PrairieLearn/PrairieLearn
Jan 2025 – Feb 2026
10 Months active
Languages Used
MarkdownPLpgSQLSQLJavaScriptPythonTypeScript
Technical Skills
Backend DevelopmentDatabaseDevOpsDocumentationSQLAPI Integration
Matthew West
PROFILE
Matthew West
Over ten months, contributed to PrairieLearn/PrairieLearn by building analytics features, strengthening data integrity, and enhancing both backend and frontend reliability. Developed SQL-driven reporting for course analytics, implemented data pipelines for usage tracking, and improved grading workflows by refining database migrations and schema design. Addressed critical bugs affecting real-time socket programming with Redis and stabilized UI components using JavaScript and TypeScript. Enhanced onboarding and user experience through comprehensive documentation updates and technical writing. The work demonstrated a methodical approach to backend development, data processing, and DevOps, resulting in more accurate analytics, reliable grading, and improved usability for instructors and students.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
24Total
Bugs
9
Commits
24
Features
9
Lines of code
2,457
Activity Months10
Your Network
30 peopleSame Organization
@prairielearn.com3
Work History
February 2026
1 Commits
Feb 1, 2026February 2026 — PrairieLearn engineering delivered a data attribution fix to improve analytics fidelity. Key change: attribute course usage data for shared questions to the consuming course context by using v.course_id instead of q.course_id, ensuring analytics reflect where students actually engage with content. The fix, implemented in commit 02683ca8d9ee9092d730ccd9d183751412306aa1, closes data-quality gaps and aligns with related issues (#11849, #14099). Business impact includes more accurate course-level engagement reports, enabling instructors and admins to make better coaching and optimization decisions. This work demonstrates strong data-context handling in the analytics pipeline and effective cross-team collaboration on critical data quality improvements.
1 Commits
Feb 1, 2026February 2026 — PrairieLearn engineering delivered a data attribution fix to improve analytics fidelity. Key change: attribute course usage data for shared questions to the consuming course context by using v.course_id instead of q.course_id, ensuring analytics reflect where students actually engage with content. The fix, implemented in commit 02683ca8d9ee9092d730ccd9d183751412306aa1, closes data-quality gaps and aligns with related issues (#11849, #14099). Business impact includes more accurate course-level engagement reports, enabling instructors and admins to make better coaching and optimization decisions. This work demonstrates strong data-context handling in the analytics pipeline and effective cross-team collaboration on critical data quality improvements.
February 2026
January 2026
1 Commits
Jan 1, 2026January 2026 monthly summary for PrairieLearn/PrairieLearn: Stabilized real-time socket server by fixing a Redis socket adapter initialization race condition and ensuring readiness before subscriber mode. The change reduces startup race risks and increases reliability for real-time features.
January 2026
1 Commits
Jan 1, 2026January 2026 monthly summary for PrairieLearn/PrairieLearn: Stabilized real-time socket server by fixing a Redis socket adapter initialization race condition and ensuring readiness before subscriber mode. The change reduces startup race risks and increases reliability for real-time features.
October 2025
1 Commits
Oct 1, 2025In Oct 2025, PrairieLearn delivered a targeted UI stability improvement focused on question panel interactions. A bug fix ensures dropdown menus inside question panels are not clipped, can extend outside their parent containers when needed, and are repositioned correctly as the main container scrolls, with height constrained to the viewport to maintain visibility.
1 Commits
Oct 1, 2025In Oct 2025, PrairieLearn delivered a targeted UI stability improvement focused on question panel interactions. A bug fix ensures dropdown menus inside question panels are not clipped, can extend outside their parent containers when needed, and are repositioned correctly as the main container scrolls, with height constrained to the viewport to maintain visibility.
October 2025
September 2025
1 Commits
Sep 1, 2025September 2025: PrairieLearn/PrairieLearn – no new features released; focused on stabilizing the question finalization flow and fixing a critical Preview ID bug. The changes improve data integrity and user experience without introducing new features.
September 2025
1 Commits
Sep 1, 2025September 2025: PrairieLearn/PrairieLearn – no new features released; focused on stabilizing the question finalization flow and fixing a critical Preview ID bug. The changes improve data integrity and user experience without introducing new features.
