Prior Learning Formulae AI SL & AI HL

Prior learning refers to the knowledge, skills, and competencies that individuals have gained through life and work experiences, training, volunteering, or other non-formal education, outside of traditional academic coursework. It is the sum total of what someone has learned throughout their life up to a certain point.

This learning is often valuable and can be equivalent to learning gained in a classroom setting.

Recognition of Prior Learning (RPL) and Accreditation of Prior Learning (APL) are terms used to describe the process of identifying, assessing, and certifying an individual's learning against a given set of learning outcomes or standards. Essentially, it's a way for educational institutions or employers to formally acknowledge the skills and knowledge someone has, regardless of how or where they acquired it.

This process can potentially lead to academic credit, advanced standing in a course, or certification towards a qualification.

A Prior Learning Assessment (PLA) is the formal evaluation process used to determine if and how a student's prior learning is equivalent to college-level learning. This assessment can take many forms, such as portfolios, essays, interviews, demonstrations, or challenge exams.

If the assessment determines that the prior learning meets the required academic standards, the student may receive "Credit for Prior Learning," which means they are granted academic credit for that learning, potentially shortening the time and cost required to complete a degree or program.

The process varies significantly depending on the institution or awarding body, but typically involves:

• Identifying the learning: Reflecting on your experiences (work, volunteering, training, etc.) and identifying the specific knowledge and skills gained.
• Mapping to requirements: Comparing your learned skills/knowledge to the learning outcomes or requirements of the specific course or qualification you are seeking credit for.
• Gathering evidence: Collecting documentation to support your claims (e.g., work samples, training certificates, performance reviews, job descriptions, testimonials).
• Submitting a portfolio: Often, you compile your reflection and evidence into a structured portfolio or essay.
• Assessment: The institution evaluates your evidence, sometimes through interviews, tests, or demonstrations.

Contact the specific educational institution or body you are interested in for their detailed RPL/APL process and application requirements.

Prior knowledge plays a critical role in how new information is learned and processed. It acts as a foundation or framework upon which new concepts are built. Specifically:

• Facilitation: Existing knowledge helps learners connect with and make sense of new information, integrating it into their existing understanding.
• Interpretation: Learners interpret new information through the lens of what they already know.
• Engagement: Activating relevant prior knowledge can increase interest and engagement in new topics.
• Obstruction: Incorrect or incomplete prior knowledge can hinder learning by leading to misconceptions or difficulty integrating accurate new information.

Educators often assess and activate students' prior knowledge before introducing new topics to leverage its benefits and address potential gaps or errors.

Yes, "the total accumulation of prior learning experiences" is essentially what is meant by the term "Prior Learning" itself. It refers to the sum total of all knowledge, skills, and competencies gained through a person's experiences before a specific point in time, typically before starting a formal educational program or seeking recognition for that learning.

In Machine Learning, particularly in Bayesian statistics and Bayesian machine learning models, a "prior" or "prior probability" refers to the probability distribution representing your beliefs about a parameter or hypothesis *before* observing any data. It's your initial assumption or knowledge. As new data is observed, this prior belief is updated using Bayes' theorem to form a "posterior probability," which represents your updated belief after considering the evidence from the data.

This is a specific statistical/ML term unrelated to the general educational concept of recognizing prior learning experiences.