assessment-builder

To generate Python assessments with varied item types and rubrics, you can design a structured assessment pipeline that outputs MCQs, code-completion, and debugging tasks mapped to learning objectives, complete with an answer key and analytic rubrics.

Bloom's taxonomy alignment for programming assessments is a validation process ensuring cognitive distribution measures understanding and ability. It validates that 60%+ of questions are non-recall, mapping items to specific learning objectives.

To design MCQ distractors based on common Python misconceptions, the assessment pipeline applies structured distractor design. This generates plausible incorrect options targeting specific student misunderstandings alongside the correct answer key.

Yes, you can use Python to validate cognitive distribution and instructional alignment for exams. The pipeline automatically checks that 60%+ of questions are non-recall and verifies that analytic rubrics are present for open-ended items.

The best way to build a complete assessment package with objective-to-question mapping is to use an automated validation pipeline. It outputs the question set, answer key with explanations, and the objective mapping in one structured package.

Yes, you need PyYAML installed to create Bloom-aligned Python assessments. It serves as the required dependency for the scripts that design the assessment pipeline, validate instructional alignment, and output the assessment package.