ALM Method /Pedagogy / Rosaline Nirmala /snsinstutions

Описание: Active learning refers to instructional methods that engage students directly in the learning process, rather than passive reception of information (e.g., through lectures alone). It involves activities where students think, discuss, investigate, create, or apply knowledge. Research shows it significantly improves retention, understanding, and performance compared to traditional lecturing—e.g., a meta-analysis of 225 STEM studies found students in active learning classes scored 6% higher on exams and were 1.5 times less likely to fail.
Benefits

Enhances higher-order thinking (analysis, synthesis, evaluation).
Builds skills like problem-solving, collaboration, and critical thinking.
Increases motivation by making learning personal and interactive.
Narrows achievement gaps for underrepresented students.

Common Active Learning Methods/Strategies
Here are some widely used techniques, with brief descriptions and examples:

Think-Pair-Share: Students individually think about a question, pair up to discuss, then share with the group. Promotes reflection and peer learning.
Jigsaw: Groups divide a topic into parts; each member becomes an "expert" on one part, then teaches it to others. Encourages accountability and collaboration.
Muddiest Point: Students write down the most confusing concept from a lesson; instructors address them next class. Quick feedback tool.
Flipped Classroom: Students review material (e.g., videos) before class; class time focuses on discussions, problem-solving, or activities.
Gallery Walk: Groups rotate between stations, adding to posters or solutions started by others. Builds on collective ideas.
Four Corners: Students move to a corner labeled with answer choices (e.g., A/B/C/D) for a question, then debate/defend their choice.
Minute Paper: At lesson's end, students write a quick summary or answer key questions (e.g., "What was the most important point? What remains unclear?").
Role-Playing/Simulations: Students act out scenarios to apply concepts (e.g., historical debates or scientific processes).
Problem-Based Learning: Students work in groups on real-world problems, researching and proposing solutions.
Peer Instruction: Students answer concept questions individually, discuss
with peers, then revote; instructor clarifies misconceptions.

These can be adapted for in-person, online, or hybrid settings and scaled from short (2-10 minutes) to full-class activities.
Active Learning in Machine Learning
Active learning is a semi-supervised machine learning technique where the model interactively queries a human (or oracle) to label the most informative unlabeled data points, minimizing labeling effort while maximizing performance. It's especially useful when labeled data is scarce or expensive.
Key Approaches/Sampling Methods

Uncertainty Sampling: Query points the model is most uncertain about (e.g., lowest confidence or closest to decision boundary in SVMs).
Query by Committee: Multiple models "vote" on labels; query points with highest disagreement.
Expected Model Change: Select points that would most change the current model if labeled.
Density-Weighted Methods: Favor uncertain points in dense regions to improve generalization.
Stream-Based vs. Pool-Based: Stream evaluates points one-by-one; pool ranks a large unlabeled set.
Batch Active Learning: Query multiple points at once for efficiency in deep learning.

Deep active learning variants (e.g., Bayesian approaches) address challenges in neural networks, like high computational cost.
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