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Kickoff: What problem are we solving about student trends?

Welcome!

Let’s Spot the Problem Type
(classification, regression, clustering)

Pick a Simple Model that Fits the Data You Have

Match Metrics to the Mission
(accuracy, precision, recall, F1, RMSE, MAE)

Check your understanding: quick concept quiz

Quiz A: Identify the problem type fast

Quiz B: Pick the right starter model

Quiz C: Choose the right metric

Quiz D: Tie it all together (scenario 1)

Quiz E: Tie it all together (scenario 2)

Choosing ML Models and Metrics for Student Engagement Data is a practical, hands-on course that teaches you exactly how to map real school scenarios to the right machine learning approach—and prove it with the right metrics. If you’ve ever wondered whether a problem is classification, regression, or clustering, or which metric (accuracy, precision, recall, F1, RMSE, MAE) truly reflects your school’s goals, this course gives you a clear, repeatable playbook. Through relatable student-life cases—like predicting who will like a new AI study buddy, estimating weekly study hours, or discovering natural learning-style groups—you’ll practice making defensible choices that educators and stakeholders can understand. You’ll learn when to start simple with logistic or linear regression, when decision trees improve interpretability, and how to make k-means useful with thoughtful scaling and validation. We emphasize ethical, explainable decisions tailored to imbalanced data and real constraints such as missing survey responses and small sample sizes. By the end, you’ll confidently justify your model and metric choices, communicate trade-offs, and deliver insights that actually help students and teachers take action.

Choosing Machine Learning Models and Metrics

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            <p>Every day, schools collect clues about how students learn: which apps you open, when you study, what formats you prefer, and how your workload changes. This course helps you turn those clues into smart, fair decisions by choosing the right machine learning problem type, a simple starter model, and the metric that actually reflects your goal. You’ll learn to spot whether a situation is classification (yes/no labels), regression (predicting a number), or clustering (finding natural groups), using relatable mini-cases like “Who will like a new study tool?”, “How many hours might someone study next week?”, and “What learning style groups appear?”</p>

<p>Once you can name the problem type, you’ll pick models that match your data and constraints: logistic regression, decision trees, and k-NN for classification; linear regression for numeric predictions; and k-means for discovering groups. You’ll also practice choosing metrics that fit school realities. For example, use accuracy when classes are balanced, precision when you want to avoid recommending stuff people won’t use, recall when you don’t want to miss students who need help, and F1 when both precision and recall matter. For numbers, compare RMSE and MAE; for clusters, try the elbow method or silhouette score and sanity-check whether the groups make sense to teachers and students.</p>

<p>After the kickoff concepts, you’ll dive into three student-focused cases. In Case 1, you’ll predict who will like a new AI study buddy and decide when accuracy is enough versus when F1, precision, or recall is smarter—especially if only a small fraction liked it before. In Case 2, you’ll estimate weekly study hours and see why big mistakes might matter more than small ones when planning support. In Case 3, you’ll group students by learning preferences and time of day, then test whether the clusters are actually useful for personalizing resources.</p>

<p>To lock it all in, you’ll take quick concept quizzes to identify problem types, pick starter models, and match metrics to the mission. You’ll finish with three justify-your-choice challenges where you explain not just what you chose, but why it fits the data and the school goal. By the end, you won’t just “run a model”—you’ll make clear, confident choices that connect student engagement data to better learning experiences.</p>
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