- Pre-Implementation Artefacts (30%)
- Project Implementation (50%)
- Post-Implementation Review (20%)
- Grading Summary
- Plagiarism Guidelines for Learners
- Plagiarism Guidelines Summary
Criteria:
- Achieve a minimum overall score of 50%.
- No component (Pre-Implementation Artefacts, Project Implementation, Post-Implementation Review) falls below a pass mark in its category.
- Basic adherence to project requirements, showing understanding and execution of the project plan, user stories, design, and implementation.
Justification:
- A pass indicates the learner has met the essential requirements of the project, demonstrating a foundational understanding and ability to execute the tasks, albeit with potential for improvement in detail and execution.
Note to Learners:
Achieving a pass grade is sufficient to demonstrate the competencies required to pursue a career in the field. While noteworthy feedback highlights exceptional work, it is not a formal grade category. Employers value the practical skills and understanding you have developed through this project, and a pass grade is a strong indicator of your readiness for the job market.
Criteria:
- Achieve an overall score of 70% or higher.
- All components (Pre-Implementation Artefacts, Project Implementation, Post-Implementation Review) are executed to a high standard, with exceptional detail and quality.
- Comprehensive and detailed project plan, user stories, design documentation, exceptional code quality, seamless AI integration, and flawless functional implementation.
Justification:
- A commendation represents a high level of achievement, indicating exceptional understanding and execution. This grade is reserved for work that is not only free of significant issues but is also innovative, detailed, and exceptionally well-documented, reflecting a deep understanding and mastery of the project requirements and tools.
The commendation serves as a detailed feedback mechanism within the fail/pass grading structure. It highlights exceptional work and provides constructive feedback, encouraging learners to continue improving their skills and aiming for excellence. The commendation is not a formal grade but rather a form of feedback. Here’s how the commendation functions within this framework:
The commendation acts as a feedback mechanism to acknowledge areas where the learner has exceeded expectations. This feedback is meant to guide learners on their strengths and encourage them to maintain or improve upon these areas in future work.
Even though it is not a formal grade, the commendation can motivate learners by recognizing their hard work and exceptional performance in certain aspects of the project. This recognition can inspire learners to continue striving for high-quality work.
The commendation provides a benchmark for what constitutes work that is beyond basic requirements. It helps learners understand what high-quality work looks like, even if it does not result in a higher formal grade.
By highlighting specific commendable aspects, the feedback helps learners understand what they did well and how they can continue to develop these skills. This constructive feedback is essential for continuous improvement.
Fail (0-4%):
- Evidence: The project plan lacks structure. User stories are either missing or very generic without specific tasks or acceptance criteria.
- Example: "Create homepage" without any details or acceptance criteria.
Pass (5-6%):
- Evidence: The project plan includes basic user stories generated with GitHub Copilot, organized into categories with priorities. Acceptance criteria are present but lack detail.
- Example: "As a user, I want a homepage so that I can see an introduction to the site." Acceptance criteria: "Homepage should include a welcome message."
Commendation (7-10%):
- Evidence: Detailed project plan with well-organized user stories. Each user story includes clear priorities and detailed acceptance criteria. Evidence of original work.
- Example: "As a user, I want a homepage with a welcome message, an overview of services/products, and a dynamically updated feature." Acceptance criteria: "Homepage should include a header with a welcome message, a section for services/products with icons, and a dynamically updated feature."
Fail (0-4%):
- Evidence: Missing or incomplete wireframes and UX design documentation. No consideration for accessibility. Lack of original design work.
- Example: No wireframes or mockups submitted.
Pass (5-6%):
- Evidence: Basic wireframes and UX design documentation with some accessibility considerations. Limited originality.
- Example: Simple wireframes showing layout of homepage and services/products page with basic notes on accessibility.
Commendation (7-10%):
- Evidence: Detailed wireframes and comprehensive UX design documentation, including accessibility considerations and user interaction details. Original design work.
- Example: Wireframes for each page (homepage, services/products, contact) with detailed notes on layout, navigation, and accessibility features like ARIA labels and keyboard navigation.
Fail (0-2%):
- Evidence: GitHub repository not properly set up. Minimal commits. Lack of version control practices.
- Example: Only one branch with initial commit.
Pass (3%):
- Evidence: Basic setup of GitHub repository with a simple branching strategy. Evidence of regular commits.
- Example: Main branch and one feature branch with commits for initial setup.
Commendation (4-5%):
- Evidence: Well-organized GitHub repository with regular commits documenting key stages of development. Clear evidence of version control practices.
- Example: Main branch, development branch, and multiple feature branches with regular commits.
Fail (0-2%):
- Evidence: Missing or unclear plan for using AI tools. No documentation of AI usage.
- Example: No mention of AI tool usage in the project plan.
Pass (3%):
- Evidence: Basic description of AI tool usage in the project. Limited documentation of AI usage.
- Example: "Use GitHub Copilot for generating HTML/CSS code."
Commendation (4-5%):
- Evidence: Detailed plan for AI tool usage, highlighting key stages and expected benefits. Comprehensive documentation of AI usage.
- Example: "Use GitHub Copilot for generating user stories, HTML structure, and CSS enhancements. Use DALL-E for creating images."
Fail (0-6%):
- Evidence: Poor code quality with inconsistent indentation, naming conventions, and poor use of HTML5/CSS3 standards. Potential evidence of copied code.
- Example: Code with mixed indentation styles, non-descriptive variable names, and invalid HTML/CSS.
Pass (7-9%):
- Evidence: Basic code quality with mostly consistent indentation and naming conventions. Some adherence to HTML5/CSS3 standards. Limited originality.
- Example: Code with consistent indentation and descriptive names, but minor HTML/CSS issues.
Commendation (10-15%):
- Evidence: High code quality with consistent indentation, meaningful naming conventions, and good adherence to HTML5/CSS3 standards. Clear evidence of original work.
- Example: Well-structured code with consistent indentation, descriptive names, and valid HTML/CSS.
Fail (0-6%):
- Evidence: Poor or ineffective use of AI tools. Minimal integration of AI-generated code. No documentation of AI usage.
- Example: Generated code not used or poorly integrated into the project.
Pass (7-9%):
- Evidence: Basic use of AI tools with some integration and critical assessment of AI-generated code. Limited documentation of AI usage.
- Example: AI-generated code is used but not thoroughly reviewed or optimized.
Commendation (10-15%):
- Evidence: Effective and innovative use of AI tools with good integration and critical assessment of AI-generated code. Comprehensive documentation of AI usage and customization.
- Example: AI-generated code is integrated, reviewed, and optimized for project requirements. Comments and documentation clearly indicate the rationale for using AI-generated code and any modifications made.
Fail (0-6%):
- Evidence: Incomplete or non-functional implementation. User stories' acceptance criteria not met. Evidence of copied work.
- Example: Homepage missing key sections, broken links, or non-functional features.
Pass (7-9%):
- Evidence: Basic functional implementation meeting most acceptance criteria with some issues. Limited originality.
- Example: Homepage with welcome message and services/products section, but some interactive elements not working properly.
Commendation (10-20%):
- Evidence: High-quality functional implementation meeting all acceptance criteria with minor issues. Clear evidence of original work and adaptation of AI-generated code.
- Example: Fully functional homepage with all required sections working, minor UI/UX issues. Documentation and comments show how AI-generated code was adapted and customized.
Fail (0-4%):
- Evidence: Poor user experience with significant usability issues and unappealing design. No consideration for accessibility.
- Example: Cluttered layout with poor navigation and no responsiveness.
Pass (5-6%):
- Evidence: Basic user experience with some usability and design issues. Some consideration for accessibility.
- Example: Simple and functional design with basic accessibility features.
Commendation (7-10%):
- Evidence: High-quality user experience with intuitive design, smooth navigation, and responsiveness. Thorough consideration for accessibility.
- Example: Intuitive and appealing design with smooth navigation and responsive layout.
Fail (0-4%):
- Evidence: Incomplete or non-functional final project submission. Missing source code repository or deployment link. Potential evidence of plagiarism.
- Example: Project not deployed, source code repository incomplete.
Pass (5-6%):
- Evidence: Complete final project submission with functional web application, source code repository, and deployment link. Limited originality.
- Example: Deployed web application with complete source code repository.
Commendation (7-10%):
- Evidence: High-quality final project submission with well-documented source code repository, functional and well-designed web application. Clear evidence of original work.
- Example: Well-documented source code repository, functional web application with good design.
Fail (0-4%):
- Evidence: Incomplete or inadequate project documentation. No explanation of design decisions or code structure. Potential evidence of copied work.
- Example: Missing or very brief documentation with no explanation of design decisions or code structure.
Pass (5-6%):
- Evidence: Basic project documentation covering essential aspects. Limited originality.
- Example: Basic documentation explaining main design decisions and code structure.
