......and Last minute checks/things to note
- Deadline for RPL applications is the 27th October 2022.
- Where your RPL case is based on prior formal learning, and you have not presented your original transcripts of results to the Admissions Office previously, then mention this at an MS Teams workshop, and we will arrange to briefly meet to validate your results. This will only take two minutes and we will meet in compliance with COVID 19 guidelines.
- Where your RPL case is based on experiential learning, or non formal learning, and it is comprised of a number of elements of evidence, make sure that everything is clearly labelled, and is included in the portfolio. The final submission must be one document, not a portfolio and many attachments. If necessary create a zipped folder to send the portfolio and evidence through the email.
Save your RPL case as a PDF and name it as follows: YOURNAME (STUDENT NUMBER) MODULE CODE ASSESSOR NAME, for example JohnSmith(R00031325)MATH6015TomDoherty
- Return the RPL case to email@example.com with the Assessor's name, who will check everything is in order and send your RPL case for assessment.
- The RPL case will be entered into the RPL database which will record the following details: the module code, date, Assessor name, basis of case, and the outcome (when it becomes available).
- Allow a week to ten days for the assessment to occur. You should hear informal word as to the outcome after this time. This informal outcome of assessment must then be ratified at the module exam boards at semester end.
- On assessment RPL cases are allowed if the learning outcomes are met. Cases based on prior formal learning and combination cases are recorded as X (exempt). Cases based on experiential learning are recorded as a % mark.
- The full module fee is payable upfront. However, for cases based on prior formal learning, a refund is allowed, less a €50 processing fee, once the result is ratified at the module exam board meeting. The full module fee applies for cases based on prior experiential learning, and cases based on a combination of different types of learning.