recognition of prior learning rpl learning rpl background
play

Recognition of Prior Learning (RPL) Learning (RPL) Background The - PowerPoint PPT Presentation

Dec 24, 2022 •975 likes •1.17k views

Recognition of Prior Learning (RPL) Learning (RPL) Background The Ministry of Skill Development and Entrepreneurship (MSDE) had launched Entrepreneurship (MSDE) had launched Pradhan Mantri Kaushal Vikas Yojana (PMKVY) in the year 2015. What

  1. Recognition of Prior Learning (RPL) Learning (RPL)

  2. Background The Ministry of Skill Development and Entrepreneurship (MSDE) had launched Entrepreneurship (MSDE) had launched Pradhan Mantri Kaushal Vikas Yojana (PMKVY) in the year 2015.

  3. What is RPL? Recognition of Prior Learning (RPL) is a platform to provide recognition to the informal learning or learning through work to get equal acceptance as the formal levels of education. It aims to appreciate prior learning irrespective of the medium of achieving it. of the medium of achieving it. In short, RPL is a process of assessment of an individual’s prior learning to give due importance to learning as an outcome rather than learning as process.

  4. Benefits for the Workforce Through RPL assessment and certification, the candidates will be able to align existing competencies with the National Skills Qualification Framework (NSQF) for better opportunities in higher education. Besides creating an equitable system, RPL also helps Besides creating an equitable system, RPL also helps them explore different options in skilling and upskilling to bridge the knowledge and skill gaps.

  5. Benefits for the Industry By getting the workforce certified under RPL, the industry gets the dual advantage: (1) formally recognized skill levels mapped with QPs (1) formally recognized skill levels mapped with QPs and job roles and (2) open up different paths for workers to upskill and grow through short term training and bridge courses.

  6. IASC-SSC: Norms for Recognition of Prior Learning (RPL) 1. Eligibility : Person who has previous working experience on our job roles. 2. Procedure for Assessment : a. Briefing of Assessors about the assessment process on QPs/ NOSs(Job roles). roles). b. Assessment will be carried out for particular NOSs of QP's. c. The pass marks will be based on the existing NSQF levels.

  7. IASC-SSC: Norms for Recognition of Prior Learning (RPL) A candidate who achieves greater than or equal to the pass percentage mentioned in Table: NSQF Level Percentage required for Passing a QP a QP 1,2,3 50% 4 & above 70%

  8. Certification Certification will be done: 15 days after Assessment. Note: Assessment Criteria: Core NOSs of a QP: 70% Non - Core NOSs of a QP: 30%

  10. QP Name / QP code Level QP Numbers Core NOS Non-Core Title NOS Industrial IAS/Q8005 5 1. IAS/N2000 Design IAS/N2000, IAS/N2003 Automation and Assemble IAS/N2001 Specialist Automation System & 2. IAS/N2001 Technical IAS/N2002. Support for Installation and Commissioning of Control Panel 3. IAS/N2002 3. IAS/N2002 Coordination With Different Stakeholders 4. IAS/N2003 Health and Safety in Workplace

  11. QP Name QP code Leve QP Numbers Core NOS Non-Core / Title l NOS 1. IAS/N2100 Design, Install and Building IAS/Q3006 5 IAS/N2100, IAS/N210 Provide Technical Support for Automatio IAS/N2101, 5 & HVAC System n Specialist IAS/N2102, IAS/N200 2. IAS/N2101 Design, Install and IAS/N2103 3 Provide Technical Support for & Fire Alarm Systems 3. IAS/N2102 Install and Provide IAS/N2104. Technical Support for Access Controls Systems 4. 4. IAS/N2103 Install and Provide IAS/N2103 Install and Provide Technical Support for CCTV Surveillance Systems 5. IAS/N2104 Integrating and Controlling Building automation Systems 6. IAS/N2105 Work Effectively With Teams 7. IAS/N2003 Health and Safety in Workplace

