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## Outcomes
The currently released project outcomes of the project include:
-1. Release of the first version of the `parsons` question block in STACK v4.5.0 ([Documentation](https://docs.stack-assessment.org/en/Topics/Parsons/)). This question block allows authors to write drag-and-drop proof assessment questions, where students must re-arrange pre-written steps into a correct order. General tools for generating specific line-by-line feedback and assessing non-unique answers are included. An example of the user interface and automated line-by-line feedback are displayed below.
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-2. Release of the second version of the `parsons` question block in STACK v4.6.0 ([Documentation](https://docs.stack-assessment.org/en/Topics/Matching/)). This block now supports more general layouts involving multiple columns and grids, which allow the student to drag items to specific groups or specific points on a grid. An example of a question in the grid layout is given below.
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+1. Release of the first version of the `parsons` question block in STACK v4.5.0 ([Documentation](https://docs.stack-assessment.org/en/Topics/Parsons/)). This question block allows authors to write drag-and-drop proof assessment questions, where students must re-arrange pre-written steps into a correct order. General tools for generating specific line-by-line feedback and assessing non-unique answers are included. An example of the user interface and automated line-by-line feedback are displayed below.
+2. Release functionality for matching problems as a the second version of the `parsons` question block in STACK v4.6.0 ([Documentation](https://docs.stack-assessment.org/en/Topics/Matching/)). This block now supports more general layouts involving multiple columns and grids, which allow the student to drag items to specific groups or specific points on a grid. An example of a question in the grid layout is given below.
3. Optimisations of the STACK code were released in v4.6.0, details [here](https://docs.stack-assessment.org/en/Developer/Optimising_STACK_for_large_Maxima_variables/), which saw processing speeds of up to 274 times faster when generating dummy datasets in a question.
## People