Welcome to pyAutoMark

pyAutoMark simplifies automating your marking and feedback

Here are pyAutoMark’s major features:

• Collection: Collection and collation of student work into folders for a cohort/assessment.

  This may be from a Blackboard dump of files and archives from grade centre or (preferred) the cloning and pulling of student work from Github Classroom repositories.

• Testing: It builds upon the pytest`_ testing framework to collect and run tests against each student submission.

• Feedback: The results of the tests are collected feedback reports per student which list the tests failed and guidance on what what was wrong. These can then be used to fill in marking spreadsheets.

• Flexibility: Tests can be anything that can be tested or measured using software. Some examples include

  • Software unitests in any language (not just Python, but C or even VHDL simulation and synthesis for hardware desing)

  • Measured data measured from instruments in a practical or generated from simulations.

  • Student provided answers in a spreadsheet.

  • A memory dump output from a debugger on an embedded system.

• Extendable: pytest fixtures are extensively used to enable customisation and extension. Provided fixtures which student files are tested and where the result should be reported. Additional sets provided for specific use cases such as using mock C testing for checking embedded systems desing (e.g. AVR), Python code and VHDL designs.

  While using python to write your tests provides the most flexibility, support is given to write your tests for C or VHDL directly without writing any python harness. See the C and VHDL examples.

• Marking Templates: A template marking spreadsheet can be automatically generated from the set of tests. This can then be customised and added to either with your own marking scheme or to including manual input for those aspects which cannot be automatically marked

• Multiple Assessments: pyAutoMark manages a directory heirachy to accomodate multiple assignments or cohorts of students.

• Student management: pyAutoMark uses a simple csv file of student details which can be provided e.g. from Blackboard. It can provide reports on who has submitted, and when, and if they have missed a deadline. When combined with Github Classroom this provides a very effective mechanism to monitor student engagement.

• Logging: Most activities on a cohort or assessment are logged to provide a historic record.

• Mark Collection: Marks from the marking spreadsheets can be collated into a csv file for adminsitrative purposes such as sending to the office.

Getting Started

The basics of installing and getting started using pyAutoMark including setting up your directory structure for submissions, reports and tests.

• Installing pyAutoMark
  • Overview
  • Installation
  • Installation from source
• Quick Start
  • The directory structure
  • Setting up a cohort
  • Setting up the tests
  • Retrieving the students work
  • Running the tests
  • Generating the mark spreadsheets
  • And finally - collating the marks

Subcommands

The pyam program subcommands

• cohort
• config
• extract
• find-duplicates
• generate-template
• Commands
• init
• mark
• retrieve
• run
• write-csv

Examples

• C
• Embedded C
• Python
• VHDL

Reference Guide

A complete collection of reference information.

• pyam python package
• Configuration
• Glossary

Indices and tables

• Index

• Module Index

• Search Page