IT Pearson Lab provides a secure, cloud-based environment for running Pearson course experiments and data analysis. Designed for instructors and students, it streamlines lab workflows while maintaining academic integrity.
By integrating learning content with coding and data tools, the platform supports statistics, research methods, and data analysis courses. The following sections detail key capabilities, implementation steps, and support guidance.
| Feature | Description | Audience | Access Type |
|---|---|---|---|
| Lab Templates | Prebuilt experiment structures aligned with Pearson course objectives | Instructors | Instructor Portal |
| Jupyter Integration | Interactive notebooks for Python and R with auto-graded cells | Students & Instructors | Web Browser |
| Dataset Library | Curated datasets with documentation and citation metadata | Students | Data Catalog |
| Progress Analytics | lab completion, code quality, and time-on-task dashboards for course insightsInstructors | Analytics Console | |
| Compliance & Security | FERPA-aligned data handling, role-based permissions, and audit loggingInstitution IT | Admin Settings |
Setup and Configuration
In this section, instructors configure course parameters, sync rosters, and provision lab environments.
Roster Import
Use SIS integration or CSV upload to enroll students automatically and assign default lab templates.
Template Selection
Choose from Pearson-aligned templates that map to learning outcomes, data sets, and grading rubrics.
Data Workflows
Students complete experiments using hosted Jupyter notebooks, with versioned datasets and reproducible pipelines.
Notebook Execution
Run cells in Python or R, visualize outputs inline, and submit artifacts directly to the course gradebook.
Version Control
Git-backed snapshots track changes, enabling rollback and facilitating peer review within the lab environment.
Grading and Feedback
Automated checks assess code correctness, while instructors add qualitative comments and adjust scores.
Auto-Grader Hooks
Unit tests and linting rules provide immediate feedback, reducing manual grading overhead.
Rubric Application
Weighted criteria such as methodology, documentation, and results interpretation align with course goals.
Best Practices and Recommendations
- Align lab templates with measurable learning outcomes for each module.
- Use auto-grader hooks to catch common errors before manual review.
- Schedule weekly data audits to verify dataset integrity and access logs.
- Communicate submission deadlines and versioning expectations clearly to students.
- Leverage analytics dashboards to identify students who need targeted intervention.
FAQ
Reader questions
How do I link my Pearson course to IT Pearson Lab?
In the Instructor Portal, select Connect Course, authenticate with your Pearson credentials, and map sections to lab sections.
Can students run labs offline?
All notebooks execute in the cloud; offline work is not supported, but projects can be downloaded for local reference.
What data does the platform collect for analytics?
Interaction events, code execution logs, and timestamps are stored to power instructor dashboards and improve lab design.
Is it possible to add custom datasets to IT Pearson Lab?
Instructors may upload CSV or Parquet files after review, ensuring compliance with privacy and institutional policies.