  12. QP Name / QP code Level QP Numbers Core NOS Non-Core Title NOS Callibration IAS/Q5001 4 1. IAS/N0200 Work Place IAS/N0200 IAS/N2105 Technician Readiness - Calibration IAS/N0201, (Thermal) 2. IAS/N0201 Calibration of IAS/N0202, Thermocouple IAS/N0203 3. IAS/N0202 Calibration of & RTD IAS/N0205 4. IAS/N0203 Calibration of Infrared / Non contact Temperature Detector 5. IAS/N0204 Task 5. IAS/N0204 Task Reporting - Calibration 6. IAS/N0205 Preventive Maintenance – Thermal Calibration 7. IAS/N2105 Work Effectively With Teams

  13. QP Name QP code Level QP Numbers Core NOS Non-Core / Title NOS Junior IAS/Q3003 3 1. IAS/N0300 Site IAS/N0300, IAS/N0105 Instrument Readiness and IAS/N0301, , ation Instrument Usability – IAS/N0103. IAS/N2105 Technician Process Control (Process 2. IAS/N0301 Preventive control) Maintenance – Process Control 3. IAS/N0103 Task reporting – Process reporting – Process Control 4. IAS/N0105 Safety, Health and Environment –Process Control 5. IAS/N2105 Work Effectively With Teams

  14. QP Name / QP code Level QP Numbers Core NOS Non-Core Title NOS Instrumenta IAS/Q3001 4 1. IAS/N0100 Maintain IAS/N0100, IAS/N0105 tion Control Valve Health IAS/N0101, IAS/N2105 Technician 2. IAS/N0101 Preventive & IAS/N0102, (Control Predictive Maintenance- IAS/N0103 Valves) 3. Control Valve & 4. IAS/N0102 Site IAS/N0104 Management – Process Control 5. IAS/N0103 Task Reporting - Process Reporting - Process Control 6. IAS/N0104 Corrective Maintenance – Control Valve 7. IAS/N0105 Safety, Health and Environment – Process Control 8. IAS/N2105 Work Effectively With Teams

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

PROFESSIONAL DESIGNATIONS: RECOGNITION OF PRIOR LEARNING MASA IN CONTEXT A SHORT

PROFESSIONAL DESIGNATIONS: RECOGNITION OF PRIOR LEARNING MASA IN CONTEXT A SHORT

PROFESSIONAL DESIGNATIONS: RECOGNITION OF PRIOR LEARNING MASA IN CONTEXT A SHORT BACKGROUND CREDIBILITY IN CONTEXT THE COMPETENCY FRAMEWORK THE RPL PROCESS CHALLENGES & THE WAY FORWARD MASA IN CONTEXT A short

178 views • 15 slides
Prior Learning Assessment Content of Presentation Introduction to Prior Learning Assessment

Prior Learning Assessment Content of Presentation Introduction to Prior Learning Assessment

Prior Learning Assessment Content of Presentation Introduction to Prior Learning Assessment Developing a Portfolio Tips & Hints Submission Evaluation Process Awards & Applying Credits What is Prior Learning

406 views • 37 slides
I Already Know That! How can you help your client achieve formal recognition of their prior

I Already Know That! How can you help your client achieve formal recognition of their prior

I Already Know That! How can you help your client achieve formal recognition of their prior learning ASPECT Conference Thursday, November 2, 2017 3:30-5:00 BCIT-Justine Arsenault DC- Lori Woods & Jules Andre-Brown VCC-Tanis Sawkins

379 views • 25 slides
Project CoachLearn Enhancing Coaches Learning, Mobility & Employment within the context of

Project CoachLearn Enhancing Coaches Learning, Mobility & Employment within the context of

Project CoachLearn Enhancing Coaches Learning, Mobility & Employment within the context of a European Sport Coaching Framework Chapter 6 Coach Certification & Recognition Step by step guide to recognition of prior learning/work

663 views • 17 slides
Learning for Action Recognition Yemin Shi shiyemin@pku.edu.cn 2018-03 1 Background Action

Learning for Action Recognition Yemin Shi shiyemin@pku.edu.cn 2018-03 1 Background Action

GPU Accelerated Sequence Learning for Action Recognition Yemin Shi shiyemin@pku.edu.cn 2018-03 1 Background Action Recognition Object Recognition (Video Classification) (Image Classification) Action Recognition VS Object Recognition