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 — PrairieLearn/PrairieLearn: Targeted documentation enhancement for Assessment Configuration. Clarified naming conventions, types, question ordering, and group work functionalities to improve how users configure and manage assessments. The update is backed by commit c0ac13298db1e75a77c43ed367eb5e963ec28758, ensuring traceability and maintainability.
1 Commits • 1 Features
Jul 1, 2025July 2025 — PrairieLearn/PrairieLearn: Targeted documentation enhancement for Assessment Configuration. Clarified naming conventions, types, question ordering, and group work functionalities to improve how users configure and manage assessments. The update is backed by commit c0ac13298db1e75a77c43ed367eb5e963ec28758, ensuring traceability and maintainability.
July 2025
June 2025
1 Commits
Jun 1, 2025June 2025 PrairieLearn monthly summary focused on strengthening data integrity and reliability in grading workflows. Delivered a critical fix to AI grading data deletion logic, improving data accuracy and governance, and ensuring safe deletion of AI gradings.
June 2025
1 Commits
Jun 1, 2025June 2025 PrairieLearn monthly summary focused on strengthening data integrity and reliability in grading workflows. Delivered a critical fix to AI grading data deletion logic, improving data accuracy and governance, and ensuring safe deletion of AI gradings.
April 2025
2 Commits • 2 Features
Apr 1, 2025Concise monthly summary for PrairieLearn/PrairieLearn (April 2025). Key features delivered include In-Term Submission Ratio Analytics for Course Instances and Metrics Namespace Enforcement in node-metrics. No major bugs fixed this month. Overall impact: improved student engagement analysis and stronger observability; Technologies/skills demonstrated include SQL refactoring for term-based usage, Node.js metrics instrumentation, and semantic versioning/API design for observability tooling.
2 Commits • 2 Features
Apr 1, 2025Concise monthly summary for PrairieLearn/PrairieLearn (April 2025). Key features delivered include In-Term Submission Ratio Analytics for Course Instances and Metrics Namespace Enforcement in node-metrics. No major bugs fixed this month. Overall impact: improved student engagement analysis and stronger observability; Technologies/skills demonstrated include SQL refactoring for term-based usage, Node.js metrics instrumentation, and semantic versioning/API design for observability tooling.
April 2025
March 2025
3 Commits • 1 Features
Mar 1, 2025March 2025 performance summary for PrairieLearn: reliability, data accuracy, and secure access improvements in PrairieLearn/PrairieLearn. Core outcomes include corrected course instance usage joins, a backfill pipeline for course instance usage data, and improved access control for workspace logs.
March 2025
3 Commits • 1 Features
Mar 1, 2025March 2025 performance summary for PrairieLearn: reliability, data accuracy, and secure access improvements in PrairieLearn/PrairieLearn. Core outcomes include corrected course instance usage joins, a backfill pipeline for course instance usage data, and improved access control for workspace logs.
February 2025
10 Commits • 3 Features
Feb 1, 2025February 2025 (2025-02): Delivered analytics-enabled features and data integrity improvements in PrairieLearn/PrairieLearn with tangible business value and developer-friendly tooling. Core deliverables include analytics for course instance usages, stronger data integrity for variant ownership, reliable API metrics, centralized validation schemas, and enhanced documentation for Excel CSV downloads. Key deliverables: - Course Instance Usages analytics: introduced dedicated course_instance_usages table and batched migrations to populate usage data from submissions, workspaces, and external grading, enabling actionable insights into student engagement and staff workload. Commits demonstrate end-to-end data path from collection to analytics (multiple backfills and a non-null enforcement step). - Enforce Authenticated Variant Association: strengthened data integrity by enforcing NOT NULL on authn_user_id in variants and safely migrating existing NULLs to maintain integrity without table locking. - Accurate download size reporting: fixed api_download.py to report actual data size using response text length instead of content-length header. - Centralize Zod validation schemas: refactored and moved Zod helper types to a new package @prairielearn/zod, with a test harness and updated dependency management to standardize validation logic. - Excel CSV downloads: added documentation and FAQ addressing rendering issues, including a Python Pandas workaround to ensure clean exports. Overall, these changes improve data-driven insights for course usage, enhance data integrity and reliability, streamline validation logic, and improve developer and user documentation.
