Apprenticeship Model of learning

Duration: 6 min

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Inside: a video lesson and guided study material.

Module outline

  1. Course Overview: About the Course
  2. Paper - 1 | Unit - 1 | Teaching Aptitude: Nature, Objectives & Characteristics of Teaching, Learners & Learning Process, Factors Affecting Teaching, Methods of Teaching, Teaching-Learning Aids & ICT Integration, Evaluation, Assessment & Measurement
  3. Paper - 1 | Unit - 2 | Research Aptitude: Introduction to Research, Validity & Reliability, Research Paradigms & Types of Research, Research Process Steps, Research Ethics, Writing & Publication
  4. Paper - 1 | Unit - 3 | Comprehension: Comprehension / Reading Comprehension / Unseen Passages (Critical Reasoning) (Paragraph Questions)
  5. Paper - 1 | Unit - 4 | Communication: Communication Basics, Language and Semiotics, Types of Communication, Communication Models, Mass Communication, Mass Media, Journalism, General Knowledge and General Studies related to Communication
  6. Paper - 1 | Unit - 5 | Mathematical Reasoning and Aptitude: Series (Number and Letter Series) (Numerical Relations and Reasoning), Coding Decoding, Number System, Percentage, Ratio and Proportion (Ratios), Simple Interest and Compound Interest, Speed Time and Distance, Powers and Exponents (Surds and Indices), Profit and Loss, Average, Blood Relations, Directions (Direction Test), Analytical Reasoning (Counting Figures Reasoning), Verbal Analogy (Word Based Analogy), Divisibility Rules, Calendar, Miscellaneous, Time and Work, Algebra
  7. Paper - 1 | Unit - 6 | Logical Reasoning: Syllogisms, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Venn Diagram, School Of Thoughts, Fallacy, Western Logic
  8. Paper - 1 | Unit - 7 | Data Interpretation: Data Interpretation
  9. Paper - 1 | Unit - 8 | Information and Communication Technology: MS Office Applications, Cyber Threats and Malware Attacks, Role of Internet and Web Services, Electronic Data Interchange and E-Commerce, Internet Fundamentals, Web Design & Development, Web Publishing & Hosting, Emerging Technologies, Terms & Abbreviations, Artificial Intelligence, Society, Law & Ethics, Keyboard Shortcuts, Website, Browser & Services
  10. Paper - 1 | Unit - 9 | People Development and Environment: Ecosystem, Biomes & Environmental Issues, MDG & SDG, Air Pollution, Water Pollution, Soil, Noise Pollution and Waste Management, Natural Resources, Energy & Disaster Management, Global Environmental Conventions – COP, Protocols & ISA
  11. Paper - 1 | Unit - 10 | Higher Education System: Vedic Education, Jainism & Buddhism, Ancient Universities, Pre-Independence Commissions, Post-Independence Policy, Higher Education Structure & Accreditation, Universities & Learning Programmes, Types of Education & NEP 2020
  12. Paper 2 | Unit 1 | Discrete Structures and Optimization: Propositional and Predicate Logic, Set Theory, Relations, Functions, Permutation and Combination, Probability, Graph Theory, Group Theory, Digital Systems & Boolean Basics, Boolean Expression, Boolean Minimization, Optimization
  13. Paper 2 | Unit 2 | Computer System Architecture: Logic Gates & Hardware, Combinational Circuit, Sequential Circuits, Number System, Number Representation, Floating Point Rep, Basics of COA, Register Transfer and Microoperations, Programming the Basic Computer, Instr Formats & Modes, Control Unit Design, Pipelining, Input Output Organisation, Cache Memory Organization, Multiprocessors
  14. Paper 2 | Unit 3 | Programming Languages and Computer Graphics: Language Design, C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling, HTML Basics, XML, JavaScript-Basics, Java, Basics of Computer Graphics, 2-D Geometrical Transforms and Viewing, 3-D Object Representation, Geometric Transformations and Viewing, OOPS with C++, Java Fundamentals
  15. Paper 2 | Unit 4 | Database Management Systems: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, SQL, Relational Calculus, Transaction Management, Concurrency Control, Database Recovery, ORDBMS, Database Security & Authorization, Query Processing & Optimization, Enhanced Data Models, Data Warehousing & Mining, Big Data Systems, NoSQL
  16. Paper 2 | Unit 5 | System Software and Operating System: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, Disc Scheduling, File Management, Windows OS, Linux OS, Security, Distributed Systems, Virtual Machines
  17. Paper 2 | Unit 6 | Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management
  18. Paper 2 | Unit 7 | Data Structures and Algorithms: Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing, Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms
  19. Paper 2 | Unit 8 | Theory of Computation and Compilers: Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Grammar, Regular Language Properties, Moore & Mealy Machines, Pushdown Automata & CFG, Turing Machines, Complexity Theory, Intro to Compilers, Lexical Analysis, Grammar & CFG, Syntax Analysis: Top-Down, Syntax Analysis: Bottom-Up, Semantic Analysis & SDT, Intermediate Code Gen, Code Optimization, Run Time Environment
  20. Paper 2 | Unit 9 | Data Communication and Computer Networks: Introduction to CN, Data Communication, DLL: Access Control, DLL: Flow Control, DLL: Error Control, DLL: Framing, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Net Layer:Routing Protocol, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware basics, Network Security, Mobile Technology, Cloud Computing and IoT, Cloud Computing
  21. Paper 2 | Unit 10 | Artificial Intelligence: Approaches to AI, Search Algorithms, Game Playing, Knowledge Representation, Planning, Multi Agent Systems, Fuzzy Sets, Natural Language Processing, Artificial Neural Networks, Genetic Algorithms
  22. Live Classes: NTA UGC NET 2025 Live Class
  23. Paper 1 | Full Mock Tests:
  24. Paper 2 | Full Mock Tests:
  25. Paper 1 | Previous Year Papers:
  26. Paper 2 | Previous Year Papers:
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video lecture introduces the Cognitive Apprenticeship Model of Learning, originally proposed by Collins, Brown, and Newman in 1989. The instructor explains that this model extends the traditional concept of apprenticeship, where learning happens by working with an expert, into the cognitive domain. The core idea is that learners acquire thinking processes, problem-solving skills, and metacognitive skills through guidance. The lecture details six major teaching methods that structure this learning process: Modeling, Coaching, Scaffolding, Articulation, Reflection, and Exploration.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video begins with a slide titled "Apprenticeship Model Of Learning" with Hindi text "आध्यागम का शिक्षुता मॉडल". The instructor points to the text on the right, explaining that the model was proposed by Collins, Brown, and Newman (1989). She highlights that it extends traditional apprenticeship into the cognitive domain. She circles a symbol at the top right and points to the categories "Receptive meaningful learning" and "Metacognition" which group the learning activities. She emphasizes the key idea that learners acquire knowledge by observing, practicing, and interacting with experts in real-world contexts.

