Grading Framework

Our grading framework defines three distinct approaches to assessment that represent different levels of automation and human involvement in the grading process. These approaches — Assisted Grading, Semiautomated Grading, and Automated Grading — form a spectrum from minimal to maximal automation while considering the critical balance between efficiency and control.

The grading framework developed at the beginning of our project

At the foundation of our framework, Assisted Grading focuses on improving efficiency and consistency of the grading process without directly influencing grade decisions. This approach provides tools and features that streamline the grader's workflow while maintaining full human control over point allocation. The grader re-mains the primary decision-maker, with technology serving purely as a facilitation tool.

In the middle of the spectrum, Semiautomated Grading represents a hybrid approach that incorporates automation while maintaining significant human oversight. This method integrates software suggestions into the grading workflow, allowing for automated recommendations while ensuring that final decisions remain under human control. The system may suggest point allocations or identify patterns, but graders retain the ability to verify and modify these suggestions.

At the highest level of automation, Automated Grading employs software to conduct the entire grading process without requiring per-item human verification. While this approach includes human oversight through sample verification and grading curve calibration, the actual assessment is fully automated by the system. This method is particularly suitable for scenarios where consistent, objective criteria can be clearly defined and verified.