Why Most Online Courses Fail (and How to Design Experiences That Don’t)

A framework for high-engagement learning built on cognitive science and agile production.

If you search for "instructional design frameworks" you’ll find archaic frameworks from the early 2000s that feel like they were invented by people who hate learning. In the fast-paced world of EdTech, we need something leaner. This post answers the fundamental question of how to build effective digital education by focusing on three pillars:

• The Pedagogical Process: From student definition to actionable outcomes.

• Agile Production: Keeping engagement high without a Hollywood budget.

• Learning Science: Leveraging memory and skill-building hacks.

Step Zero: Who is your student, really?

It sounds repetitive or maybe obvious, but almost everyone skips this. Before recording a single second, or before thinking about setting a learning goal you must define WHO you are designing for. Designing for a newcomer without experience and a full time job who wants to shift career paths is worlds apart from designing for a professional navigating a highly regulated industry who wants to update her understanding of the new aeronautical international laws.

The student’s context dictates the architecture of the course. For example, if you are teaching Photoshop, the "Job to be Done" is often immediate and practical. The student wants to know "how to remove my ex from this photo." In that case, your sessions should be named exactly like the problem they solve. However, if you are designing a University-level Critical Thinking course, the student is there to build a system of thought. You can’t just give them a "hack." You need to guide them through a serial, progressive journey from the bird’s-eye view to the complex details.

If you don’t understand their constraints, whether they are exhausted parents who can only study after the kids are asleep or professionals looking for a quick fix, you’re designing a course no one will finish.

Ask yourself:

• Why are they here? (Are they looking for a certificate to advance their career, is it a mandatory regulation training they have to pass, or are they here purely for intellectual curiosity?)

• What is their prior knowledge? (Are they experts who just need to find a specific new law, or are they beginners who don't even know the basic terminology of the industry yet?)

• How do they measure their own success? (Will they feel they succeeded because they passed a technical exam for a license, or because they finally understood a complex concept they have been struggling with for years?)

• What does their "real life" look like? (Do they have a house full of kids and a dog where they can only engage with your content at 9 PM on a weekday, or are they single with plenty of free time? These psychographics matter because if your content doesn't fit into their actual schedule, you lose them immediately.)

• Where are they consuming this? (Are they watching on a phone during a commute or at a dedicated desk with two monitors?)

• What is their time budget? (Do they have 10 minutes between meetings or a dedicated 2-hour block at night?)

Objectives: What will they have built?

Forget Bloom’s Taxonomy and checking off verb lists. It's an important contribution but fear instructional designers focus more on writing objectives compliant with Bloom's taxonomy instead of focusing on the user and speaking a language everyone on the team can easily align with. The most effective shift I’ve made is to rethink how I establish objectives (This opinion might make some academics cringe, but then again, show me your highly engaging learning experiences, because I can show you my portfolio). Commonly, learning objectives are stated the following way: "At the end of the experience, the learner will..." and focus on what the birght future will bring. I have found it is much more useful to just ask:

"By the end of this course, what will they have created?"

When a student finishes a course and realizes they’ve built a project—a piece of code, a regulatory plan, or a design—the learning becomes undeniable. The project isn't an "extra"; it’s the core. Every activity should be a step toward that final output, moving from the concrete to the abstract.

Designing Activities and Content: The Agile Syllabus

Once your objective is set, you need to structure the path. In traditional circles, this is the "Syllabus," but in effective online design, it is a sequence of high-value activities.

The most important caveat here is to go directly to the value. People choose online learning because they want speed and efficiency. Don't waste their time with the entire history of a regulation if they just need to know how to apply it. The "bird’s-eye view" should only be used to position the student on the board so they aren't lost, not to give them a long-winded backstory.

If you are teaching a specific law or a mathematical procedure, skip the fluff and present the Key Idea and its relevance immediately. For example, if you are teaching Fourier Transforms:

"The Fourier Transform is a massive tool because it allows us to take a complex signal in the time domain and deconstruct it into its individual frequencies. It lets us see the 'ingredients' of a wave. In this session, I’m going to show you how to use it to analyze data, and we’ll solve a few problems so you can see exactly when to apply it in the real world."

