Ghana curriculum lesson note
SHS 2 Computing 2nd Semester Week 10 Lesson Plan
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Create Lesson Plan| Weekly Learning Plan | |||
| Subject | Computing | Week | 10 |
| Duration | 60 minutes | Form | SHS 2 |
| Strand | COMPUTATIONAL THINKING (PROGRAMMING LOGIC) | Sub-Strand | APP DEVELOPMENT |
| Learning Outcome(s) | 2.2.2.LO.1 - Demonstrate an understanding of fundamental concepts in text-based programming and the programming process, and apply acquired skills to create new functions | ||
| Content Standard | 2.2.2.CS.1 | ||
| Learning Indicator(s) | 2.2.2.LI.2 - Utilise simulation tools in Machine Learning to train a computer to perform specific actions or sets of actions. Problem-Based Learning Approach: • Apply algorithms to design programmes for various tasks, such as calculating the sum of two numbers or computing the area of geometric shapes (rectangle, circle, triangle, etc.). Demonstrate the use of variables and different data types (integers, strings, etc.) in programming | ||
| Lesson Focus | Utilise simulation tools in Machine Learning to train a computer to perform specific actions or sets of actions. | ||
| Previous Knowledge | Learners recall related ideas, vocabulary or experiences from earlier lessons and everyday contexts. | ||
| Lesson Objective(s) | Describe the key idea in: Utilise simulation tools in Machine Learning to train a computer to perform specific actions or sets of actions. Apply the idea through guided and independent learning activities. Demonstrate understanding through oral responses, written work or practical performance. | ||
| Essential Question(s) | What computing concept, system, algorithm or technology is central to Utilise simulation tools in Machine Learning to train a computer to perform specific actions or sets of actions. Problem-Based Learning Approach: • Apply algorithms to design programmes for various tasks, such as calculating the sum of two numbers or computing the area of geometric shapes (rectangle, circle, triangle, etc.). Demonstrate the use of variables and different data types (integers, strings, etc.) in programming? How can we model, test or build the concept using hardware, software, code, diagrams or simulation? How can this knowledge be applied to solve a new real-life computing problem? | ||
| Pedagogical Strategies | Problem-Based Learning Collaborative Learning Pair Programming/Peer Review where appropriate Demonstration and Guided Practice Project-Based Learning Digital/Simulation-Based Learning Computational Thinking Debugging/Test-Driven Practice | ||
| Teaching & Learning Resources | Computer(s) where available Projector/TV where available Computing textbook/reference materials Whiteboard/markers Learners' notebooks Notepad or exercise book Pen Smartphones Laptops Productivity tools ix. Subject-based application software Desktop computers Tablets TV and radio Open Educational Resources (including YouTube, MOOCs - Udemy/Coursera, Khan Academy, and TESSA) The iBox/iCampus (CENDLOS) Flowchart/pseudocode sheets Programming environment or simulator where available Code editor/IDE or visual programming environment Sample code files Web browser Code editor Sample webpages/database files where relevant | ||
| Key Notes on Differentiation | |||
| Content | Use labelled diagrams, step-by-step worked examples, starter code, partially completed flowcharts or guided configuration sheets for learners who need support. Extend advanced learners with optimisation, debugging, system-design trade-offs, additional features or more complex test cases. | ||
| Process | Use mixed-ability grouping and pair work, rotating roles such as driver, navigator, tester, documenter or presenter. Where devices or internet access are limited, use offline simulators, printed code/diagrams, unplugged activities and shared-device rotations without changing the learning objective. | ||
| Product | Allow appropriate evidence such as annotated diagrams, algorithms, pseudocode, code, app screens, webpages, network designs, troubleshooting reports or presentations. Assess correctness, computational reasoning, functionality, testing/debugging, documentation, responsible digital practice and ability to justify design decisions. | ||
| Success Criteria | Learners use correct subject vocabulary. Learners complete the main task with reasonable accuracy. Learners explain or demonstrate how the concept applies in a new situation. | ||
| Homework | Complete a short application task connected to the week's learning indicator. | ||
| Lesson Activities | |||
| Stage | Teacher Activity | Learner Activity | Assessment / DoK |
| Starter10 minutes | Show a simple digital example involving utilise simulation tools in machine learning to train a computer to perform specific actions or sets of actions and ask learners to predict what is happening. | Share prior knowledge, listen to peers and record the lesson question in their notebooks. | Not provided. |
| Activity 115 minutes | Demonstrate utilise simulation tools in machine learning to train a computer to perform specific actions or sets of actions using a board sketch, device setting, code snippet, data table or simulation. | Observe the demonstration, identify the key parts and explain their functions. | Ask learners to define the key term and identify it in the demonstration. |
| Activity 220 minutes | Guide learners through a hands-on task where they apply utilise simulation tools in machine learning to train a computer to perform specific actions or sets of actions and record the result. | Complete the practical task, test the result and note any errors or fixes. | Check the task output for correct procedure, testing and explanation. |
| Activity 310 minutes | Present a different digital scenario and ask learners to use utilise simulation tools in machine learning to train a computer to perform specific actions or sets of actions to solve or explain the result. | Apply the idea to the new scenario and present the result or explanation. | Use a short exit task requiring learners to apply the idea in another digital context. |
| Lesson Closure | Summarise utilise simulation tools in machine learning to train a computer to perform specific actions or sets of actions and correct one common misconception using learner examples. State one thing learned and complete the exit response. | ||
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