:
Effective learning depends on three essential processes:
1) information transmission
2) engagement and motivation
3) neural linkage formation
When any one of these is missing or mis-designed, learning stumbles. When all three are aligned, learning sticks.
Below is what that model means — and why it matters more than ever in today’s EdTech landscape.
1) Information Transmission: Encoding the Right Knowledge
At its simplest, learning begins with information. You cannot internalise what you never encounter. But information alone is not learning. How it is structured and processed by the learner’s cognition profoundly shapes whether it becomes understood knowledge.
One of the most influential frameworks in this space is Cognitive Load Theory, which explains how our limited working memory must be managed for new information to move into stable long-term memory. Effective instruction reduces unnecessary complexity, sequences material logically, and supports meaningful connections to what learners already know. (Sweller, 1988)
For example:
- sequencing topics to build on prior knowledge
- using worked examples to scaffold understanding
- removing irrelevant distractions
…all help learners absorb and integrate information more successfully.
This academic basis helps explain why well-designed content — whether text, digital, or teacher-led — is not interchangeable with random exposure to content.
2) Engagement and Motivation: Sustaining Attention and Drive
Transmission gets information in front of learners; engagement and motivation keep it in front of their minds long enough to matter.
Research distinguishes multiple dimensions of engagement — behavioral (what learners do), cognitive (how deeply they think), and emotional (how they feel about learning). Sustained engagement is a significant predictor of learning outcomes, and it is deeply tied to motivation — the internal drive that encourages learners to persist through challenge. (Fredricks, Blumenfeld & Paris, 2004)
Educational neuroscience also shows that active, participatory learning recruits neural systems associated with attention, curiosity, and reward — mechanisms that support deeper processing and persistence. (Immordino-Yang & Gotlieb, 2017)
Engagement and motivation come from:
- relevance of material
- clear goals and feedback
- social interaction and accountability
- tasks calibrated to challenge without overwhelming
Platforms that treat engagement as optional “gamification” often miss this nuance. Engagement here is neurobiological and behavioral, not just decorative.
3) Neural Linkage Formation: Building Durable Memory
The final — and most often overlooked — step is neural linkage formation: turning transient exposure into lasting knowledge.
Research in neuroscience explains that real learning is tied to synaptic plasticity — the brain’s ability to strengthen connections between neurons through repeated, meaningful activation. This is the biological basis of memory formation and transfer. (Kandel, 2001)
This is why:
- spaced repetition (revisiting material over time) enhances long-term retention,
- retrieval practice (recalling information) strengthens memory,
- application and feedback cycles shape knowledge transfer to new contexts.
These effects are robustly supported in psychology and neuroscience.
This is why learners forget if they only passively scroll or watch short videos, and why they remember when they actively practice, retrieve, and reinforce.
Why This Model Matters for EdTech
Too many discussions about EdTech focus on format — online vs offline, app vs classroom — and not on the mechanisms that produce learning.
Your product can have the best animation or slickest UI, but if it fails any of the three processes above, retention collapses and impact fizzles.
This helps explain some real patterns we see in the industry:
- Pure information delivery (like static video libraries) can increase exposure but does not ensure comprehension.
- Engagement designed around novelty, rather than cognitive and emotional investment, leads to short-term metrics that evaporate over time.
- Systems that ignore spaced practice, retrieval testing, or meaningful feedback fail to translate early gains into durable understanding.
Academically, this model aligns with research that ties cognitive load, engagement and motivation, and neural plasticity directly to learning outcomes.
A Broader Way to Think About “EdTech”
Seen through this lens, EdTech is not just “tech in education” — it is any tool that meaningfully improves one of these three processes:
- Transmission: structuring and delivering information so it can be absorbed
- Engagement and motivation: activating and sustaining learner attention and drive
- Neural linkage: supporting the formation of durable memory and flexible application
AI-driven adaptive systems that reduce extraneous cognitive load are EdTech.
Interactive classrooms that deepen engagement are EdTech.
Practice systems that promote retrieval and spaced feedback are EdTech.
All of these do not merely change delivery medium — they change how learning actually happens, in a way that aligns with how the human brain learns.
A Simple Framework, a Real Foundation
This transmission → engagement and motivation → neural linkage formation model is not a final taxonomy, nor is it a formal academic theory itself, but it maps directly onto decades of research across cognitive science and educational practice.
If educational tools want to deliver real impact — not just surface engagement or temporary adoption — they must be designed with these three processes at the center.
Because if information never sticks, learning never truly happens.
📌 References (selected)
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Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. A foundational articulation of Cognitive Load Theory showing how working memory limitations influence instructional design.
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Fredricks, J.A., Blumenfeld, P.C., & Paris, A.H. (2004). School Engagement: Potential of the Concept, State of the Evidence. Review of Educational Research, 74(1), 59–109 — a widely-cited review of engagement (behavioral, emotional, cognitive) and motivation in learning.
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Immordino-Yang, M.H. & Damasio, A. (2007). We feel, therefore we learn: The relevance of affective and social neuroscience to education. Mind, Brain, and Education — foundational synthesis explaining how emotion and cognition interact in learning.
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Kandel, E.R. (2001). The molecular biology of memory storage: A dialogue between genes and synapses. Science.