Commendation (7-10%):
- Evidence: Comprehensive project documentation with detailed design decisions, code structure, and deployment process. Clear evidence of original work.
- Example: Detailed documentation explaining design decisions, code structure, and deployment process.
Fail (0-4%):
- Evidence: Missing or superficial retrospective report. No meaningful reflection. Potential evidence of copied content.
- Example: No report or very brief summary without meaningful reflection.
Pass (5-6%):
- Evidence: Basic retrospective report with reflection on development process and some insights. Limited originality.
- Example: Report summarizing development process with basic reflection on successes and challenges.
Commendation (7-10%):
- Evidence: Detailed retrospective report with clear reflection on development process and actionable insights. Clear evidence of original work.
- Example: Detailed report reflecting on development process, successes, challenges, and lessons learned.
To achieve a Pass grade, learners must:
- Score between 5-6% in each rubric category.
To achieve a Commendation grade, learners must:
- Score between 7-10% in each rubric category.
Guidelines on AI Tool Usage:
- Use Responsibly: While using AI tools like GitHub Copilot, ensure that the code you submit is your own. You should be able to explain all AI-generated content.
- Document Usage: Clearly document where and how AI tools were used in your project, specifying the contributions made by AI versus your own work.
Progress Submissions:
- Intermediate Deliverables: Submit drafts of your project plan, wireframes, and initial code. These should show the evolution of your project and your understanding at each stage.
Version Control Analysis:
- Regular Commits: Make regular commits to your version control system (e.g., GitHub) to document your progress. Each commit should have a clear message detailing the changes made.
- Detailed Contribution Reports: Include a report explaining your contributions at each stage of the project. Clearly differentiate between AI-generated code and your own work.
Plagiarism Detection:
- Use of Tools: Be aware that plagiarism detection tools will be used to scan your code and documentation for similarities with other sources. Ensure that all content is original or properly cited.
- Original Content: Your project plan and user stories must be your own work. Avoid copying from other sources without proper attribution.
- Specificity and Detail: Ensure that your user stories are specific to your project scenario and not generic templates from the internet.
- Create Your Own Designs: Wireframes and UX design documentation should be original. If you use design tools or templates, customize them significantly to reflect your unique project requirements.
- Accessibility Considerations: Document how you have considered accessibility in your design. This should be based on your understanding and application of accessibility standards.
- Consistent Use: Use your version control system consistently. Regular, descriptive commits will help demonstrate your ongoing work and understanding.
- Branching Strategy: While branching is not required, document any strategies used to manage different parts of your project.
- Clear Usage Plan: Document your plan for using AI tools. Specify which parts of the project will use AI-generated code and how you will ensure the quality and originality of this code.
- Adhere to Standards: Ensure your code meets HTML5/CSS3 standards and follows best practices for readability and maintainability.
- Review and Optimize: Review and optimize AI-generated code. Clearly document any modifications or optimizations you make.
- Document AI Use: Clearly document where AI-generated code is used. Include comments in the code to highlight AI-generated sections.
- Critical Assessment: Critically assess AI-generated code for quality and relevance. Make necessary adjustments to ensure it fits your project requirements.
- Meet Acceptance Criteria: Ensure your implementation meets all user stories' acceptance criteria. Document any challenges and how you addressed them.
- Original Implementation: Avoid copying implementation details from other sources. Your solution should be unique to your project scenario.
- Ensure Quality UX: Focus on creating a high-quality user experience, with emphasis on design aesthetics, usability, and responsiveness.
- Document Decisions: Record design decisions that enhance the user experience.
- Complete Submission: Ensure your final project submission includes a fully functional web application, source code repository, and deployment link.
- Proper Attribution: If you use any external resources or libraries, provide proper attribution and documentation.
- Reflect Honestly: Your retrospective report should honestly reflect your development process, challenges faced, and lessons learned. It should be your own analysis and not copied from other sources.
- Use AI Tools Responsibly: Ensure you understand and can explain all AI-generated content.
- Document Everything: Clearly document the use of AI tools, progress, and contributions.
- Submit Original Work: Your project plan, user stories, designs, code, and retrospective report must be original and specific to your project.
- Use Version Control: Make regular, descriptive commits to document your progress and contributions.
- Attribute Properly: Properly cite any external resources or libraries used in your project.
By following these guidelines, you can ensure that your work is original, demonstrates your understanding, and adheres to academic integrity standards. Assessors will use these documented processes and tools to verify the originality and authenticity of your work.