Matlab Create Hands On Exercises

MATLAB Hands-On Exercises

Purpose

Guide learners through active MATLAB practice. Exercises must be complete, runnable MATLAB scripts when assessment is involved, and the tutor must execute those scripts through MATLAB tools before judging correctness.

The goal is to provide MATLAB Grader-style formative assessment without requiring MATLAB Grader: create an exercise, run the learner's code in MATLAB, compare script outputs against expected values, inspect programming style with Code Analyzer in MATLAB, and give targeted feedback.

For instructors, this skill turns tutoring into a small formative assessment. Students still receive coaching, but the tutor also checks whether the code actually runs and whether the produced outputs match the learning objective.

Exercise Loop

1. State the goal in one sentence.
2. Define expected outputs and assessment criteria before the learner starts.
3. Give a complete script scaffold with a clearly marked learner section.
4. Ask the learner to predict, fill in, or revise the learner section.
5. Save the complete script as a temporary .m file.
6. Apply the execution preflight in references/execution-safety.md, which includes running check_matlab_code; do not run it a second time.
7. Run run_matlab_file on the script and inspect the MATLAB output.
8. Compare produced variables, values, sizes, classes, errors, and required or forbidden functions against the assessment criteria.
9. Give targeted feedback and one extension or revision prompt.

Never mark an assessable exercise correct from visual inspection alone. If the exercise has expected output, run the complete script in MATLAB and evaluate the actual output.

Exercise Types

• Trace: Predict workspace variables after each line.
• Edit: Modify a snippet to meet a requirement.
• Debug: Diagnose an error message and fix the root cause.
• Refactor: Replace fragile or verbose code with clearer MATLAB.
• Test: Write a matlab.unittest test for a function.
• Analyze: Import or summarize a tiny dataset.
• Visualize: Create or improve a plot.

Use the shortest exercise that can reveal the misconception. A five-line script that exposes row-versus-column behavior is often more useful than a large project when the goal is concept formation.

Starter Exercise Pattern

Read references/exercise-patterns.md for reusable exercise formats.

Read references/script-assessment-patterns.md when creating a complete runnable script, output checks, MATLAB Grader-style assessments, tolerance-based comparisons, or Code Analyzer feedback.

Read references/execution-safety.md before running learner-provided or generated MATLAB scripts.

Safety and Academic Integrity

• For homework-like prompts, ask for the learner's attempt first.
• Treat learner code as untrusted input. Perform the execution safety preflight before running scripts.
• Do not run large or destructive code. Keep practice files small and temporary.
• Always explain what MATLAB script was run, which checks passed or failed, and what the output means.
• Avoid file I/O, network calls, delete, rmdir, shell commands, or long simulations unless the learner's explicit task requires them and the path is temporary and scoped.

Feedback

Feedback should be specific:

• Identify the MATLAB rule involved.
• Point to the exact expression or line.
• Report the relevant MATLAB output, variable value, size, class, error, or Code Analyzer message.
• Explain how to inspect evidence next time.
• Give one revised attempt or next prompt.

Assessment Policy

Assess scripts with the same broad categories MATLAB Grader uses for script assessment:

• expected variable exists;
• expected variable has the right class, size, and value;
• numeric values are compared with an explicit tolerance;
• required functions or keywords are present when the learning objective calls for them;
• prohibited functions or shortcuts are absent when the exercise is about a specific programming concept;
• custom checks verify plots, tables, errors, or edge cases when variable equality is insufficient.

For course pilots, make the expected output explicit before the learner starts. This helps instructors compare student attempts, AI feedback, and MATLAB execution evidence.