Prior Learning Formulae AI HL only

Prior learning refers to the knowledge, skills, and competencies that individuals have acquired through various experiences outside of formal classroom settings. This includes learning from work, volunteering, personal interests, independent study, community activities, or non-credit training programs. It's essentially the sum total of relevant learning accumulated throughout life.

Recognition of Prior Learning (RPL), also sometimes called Accreditation of Prior Learning (APL), is the process by which educational institutions or other credentialing bodies evaluate and acknowledge the learning that individuals have gained from their life and work experiences. The goal is to determine if this learning is equivalent to the learning gained through formal coursework or training and grant appropriate credit or standing.

Recognized prior learning is the outcome of this assessment process.

A Prior Learning Assessment (PLA) is the formal evaluation process used within RPL/APL to determine if an individual's prior learning meets the standards for academic credit or certification. PLA methods can include portfolio development, challenge exams, skill demonstrations, interviews, or evaluation of existing certifications and training records.

If the PLA is successful, the individual may receive "Credit for Prior Learning," which allows them to bypass certain courses or requirements in a formal program of study, saving time and money.

While specific processes vary by institution, the general steps for RPL/APL often include:

1. Identify the relevant learning: Think about your experiences and what you've learned that aligns with the program you're interested in.
2. Determine if the institution offers RPL/APL: Check their policies and contact their admissions or prior learning office.
3. Understand the requirements: Learn what documentation or assessment methods are needed (e.g., portfolio, exam).
4. Prepare your evidence: Gather work samples, certificates, training records, write reflective essays, etc.
5. Submit your application and evidence: Follow the institution's specific submission process.
6. Undergo assessment: Your evidence is reviewed, and you might need to participate in an interview or test.
7. Receive the outcome: You'll be notified of the decision regarding granted credit or recognition.

Starting by contacting the institution's admissions or RPL office is usually the best first step.

Prior knowledge is fundamentally important because learning is an active process of constructing new understanding based on what is already known. Existing knowledge helps learners:

• Make connections and integrate new information.
• Understand concepts more deeply.
• Filter and organize incoming information.
• Solve problems and transfer learning to new situations.

Activating relevant prior knowledge makes learning more efficient and meaningful. Conversely, inaccurate or insufficient prior knowledge can lead to misunderstandings and make learning new, related concepts more difficult.

In Machine Learning, particularly in Bayesian approaches, a "prior" or "prior probability distribution" represents the initial belief or information about a model parameter or hypothesis *before* any data is observed. It's a way to incorporate existing knowledge or assumptions into a statistical model. As data is analyzed, this prior is updated to a "posterior probability distribution," which reflects the belief *after* seeing the data.

This concept of a "prior" is distinct from the educational term "prior learning."