  2. 2:00 – 5:00 02:00-05:00

    The instructor focuses on the left column of the slide, which lists the six components vertically. She writes "MCS ARE" on the screen, likely a mnemonic for the components. She points to "Modeling," defined as providing a conceptual model. She moves to "Coaching," described as knowledge acquisition and development of a specific mental model. She points to "Scaffolding," defined as the development and stabilization of cognitive skills or heuristics of problem solving. She explains "Articulation" as the verbal articulation of thought processes, or "thinking aloud." She points to "Reflection," defined as the evaluation of thought processes by comparing them with others. Finally, she points to "Exploration," defined as solving new, but similar, tasks. She traces the flow from modeling to exploration.

  3. 5:00 – 6:06 05:00-06:06

    The slide changes to a detailed list titled "Major Components - The model consists of six teaching methods that structure the learning process". The instructor reads through the definitions and examples for each method. For "Modeling," she notes the teacher demonstrates the task, like solving a math problem step-by-step. For "Coaching," she explains the teacher observes students and gives hints, like guiding a science experiment. For "Scaffolding," she describes support provided while tasks are beyond current ability, such as guiding questions for an essay. She explains "Articulation" as learners explaining their thinking, like explaining a solution in class. For "Reflection," she notes learners compare their approach with peers, like analyzing an essay against model answers. Finally, for "Exploration," she describes learners applying skills independently, like designing a science project. She places checkmarks next to Modeling and Coaching.

The lecture provides a structured overview of the Cognitive Apprenticeship Model, moving from theoretical definitions to practical classroom applications. By breaking down the model into six distinct components—Modeling, Coaching, Scaffolding, Articulation, Reflection, and Exploration—the instructor clarifies how expert guidance facilitates cognitive skill acquisition. The use of specific examples, such as math problems and science experiments, grounds the abstract concepts in real-world educational scenarios, making the model accessible for understanding how metacognitive skills are developed through interaction.

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