This approach gives the learner an immediate sense of why the structure exists. By showing them the "win" upfront, you create a more effective path to retention and solve the biggest problem in online education: learner engagement.

Evaluation: Scale vs. Depth

Your evaluation strategy must be the mirror image of your objective. It answers one question: did they achieve what we promised? When designing for online experiences, you have to navigate the tradeoff between scale and depth.

High Scale, Low Depth: Multiple-choice exams are the standard because they are easy to automate for thousands of users. They are useful for verifying basic conceptual retention, but they are relatively superficial.

Low Scale, High Depth: A capstone project or a thesis is the gold standard for deep learning, but it doesn't scale. You can't manually grade ten thousand projects without a massive team.

The middle ground is where the magic happens. Use automated tools for the "knowledge" checks, but push for a "build" or a "project" for the skill verification. Even if it is self-evaluated or peer-reviewed, the act of creating is what validates the experience for the student.

Production: Rhythm is King A talking head for 20 minutes is the ultimate sleep aid. Here are my golden rules for production:

• The 10-20 Second Rule: Change the shot, add an image, or use a text overlay every 10 to 20 seconds. Visual rhythm keeps the brain from switching to "autopilot."

• The 10-Minute Ceiling: Engagement craters after 10 minutes. If a topic is complex, break it into 5-8 minute "capsules."

• Functional Quality: You don't need 8K resolution. You need crystal-clear audio and legible diagrams. Beyond that, you hit diminishing returns.

• Cut the Fluff: Your course is finished not when there’s nothing left to add, but when there’s nothing left to take away. Start directly with the value.

Leveraging Learning Sciences: Forests, Houses and Trails

This is an oversimplification, but it works to understand how to think about learning design. For a lasting impact of the learning experience, or what is called retention, I find it useful to think about two aspects of how memory and learning work. One is related to memorizing concepts, and the other is how to build a skill.

Non-experts often confound these, but it is important to look at them separately. When designing the learning experience, you need to know what concepts the person needs to memorize and retain, and what things they need to be able to do. One is a conceptual memory; the other is a skill.

Skills can be simple, such as learning how to pass a pen from your left to your right hand—a skill you developed before you were one year old. But more complex skills, such as interpreting the law and planning a defense strategy as an attorney, are still skills. We use two key concepts to address this:

• Retrieval Practice (for Concepts): Memory is like a trail in the forest. If you don't walk it, the weeds take over. The "house" is the memory you built, but the trail is how you access it. To mark the path, you must ask students to recall information from previous lessons. If you only expose them to a concept once, the weeds will eat the trail by next week.

• Procedural Memory (for Skills): This is about how your body and mind "remember" a process without overthinking it. To develop skills like coding or legal reasoning, you need deliberate practice, immediate feedback, and interleaved practice. Don’t just repeat the same exercise. Throw curveballs and randomize the problems to force the brain to adapt.

Recap: The Takeaway Building an online course isn't just a content dump; it’s human engineering. If you want to move from "video provider" to "learning architect," follow this hierarchy:

• Pedagogy First: Start with the user context to establish your constraints. Define your objective by asking what they will have achieved or built by the end. Only then do you design the activities and finish with an evaluation strategy that verifies you actually hit that objective.

• Production in Service of Learning: Your content must align with your pedagogy. Quality is vital to ensure the content is accessible and clear, but remember that less is more. Keep it brief and agile. Your content is ready when you can’t remove anything else.

• Science-Backed Retention: Treat concepts and skills as two different animals. They are acquired and stored differently in the brain, and both are necessary for a complete experience.

If you want to dive deeper into the mechanics of how we learn, I suggest starting with these two books:

• Make It Stick: The Science of Successful Learning by Peter C. Brown.

• A Mind for Numbers: How to Excel at Math and Science by Barbara Oakley.