How to Develop Courses on Problem Solving in Six Simple Steps

I get why problem solving feels hard to teach. Students don’t just need “the right answer” — they need a repeatable way to think when the situation is messy, incomplete, or just plain frustrating.

What I’ve found works best is building a course where students practice diagnosing problems, testing ideas, and reflecting on their reasoning over and over. Not in theory. In activities that look like real life.

Below is a practical, course-ready way to develop courses on problem solving in six simple steps — with templates, sample assessments, and examples you can reuse.

Step 1: Understand Key Problem-Solving Techniques

This step is about getting clear on the tools you’ll teach — and making sure they map to what learners will do later.

I like to start with three “core moves” because they show up in almost every problem-solving model:

• Diagnose the problem (what’s actually happening?)
• Generate options (what could we try?)
• Decide & test (what’s the best next action, and how will we know?)

Then I teach specific techniques that support those moves. For example:

• Break it down: split a messy issue into smaller components (process, people, data, constraints).
• Five Whys: ask “why” repeatedly, but require learners to stop and verify assumptions at each step (e.g., “What evidence do we have?”).
• PDCA: Plan what you’ll change, Do it on a small scale, Check results, Act based on what you learned.
• IDEAL: Identify the problem, Define it clearly, Explore options, Act, Look back.

Here’s a course-ready micro-lesson you can drop into your curriculum (45 minutes):

• 0–10 min: Short demonstration. Show a real problem statement (messy, incomplete) and highlight what’s missing.
• 10–25 min: Guided practice using a worksheet. Students fill in: “What do we know?” “What do we need?” “What could be causing it?”
• 25–35 min: Technique drill. Each group runs one “five whys” attempt and flags which “whys” are guesses.
• 35–45 min: Quick share-out. Groups explain their diagnosis and the evidence they used.

One thing I insist on: teach students to ask, “What’s the real problem here?” before they ask, “What should we do?” It saves so much time (and so much frustration) later.

Example problem statement (for your first assignment):
“Customer complaints increased this month. Support tickets mention ‘the app is broken,’ but no one agrees on what’s happening.”

Expected student output (what “good” looks like):

• A clear problem definition (e.g., “Users experience login failures after the latest update” — or “We don’t yet have enough data to define the problem”).
• At least 3 hypotheses with evidence requirements (e.g., logs, timestamps, device types).
• A short plan for gathering missing info (what to check first, and why).

If you want a structure for this part, use [lesson planning](https://createaicourse.com/lesson-writing/) as a starting point, then tailor it to your technique and rubric (more on rubrics below).

Step 2: Integrate Problem Solving into Educational Strategies

Once you’ve picked your techniques, don’t keep them in a “skills lecture.” I learned the hard way that students forget frameworks the moment they’re not used in assignments.

Instead, weave problem solving into how students spend their time every week.

Here are specific ways to do that:

• Case studies (written). Students diagnose, propose options, and justify their choice.
• Scenario simulations (role-based). Students respond to new info mid-way through.
• Team problem-solving. Give roles (e.g., “evidence checker,” “options generator,” “risk reviewer”).
• Discussion prompts that require reasoning, not opinions. (More examples in Step 4.)
• Gamified tasks where the “score” depends on the quality of justification — not just selecting an answer.

What I noticed in my own teaching is that students get better faster when you repeat the same problem-solving structure across different topics. They stop guessing what you want, and they start practicing the skill.

Try this weekly rhythm (for a 4-week module):

• Day 1: Mini lesson + worked example (10–20 minutes).
• Day 2: Scenario practice (students use the framework).
• Day 3: Peer review (students critique reasoning using a rubric).
• Day 4: Reflection + revision (they improve their own solution).

If you want more ideas for how to structure these learning moments, explore [effective teaching strategies](https://createaicourse.com/effective-teaching-strategies/). Just make sure your activities require explanation — that’s where the real learning shows up.

Also, skip unverifiable “big stats” and focus on what you can observe: do students actually talk through their reasoning? Do they revise when they get feedback? If the answer is yes, your approach is working.