508 views • 49 slides
NARIC Study on Formal Recognition of Non-formal and Informal Learning RPL Bologna Seminar

NARIC Study on Formal Recognition of Non-formal and Informal Learning RPL Bologna Seminar

NARIC Study on Formal Recognition of Non-formal and Informal Learning RPL Bologna Seminar Jenneke Lokhoff Policy Officer 12 December 2008 Background (1) National and international Lifelong Learning strategies Increasing number of

348 views • 18 slides
Complex learning example: curve fitting t = sin(2 x ) + noise t n t 1 y ( x n , w ) 0 1

Complex learning example: curve fitting t = sin(2 x ) + noise t n t 1 y ( x n , w ) 0 1

Artificial Intelligence: Representation and Problem Solving 15-381 April 10, 2007 Introduction to Learning & Decision Trees Learning and Decision Trees to learning aka: regression What is learning? pattern recognition

462 views • 17 slides
PLAR Prior Learning Assessment and Recognition Who is Eligible? Mature Students Mature

PLAR Prior Learning Assessment and Recognition Who is Eligible? Mature Students Mature

Adult High School PLAR Prior Learning Assessment and Recognition Who is Eligible? Mature Students Mature students are those that are over 18 and have been out of school for at least one year. Enrolled in Adult High School with the

391 views • 12 slides
Background to and Rationale for the Research RPL was first introduced to the South African

Background to and Rationale for the Research RPL was first introduced to the South African

Panel title: The South African National Qualifications Framework, and mobility between education, training, development and work: four aspects Presentation sub- title: Exploring the recognition of prior learning: assessment device and

250 views • 8 slides
Meta Learning Shengchao Liu Background Meta Learning (AKA Learning to Learn) A

Meta Learning Shengchao Liu Background Meta Learning (AKA Learning to Learn) A

Meta Learning Shengchao Liu Background Meta Learning (AKA Learning to Learn) A fast-learning algorithm: quickly adapted from the source tasks to the target tasks Key terminologies Support Set & Query Set C-Way K-Shot

2k views • 41 slides
Bayesian Learning l A powerful approach in machine learning l Combine data seen so far with prior

Bayesian Learning l A powerful approach in machine learning l Combine data seen so far with prior

Bayesian Learning l A powerful approach in machine learning l Combine data seen so far with prior beliefs This is what has allowed us to do machine learning, have good inductive biases, overcome "No free lunch", and obtain good

1.42k views • 29 slides
Background Subtraction in Video using Bayesian Learning with Motion Information Suman K. Mitra

Background Subtraction in Video using Bayesian Learning with Motion Information Suman K. Mitra

Background Subtraction in Video using Bayesian Learning with Motion Information Suman K. Mitra DA-IICT, Gandhinagar suman_mitra@daiict.ac.in 1 Bayesian Learning Given a model and some observations, the prior distribution of the parameters

414 views • 27 slides
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning Kelvin Xu, Ellis Ratner, Anca

Learning a Prior over Intent via Meta-Inverse Reinforcement Learning Kelvin Xu, Ellis Ratner, Anca

Learning a Prior over Intent via Meta-Inverse Reinforcement Learning Kelvin Xu, Ellis Ratner, Anca Dragan, Sergey Levine, Chelsea Finn University of California, Berkeley Motivation : a well specified reward function remains an important

924 views • 42 slides
Classify as a Whole? MULTIPLE INSTANCE LEARNING Set Learning? Multi-Set Learning? Marco Loog

Classify as a Whole? MULTIPLE INSTANCE LEARNING Set Learning? Multi-Set Learning? Marco Loog

TUDelft PRLab NL Classify as a Whole? MULTIPLE INSTANCE LEARNING Set Learning? Multi-Set Learning? Marco Loog Pattern Recognition Laboratory Delft University of Technology heavily inspired by slides from Veronika Cheplygina and David MJ

486 views • 32 slides
Softmax Classifier + SGD Todays Class Intro to Machine Learning What is Machine Learning?

Softmax Classifier + SGD Todays Class Intro to Machine Learning What is Machine Learning?

