1. Fork the Portfolios Repository and Clone your Fork

Fork Turing’s Portfolios Repository and clone it to your machine.

2. Create an M1 Portfolio

Use the template in the templates directory to create a new template for yourself.

The file should be saved in the format "date-firstname-lastname-module.markdown" where:

  • date is the last day of the module in compressed year/month/day, like 20150831
  • name is your first and last name like jeff-casimir
  • completing is the module that you’re completing, like B1

So a complete filename might look like 20150831-jeff-casimir-B1.markdown. This file needs to be stored in a directory portfolios/students/your-cohort/your-name where your-name is like jeff-casimir and your-cohort is your cohort number.

If you were completing Module 4 in Cohort 1410 on August 31, 2015 and your name was Jeff Casimir, your portfolio submission would reside at the following location: portfolios/students/1410/jeff-casimir/20150831-jeff-casimir-m4.markdown.

Note: Do not move the file from the templates directory. If you do that and then commit, Git will register it as you deleting the template.

3. Update Your Portfolio

Answer the questions in each section of the portfolio. Enter self-assessment scores in the final section.

4. Make a PR to turingschool/portfolios by 4:30pm the Wednesday prior to your portfolio review



  • Your portfolio will be evaluated in the last week of the module.
  • A pull request with your submission must be made by 2:30pm the day before your portfolio review.
  • Non-conforming formats, filenames, or directories will be rejected and your review rescheduled.
  • Instructors will review all portfolio submissions before your scheduled portfolio review and merge pull requests.
  • During your portfolio review, you will discuss:
    • Areas of strength
    • Areas in which continued attention is warranted.
    • Discrepancies (if any) between self-assessment scores and instructors’ estimation of performance.
    • Provisional work, if applicable, to close out expectations for the module.
  • Instructors will render a promotion/retained/excused/provisional outcome.


Portfolio evaluations have four possible outcomes.

  • PROMOTED - your portfolio demonstrates a successful fulfillment of expectations and you may move on to the next module or graduate
  • PROVISIONAL - your portfolio demonstrates a high likelihood that you will be successful in Module 2 with additional preparation. Failure to complete additional work assigned will result in being either be RETAINED or EXCUSED
  • RETAINED - your portfolio is lacking in one or more areas and you may either repeat the current module or leave the program
  • EXCUSED - your portfolio is lacking in one or more areas and either (a) you’ve now failed to pass the module in two attempts or (b) the reviewers have determined that you will not be successful at Turing


  • To be Promoted
    • Performance across all projects and assessments that demonstrates a high likelihood of success in Module 2.
    • This may be demonstrated by high performance throughout the module, or a strong upward sloping trend in performance.