Step 3: Teach Problem Solving Explicitly

This is the “map and compass” step. Students need to know what to do at each stage, and they need examples of what good reasoning sounds like.

Start by teaching a clear sequence. Here’s a simple one that works across many fields:

• Define: What’s the problem? What’s the impact? What constraints exist?
• Diagnose: What might be causing it? What evidence do we need?
• Explore options: List 3–5 approaches. Include “low-cost / low-risk” options.
• Decide: Choose one approach and justify it (criteria + tradeoffs).
• Look back: What happened? What would we do differently next time?

Then make it visible. Use a flowchart or checklist students can keep open while they work.

Sample “Problem-Solving Checklist” (student-facing):

• Problem statement is specific (not vague like “it’s broken”).
• I listed at least 2–3 hypotheses with evidence needs.
• My options include tradeoffs (time, cost, risk, impact).
• I chose a solution using criteria (not just preference).
• I wrote what I’ll measure to know if it worked.

Now, model the process yourself. I like doing a “think-aloud” version where I pause and say what I’m deciding and why. For online classes, a short screen recording works great.

Guided practice activity (30 minutes):

• Give students a partially completed worksheet (missing the “diagnosis” section).
• Ask them to fill in the missing parts using the framework.
• Provide immediate feedback with comments tied to the checklist.

Grading rubric you can use (focus on root cause quality):

• Root Cause Clarity (0–4): 0 = guesses only; 2 = plausible but vague; 4 = specific and testable.
• Evidence & Assumptions (0–4): 0 = no evidence; 2 = some evidence; 4 = clearly separates facts from assumptions.
• Option Quality (0–4): 0 = one option; 2 = 3 options; 4 = 3–5 options with tradeoffs.
• Decision Justification (0–4): 0 = “because I think so”; 4 = criteria-based decision.
• Measurement Plan (0–4): 0 = no metrics; 2 = one metric; 4 = clear metrics + timeframe.

Want a starting point for turning your objectives into lesson structure? Use [lesson preparation](https://createaicourse.com/what-is-lesson-preparation/) to help you draft step-by-step activities and materials.

And yeah — the goal isn’t just to solve a single problem. It’s to build confidence that they can approach a new problem without freezing or flailing.

Step 4: Foster Critical Thinking Skills in Online Students

Critical thinking is what turns “following steps” into real skill. Without it, students will just copy the framework and still miss the real issue.

To build this, I focus on three things:

• Assumptions: Are they making claims without evidence?
• Options: Did they consider alternatives or stop at the first idea?
• Reasoning: Can they explain why their choice makes sense?

Use ambiguous scenarios on purpose. If the case is too clean, students won’t practice judgment. Here’s a case prompt you can assign:

Case Study Prompt #1 (Ambiguous outcome):
“A school district reports that attendance dropped after a new schedule was introduced. Students say ‘it’s too early’ but teachers say ‘home issues are worse now.’ You also learn that attendance is measured differently in two months of the year.”

Student tasks:

• Define the problem as two competing hypotheses (not one).
• List 3 pieces of evidence you’d collect to distinguish the hypotheses.
• Propose two interventions that could test each hypothesis.
• Explain which intervention you’d run first and why (include tradeoffs).

What strong answers usually include: clear hypothesis framing, evidence needs that match the hypotheses, and a measurement plan (e.g., “track attendance by grade level for 2 weeks after change”).

Debate question bank (short, discussion-friendly):

• “Is it better to fix the process first or collect more data first? Defend your choice.”
• “When evidence conflicts, should you average the results or choose the most reliable source? Why?”
• “What’s the risk of optimizing a metric that you can measure easily but doesn’t reflect the real problem?”

Online quiz question examples (with explanation required):

• Scenario: “A customer reports slow performance right after an update.”
  Question: “Which is the best next step?”
  Answer choices: (A) Revert immediately (B) Check logs and isolate the change (C) Ask all users for feedback (D) Increase server capacity without testing.
  Explanation requirement: “In 4–6 sentences, explain what evidence you’d look for and why.”
• Scenario: “A project is late, but stakeholders disagree on what’s causing delays.”
  Question: “What’s the most useful first activity?”
  Answer choices: (A) Assign blame (B) Create a shared problem definition + timeline evidence (C) Add more features (D) Hold an extra meeting without data.
  Explanation requirement: “Identify one assumption you think each stakeholder might be making.”

And yes, I still use guiding questions — but I keep them concrete, like: “What evidence supports this?” and “What could go wrong if we’re wrong about the cause?”

If you want more ways to keep students thinking independently in an online format, check [online community engagement tips](https://createaicourse.com/student-engagement-techniques/). The key is that discussion posts should include reasoning, not just agreement.

Step 5: Follow Best Practices for Course Design

Good course design isn’t just “pretty slides.” It’s what prevents students from dropping off and what makes practice possible.

Here’s what I recommend (with specifics):

• Start with measurable objectives. Example: “Given a scenario, the learner can produce a testable problem definition and a 2-intervention test plan.”
• Build complexity gradually. Week 1 problems should be smaller and more structured; by Week 4, let the scenario include conflicting info.
• Chunk content. Aim for 5–10 minute learning segments, then immediately practice.
• Use varied practice. Mix reading + worked examples + scenario writing + peer review.
• Include reflection that leads to revision. Don’t just ask “what did you learn?” Require a “before/after” edit to their solution.
• Use multimedia to reduce cognitive load. Short videos, simple diagrams, and simulations help students see the workflow.

Course syllabus template (problem-solving module example):

• Lesson 1: Problem definition & evidence (worksheet + worked example)
• Lesson 2: Root cause methods (five whys + hypothesis checklist)
• Lesson 3: Option generation & tradeoffs (decision criteria practice)
• Lesson 4: Testing with PDCA (small experiment design)
• Lesson 5: Reflection & iteration (revise solution based on feedback)

For more on organizing your content, use [course structure tips](https://createaicourse.com/course-structure/).

One quick note: I’m not going to throw around massive enrollment numbers without a clear source and definition. What matters more is whether your course design supports practice, feedback, and revision — and whether students can demonstrate the skill.

Step 6: Measure Effectiveness and Make Improvements

Tracking success is where courses either improve… or stay stuck. If you only measure completion, you’ll miss whether students actually learned to solve problems.

Here’s a measurement plan that’s realistic:

1) Pre/post assessment (problem-solving reasoning)
Use the same scenario type before and after the module.

• Pre: Students write a diagnosis + plan (10–15 minutes).
• Post: Students solve a similar scenario using the same rubric.
• Target: Aim for an average rubric improvement of 20–30% from pre to post.

2) Rubric-based grading (not just “correct/incorrect”)
Track rubric categories like:

• Problem definition clarity
• Root cause quality
• Options & tradeoffs
• Decision justification
• Measurement plan

3) Engagement signals (to find where students get stuck)
Look at metrics like:

• Drop-off points by lesson (where do they stop?)
• Time spent on practice activities (too low can mean confusion or disengagement)
• Participation rates in discussions
• Submission rates for drafts (do they revise?)

4) Feedback that leads to changes
Use a short survey with questions like:

• “Which activity helped you most with diagnosing problems?”
• “Where did you feel least confident — defining, diagnosing, or deciding?”
• “Did the feedback help you revise your solution?”

5) Decide what to change (simple rules)

• If Root Cause Quality scores are low, add more worked examples and a guided worksheet stage.
• If students can pass quizzes but fail projects, you likely need more explanation-based assignments.
• If most students don’t revise, your feedback may be too vague or too late.

And don’t ignore your own teaching data: are you giving enough opportunities for students to practice with real reasoning? If not, no amount of content will fix it.

If you want more continuous improvement ideas, revisit [course evaluation tips](https://createaicourse.com/effective-teaching-strategies/) and adapt them to your rubric and assessments.

Do this loop a couple times, and you’ll see the course shift from “teaching problem solving” to actually developing problem solvers.

FAQs