PATCH - Update API

This is a generic API that allows you to update various components of an evaluation platform:

• You can update the question text to reflect any changes or corrections.
• The marking scheme can be modified to adjust the scoring criteria.
• Model answer step marking can be updated to ensure accurate evaluation.
• Additionally, you can update the student evaluated answer marking for consistency

Scenario: Updating Question Text for Clarity or Correction:

1. Clarifying or Correcting Question Text:
   • During the evaluation process, an evaluator identifies an ambiguous or incorrect question. The Generic Update API allows updating the question text to make it clearer or correct any inaccuracies.
   • Outcome:
     • The question text is updated, ensuring consistency across the platform and more accurate evaluation for students.

Scenario: Adjusting the Marking Scheme for a Specific Question:

1. Modifying the Marking Scheme:
   • An evaluator reviews a question and decides to adjust the marking scheme, either by changing the total marks, modifying the breakdown, or adding new criteria.
   • Outcome:
     • The marking scheme is updated, ensuring fairness and alignment with evaluation criteria.

Scenario: Updating Model Answer’s Step Marking:

1. Modifying Step-Wise Marking:
   • After reviewing the model answer, the evaluator notices the step marking needs to be adjusted for clarity or accuracy. The Generic Update API is used to modify how marks are distributed across different steps.
   • Outcome:
     • The step marking is updated, impacting how student answers are evaluated and ensuring accurate scoring.

Scenario: Correcting Student Answer Evaluation Marking:

1. Correcting Mistakes in Evaluation:
   • After reviewing a student’s answer, an error is discovered in the evaluation, such as incorrect marking or wrong application of the model answer’s step-wise evaluation.
   • Outcome:
     • The student’s evaluated answer is corrected, ensuring that the final score accurately reflects the correct evaluation based on the model answer.