Artificial Intelligence and Data Science

ARTIFICIAL INTELLIGENCE AND DATA SCIENCE

Mind Mapping is a powerful learning strategy that enhances understanding, creativity, and retention. It is widely used in education, business, and personal planning for effective knowledge management.

Outcome

STEP 3: Teach the home group

STEP 2: Meet in “Expert Groups”

Innovation Teaching Method

1. Jigsaw method

STEP1: Form homegroups and assign subtopics

Using the Think–Pair–Share method, students first think individually about problem types like Important Problems, the N-Queens Problem, and Branch and Bound techniques. Then they pair up to discuss their approaches and compare solutions.
Finally, pairs share their insights with the class, refining understanding through discussion. The outcome is stronger problem-solving skills, clearer concepts, and improved confidence in algorithmic thinking.

Outcome

STEP 3: Share (Group Discussion)

STEP 2: Pair (Peer Discussion)

2. Think-Pair-Share

STEP 1: Think (Individual Reflection)

In Peer-to-Peer Learning, students explain Supervised Learning and Unsupervised Learning concepts to each other in small groups. Each learner shares examples (like classification vs clustering) and clarifies doubts collaboratively. This mutual teaching strengthens understanding through active discussion and real-life connections. The outcome is better concept clarity, communication skills, and deeper retention of machine learning basics.

Outcome

STEP 3: Finally review about the discussed topic from students

STEP 2: Active Interaction and Knowledge Exchange

3. Peer to Peer Learning

STEP 1: Preparation and Goal Setting

Using Simulations & Virtual Labs, students practice Tableau software execution in a guided, hands-on virtual environment. They simulate real-world tasks like data importing, visualization, and dashboard creation. This interactive approach helps them learn by doing and experimenting safely. The outcome is improved practical skills, better understanding of data visualization, and increased confidence in using Tableau.

Outcome

STEP 3 : Simulation and Execution

STEP 2: Active Learning

4. Simulations & Virtual Labs

STEP 1: Preparation and Setup

Using Application-Based Learning, students work on real projects involving the 8085 Microprocessor, such as programming simple arithmetic or interfacing with sensors. By applying theory to practical tasks, they see how instructions, registers, and memory work in real scenarios. The outcome is enhanced hands-on skills, deeper understanding of microprocessor operations, and improved problem-solving ability.

Outcome