CS6501: Deep Learning for Visual Recognition Softmax Classifier + SGD Todays Class Intro to Machine Learning What is Machine Learning? Supervised Learning: Classification with K-nearest neighbors Unsupervised Learning: Clustering with

1.36k views • 91 slides
Learning distance functions Xin Sui CS395T Visual Recognition and Search The University of Texas

Learning distance functions Xin Sui CS395T Visual Recognition and Search The University of Texas

Learning distance functions Xin Sui CS395T Visual Recognition and Search The University of Texas at Austin Outline Introduction Learning one Mahalanobis distance metric Learning multiple distance functions Learning one classifier

1.11k views • 77 slides
Challenges of Humanitarian Assistance Coordination Zahidul Huque UNFPA Representative for Turkey

Challenges of Humanitarian Assistance Coordination Zahidul Huque UNFPA Representative for Turkey

Challenges of Humanitarian Assistance Coordination Zahidul Huque UNFPA Representative for Turkey Country Director for Armenia, Azerbaijan and Georgia What is Global Humanitarian Assistance? A generic term used to describe the aid and action

543 views • 28 slides
The challenges of Arctic research Looking Beyond the International Polar Year David Hik

The challenges of Arctic research Looking Beyond the International Polar Year David Hik

The challenges of Arctic research Looking Beyond the International Polar Year David Hik President, International Arctic Science Committee University of Alberta, Edmonton, Alberta, Canada 10 th Conference of Parliamentarians of the Arctic Region

908 views • 37 slides
Population Movements in a Crisis Context within the Rabat Process Anja Klug Senior Policy

Population Movements in a Crisis Context within the Rabat Process Anja Klug Senior Policy

Population Movements in a Crisis Context within the Rabat Process Anja Klug Senior Policy Officer Bureau for Europe Bureau for Europe April 2014 Population Movements in a Crisis Context within the Rabat Process What is a crisis? - UNHCRs

598 views • 23 slides
Workshop on Humanitarian Assistance, Structures and Coordination in the Western region

Workshop on Humanitarian Assistance, Structures and Coordination in the Western region

ACBAR Workshop on Humanitarian Assistance, Structures and Coordination in the Western region OCHA ACBAR NHRP

1.19k views • 25 slides
LCCMR ID: 206-G Project Title: Northern Minnesota Site-Based Watershed Education Category: G.

LCCMR ID: 206-G Project Title: Northern Minnesota Site-Based Watershed Education Category: G.

Environment and Natural Resources Trust Fund 2011-2012 Request for Proposals (RFP) LCCMR ID: 206-G Project Title: Northern Minnesota Site-Based Watershed Education Category: G. Environmental Education Total Project Budget: $ $540,698

208 views • 6 slides
Strategic Aim 2 and 3 Global Shelter Cluster Meeting Geneva, 9 October 2014 Global Shelter

Strategic Aim 2 and 3 Global Shelter Cluster Meeting Geneva, 9 October 2014 Global Shelter

Strategic Aim 2 and 3 Global Shelter Cluster Meeting Geneva, 9 October 2014 Global Shelter Cluster ShelterCluster.org Coordinating Humanitarian Shelter Strategic Aim 2 An effective and well-functioning Global Shelter Cluster. Global Shelter

376 views • 6 slides
Leaving no one behind The role of evidence-building and profiling to include displacement in

Leaving no one behind The role of evidence-building and profiling to include displacement in

Leaving no one behind The role of evidence-building and profiling to include displacement in recovering and development processes Natalia Krynsky Baal , Coordinator, Joint IDP Profiling Service WHAT IS PROFILING? Defining profiling A

439 views • 12 slides
Learning from disaster Andy Featherstone 27 March, IFRC, Geneva Purpose of the study To explore

Learning from disaster Andy Featherstone 27 March, IFRC, Geneva Purpose of the study To explore

Learning from disaster Andy Featherstone 27 March, IFRC, Geneva Purpose of the study To explore the ways NDMAs and other state actors learn about and improve their humanitarian response activities with a view to identifying: Current

368 views • 13 slides

More recommend