Chameleon-company/MOP-Code
Aug 2025 – Aug 2025
1 Month active
Languages Used
Jupyter NotebookPythonSPSS
Technical Skills
Data AnalysisData WranglingDocumentationJupyter NotebookPythonSPSS
HTMATHS158
PROFILE
Htmaths158
During August 2025, Htmaths158 developed a reusable education analytics notebook for the Chameleon-company/MOP-Code repository, focusing on analyzing factors impacting student performance. Leveraging Python, Jupyter Notebook, and SPSS, Htmaths158 implemented a robust SPSS data ingestion function to streamline future analyses and enhance reproducibility. The project incorporated background context from both PISA and Victorian datasets, laying groundwork for cross-context educational analysis. Iterative enhancements were documented through versioned commits, reflecting a methodical approach to data wrangling and documentation. While no bugs were reported or fixed, the work established a solid foundation for scalable, data-driven insights in educational analytics pipelines.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
288
Activity Months1
Your Network
98 people
Work History
August 2025
2 Commits • 1 Features
Aug 1, 20252025-08 Monthly Summary for Chameleon-company/MOP-Code. Focused on delivering a reusable education analytics notebook and establishing SPSS data ingestion to accelerate data-driven insights into factors affecting student performance. No critical bugs reported this month. The work lays a foundation for cross-context analysis (PISA and Victorian context), improves reproducibility, and prepares the data pipeline for future datasets.
2 Commits • 1 Features
Aug 1, 20252025-08 Monthly Summary for Chameleon-company/MOP-Code. Focused on delivering a reusable education analytics notebook and establishing SPSS data ingestion to accelerate data-driven insights into factors affecting student performance. No critical bugs reported this month. The work lays a foundation for cross-context analysis (PISA and Victorian context), improves reproducibility, and prepares the data pipeline for future datasets.
August 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonSPSS
Technical Skills
Data AnalysisData WranglingDocumentationJupyter NotebookPythonSPSS
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