IB Personal Project (MYP) External Assessment Exemplar

Rich reflection on collaborative ATL skills, describing how motivating peers, integrating diverse perspectives, and iterative teamwork improved the codebase.

Effective use of self-management ATL skills, including Pomodoro technique, detailed task lists, and a Gantt chart to monitor progress and meet deadlines.

Unclear how time-management strategies directly impacted milestone completion and product quality—for example, linking mentor review sessions to the on-time delivery of specific modules.

Research strategies lack specificity: the report should cite particular tools or data sources evaluated and explain how these informed the ML approach.

Communication challenges are described, but there is no detail on how improved explanations led to concrete product refinements or positive stakeholder feedback.

Comprehensive and well‐justified product success criteria paired with appropriate testing methods for aesthetics, functionality, and data quality.

Well‐sequenced action plan with clear dates, tasks, and linked success criteria, demonstrating structured project management.

Precise and ambitious learning goal convincingly linked to personal passion for coding, supported by concrete examples from Scratch projects and ML internships.

Lacks explicit quantitative targets within each success criterion (e.g., MSE <50, GUI load time <30 s) to enhance measurability and assessment clarity.

Does not specify the resources and dependencies (software tools, data access, mentor feedback) required for each task, which would strengthen feasibility and realistic sequencing.

Deep personal growth reflection linking trial-and-error in coding and publishing to increased resilience, decision-making, and problem-solving skills.

Robust aesthetic evaluation using interface screenshots and survey data (100% Excellent) to validate design success criteria.

Comprehensive data content evaluation, showing analysis of 4,000 datasets from diverse sources mapped to OHLC requirements.

Thorough GUI evaluation referencing Matplotlib performance, 30 s load time, and user survey results, directly tied to success criteria.

Effective resource efficiency assessment, with software size <100 MB and a balanced codebase aligning with resource criteria.

Exploratory data analysis should be extended to B-grade companies to ensure consistency and completeness across all data segments.

Reflections would benefit from explicit mapping of each challenge or insight to the corresponding success criterion to sharpen focus.

CPU usage peaked at 80% instead of the target 20%; the student should propose code optimization or resource-scaling strategies to meet efficiency benchmarks.

Security concerns raised by 30% of users need to be addressed through concrete plans (e.g., third-party audits, enhanced privacy measures) to align with safety criteria.

The ARIMA algorithm currently supports only one-day predictions; the student should either expand its capability for multi-day forecasting or revise the success criteria accordingly.