10 Commits • 3 Features
Feb 1, 2025February 2025 (2025-02): Delivered analytics-enabled features and data integrity improvements in PrairieLearn/PrairieLearn with tangible business value and developer-friendly tooling. Core deliverables include analytics for course instance usages, stronger data integrity for variant ownership, reliable API metrics, centralized validation schemas, and enhanced documentation for Excel CSV downloads. Key deliverables: - Course Instance Usages analytics: introduced dedicated course_instance_usages table and batched migrations to populate usage data from submissions, workspaces, and external grading, enabling actionable insights into student engagement and staff workload. Commits demonstrate end-to-end data path from collection to analytics (multiple backfills and a non-null enforcement step). - Enforce Authenticated Variant Association: strengthened data integrity by enforcing NOT NULL on authn_user_id in variants and safely migrating existing NULLs to maintain integrity without table locking. - Accurate download size reporting: fixed api_download.py to report actual data size using response text length instead of content-length header. - Centralize Zod validation schemas: refactored and moved Zod helper types to a new package @prairielearn/zod, with a test harness and updated dependency management to standardize validation logic. - Excel CSV downloads: added documentation and FAQ addressing rendering issues, including a Python Pandas workaround to ensure clean exports. Overall, these changes improve data-driven insights for course usage, enhance data integrity and reliability, streamline validation logic, and improve developer and user documentation.
February 2025
January 2025
3 Commits • 2 Features
Jan 1, 2025Month: 2025-01 | PrairieLearn/PrairieLearn Key features delivered: - Reporting Enhancements for Course Analytics: Refactored SQL to restrict enrollments to a time range and added computation of total compute time for workspaces and external grading jobs, delivering a more comprehensive view of student activity and resource utilization within course instances. - Documentation Improvements: LTI integration and environment/setup documentation updated to improve onboarding and setup experience (instructor permissions, Python virtual environments, dependency commands, and native installation prerequisites). Major bugs fixed: - No explicit major bugs fixed this month; focus was on feature delivery and documentation improvements to enhance reliability and onboarding. Overall impact and accomplishments: - Enhanced analytics accuracy and visibility into student activity and resource usage, enabling better course decisions for instructors and admins. - Smoother onboarding with clearer LTI setup guidance and environment prerequisites, reducing setup time for new users. Technologies/skills demonstrated: - SQL query refactoring and data reporting - Data analysis and metrics (active enrollments, compute time) - Documentation best practices and onboarding content - LTI integration concepts and Python environment setup
January 2025
3 Commits • 2 Features
Jan 1, 2025Month: 2025-01 | PrairieLearn/PrairieLearn Key features delivered: - Reporting Enhancements for Course Analytics: Refactored SQL to restrict enrollments to a time range and added computation of total compute time for workspaces and external grading jobs, delivering a more comprehensive view of student activity and resource utilization within course instances. - Documentation Improvements: LTI integration and environment/setup documentation updated to improve onboarding and setup experience (instructor permissions, Python virtual environments, dependency commands, and native installation prerequisites). Major bugs fixed: - No explicit major bugs fixed this month; focus was on feature delivery and documentation improvements to enhance reliability and onboarding. Overall impact and accomplishments: - Enhanced analytics accuracy and visibility into student activity and resource usage, enabling better course decisions for instructors and admins. - Smoother onboarding with clearer LTI setup guidance and environment prerequisites, reducing setup time for new users. Technologies/skills demonstrated: - SQL query refactoring and data reporting - Data analysis and metrics (active enrollments, compute time) - Documentation best practices and onboarding content - LTI integration concepts and Python environment setup
Activity
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Quality Metrics
Correctness93.0%
Maintainability89.6%
Architecture89.6%
Performance85.0%
AI Usage22.6%
Skills & Technologies
Programming Languages
JavaScriptMarkdownPLpgSQLPythonSQLTypeScript
Technical Skills
API IntegrationBackend DevelopmentCode OrganizationData ProcessingData TrackingDatabaseDatabase DesignDatabase ManagementDatabase MigrationDatabase MigrationsDebuggingDevOpsDocumentationExpress.jsFront End Development
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline