the joy of learning
play

The JOY of LEARNING Think of a topic you are passionate about Think - PowerPoint PPT Presentation

Aug 07, 2023 •839 likes •1.03k views

The JOY of LEARNING Think of a topic you are passionate about Think about your learning process How much time was spent? How did you practice? To experience research-based learning tools: Focus/Diffuse modes of thinking

  1. The JOY of LEARNING • Think of a topic you are passionate about • Think about your learning process • How much time was spent? • How did you practice?

  2. • To experience research-based learning tools: • Focus/Diffuse modes of thinking • Chunks • Recall • Pomodoro Technique • Collaborate to develop 1-2 ideas to incorporate into your teaching next week and beyond

  3. Learning How to Learn Work of Barbara Oakley Ph.D., Oakland University “ A Mind for Numbers: How to Excel at Math and Science ” Presenter: Kenda Caligure

  4. Two modes of thinking • Focused • Diffused • To learn something new – go back and forth between the FOCUS and DIFFUSE Modes • Learning something difficult takes TIME! • Path to EXPERTISE is built bit by bit • Learning changes the structure of the brain

  5. Think about it… How do you SWITCH from Focused to Diffuse mode?

  6. Chunking • Pieces of information bound together through meaning • Packages of information your brain can easily access • Chunking = Efficient BRAIN

  7. How to Chunk • Focus on the information • Understand basic/main ideas “You want your brain to become used to the idea that just knowing how to use a particular problem-solving technique isn’t enough – you also need to know when to use it.” Oakley, p.75 • Practice

  8. Chunking Examples • Multiplication Tables • Backing a car out of the driveway • Think again about that topic you are passionate about. Describe to a neighbor a chunk involving that topic that was at first difficult for you to grasp but now seems easy.

  9. Recall • Retrieval Practice – more effective than rereading • Karpicke , JD. “Retrieval - based learning: Active retrieval promotes meaningful learning.” Current directions in Psychological Science 21, 3 (2012): 157-163 • Predicting what will be on the test • Self-testing • Testing/Quizzing – great forms of Recall • TRY IT…RECALL what we’ve covered so far • Recall: What were the key points in your last lesson? What do you want your students to remember? Generate ways for students to RECALL these key points.

  10. What you are teaching next week? • Key points to address • Create a draft learning activity (In-Class or Outside-Class ) to incorporate one or more: • Focus/Diffuse modes • Chunking • Recall • Share activity with a neighbor • Person sharing: share briefly key point(s) and the activity • Neighbor’s job: listen to entire idea, then make suggestions

  11. “ You must have information Spaced Repetition persisting in your memory if you are to master the material well enough to do well on tests and think creatively with it. Barbara Oakley, p. 66 • Scheduled study – plan the work/work the plan • Deliberate Practice – practice on the toughest aspects of the material • Interleaving – varying learning by hopping back after a break to strengthen an approach learned earlier • Mini-Victories lead to motivation • Importance of Sleep

  12. PROCRASTINATION “ When you procrastinate, you are leaving yourself only enough time to do superficial focused-mode learning. You are increasing your stress level because you know you have to complete what feels like an unpleasant task .” Oakley, p.24

  13. PROCRASTINATION • Brain’s pain -center activates when we think about something we don’t want to do • Making a choice to “procrastinate” gives temporary relief (ONLY) • Reprogramming Your Brain – Pomodoro Technique • http://tomato-timer.com/

  14. Process NOT Product • Focus on making • Progress not perfection • Focus on product equals: • Judging self • Stress

  15. Group discussion on Procrastination • In groups of 4-5, discuss: • Ways to incorporate teaching students about ways to decrease procrastination habits • How do you incentivize decreased procrastination?

  16. COCC Study Strategies – HumanDev 101 • Covers the “Learning to Learn” strategies and many others to help students develop and practice habits and strategies to become more efficient and effective learners • Begin designing activity for your classes to help those student’s apply this information • How can you help those who haven’t taken the class…

  17. Summary • Recall what was covered today: • Focus/Diffuse Modes of Thinking • Chunking • Recall • Procrastination

  18. Coursera: Learning How to Learn • https://www.coursera.org/course/learning • It’s FREE and always open • 4 weeks, videos and quizzes (Free) additional assignments for Certificate • Brainfacts.org • Strengthening the Student Toolbox: Study Strategies to Boost Learning

Recommend

Learning is a never-ending process Tasks come and go, but learning is forever Learn more e ff

Learning is a never-ending process Tasks come and go, but learning is forever Learn more e ff

NeurIPS 2018 Tutorial on Automatic Machine Learning Learning to Learn Learning Learning Learning Learning Learning automl.org/events -> AutoML Tutorial -> Slides Frank Hutter Joaquin Vanschoren Eindhoven University of Technology

1.36k views • 40 slides
What is Action Learning? What is an ALC? 1 What is Action Learning? Learning by doing

What is Action Learning? What is an ALC? 1 What is Action Learning? Learning by doing

What is Action Learning? What is an ALC? 1 What is Action Learning? Learning by doing Process for bringing together a group of people with varied levels of skills and experience (but with influence, passion, etc. to impact an

191 views • 5 slides
Learning From Data Lecture 2 The Perceptron The Learning Setup A Simple Learning Algorithm: PLA

Learning From Data Lecture 2 The Perceptron The Learning Setup A Simple Learning Algorithm: PLA

Learning From Data Lecture 2 The Perceptron The Learning Setup A Simple Learning Algorithm: PLA Other Views of Learning Is Learning Feasible: A Puzzle M. Magdon-Ismail CSCI 4100/6100 recap: The Plan 1. What is Learning? 2. Can We do it?

613 views • 25 slides
4. Learning in MAS 4.1. What is learning? One definition: Bower, Hilgard: Theories of learning,

4. Learning in MAS 4.1. What is learning? One definition: Bower, Hilgard: Theories of learning,

4. Learning in MAS 4.1. What is learning? One definition: Bower, Hilgard: Theories of learning, Prentice-Hall, 1975: Learning refers to the change in a subjects behavior to a given situation brought about by his repeated experiences in

173 views • 15 slides
What are learning outcomes? Learning Outcomes Learning outcomes are statements that specify what

What are learning outcomes? Learning Outcomes Learning outcomes are statements that specify what

What are learning outcomes? Learning Outcomes Learning outcomes are statements that specify what learners will know or be able to Everybody Wins do as a result of a learning FYE 2007 activity. Dr. Carolyn Hopper Middle Tennessee State

344 views • 11 slides
Machine Learning 11 AI Slides (6e) c Lin Zuoquan@PKU 1998-2020 11 1 11 Machine Learning

Machine Learning 11 AI Slides (6e) c Lin Zuoquan@PKU 1998-2020 11 1 11 Machine Learning

Machine Learning 11 AI Slides (6e) c Lin Zuoquan@PKU 1998-2020 11 1 11 Machine Learning 11.1 Learning agents 11.2 Inductive learning 11.3 Deep learning 11.4 Statistical learning 11.5 Reinforcement learning 11.6 Transfer learning

1.7k views • 159 slides
04 FOCUS ON LEARNING STUDENTS REFLECTING ON THEIR LEARNING In this unit we are learning to

04 FOCUS ON LEARNING STUDENTS REFLECTING ON THEIR LEARNING In this unit we are learning to

workshop 04 FOCUS ON LEARNING STUDENTS REFLECTING ON THEIR LEARNING In this unit we are learning to . discuss the role of reflection as an important part of learning identify strategies that will help students to reflect on their

539 views • 14 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
Further thoughts How does e-Learning relate to instruction? How does eLearning change

Further thoughts How does e-Learning relate to instruction? How does eLearning change

Ideas from group What is the difference between learning and training? What is the difference between learning and education? Where does learning take place? Who learns? What is the role of instruction in learning? Further

800 views • 47 slides
Learning Sciences: Impact on Learning Technologies & Learning Activities Phillip D. Long,

Learning Sciences: Impact on Learning Technologies & Learning Activities Phillip D. Long,

Learning Sciences: Impact on Learning Technologies & Learning Activities Phillip D. Long, Ph.D. 12-May, 2015 Narrative Arc ( 5 2) Conclusion: What do we want? Learning learning |lrniNG | noun the acquisition of knowledge or

653 views • 36 slides
The Learning Tree Workshop: The Learning Tree Workshop: Regulating Sensation Series on Learning

The Learning Tree Workshop: The Learning Tree Workshop: Regulating Sensation Series on Learning

The Learning Tree Workshop: The Learning Tree Workshop: Regulating Sensation Series on Learning Differences, Learning Challenges, and Learning Strengths Transcript by Stanley I. Greenspan M.D. Narration by Brian MacDonald Sensory Modulation

453 views • 16 slides
Foundations of AI Why learning works 1 6 . Statistical Machine Learning Bayesian Learning and

Foundations of AI Why learning works 1 6 . Statistical Machine Learning Bayesian Learning and

Contents Statistical learning Foundations of AI Why learning works 1 6 . Statistical Machine Learning Bayesian Learning and Why Learning Works W olfram Burgard, Bernhard Nebel, and Andreas Karw ath 10/ 1 10/ 2 Statistical Learning

305 views • 5 slides
Welcome to Welcome to The Learning Tree Workshop Series on Learning Differences, Learning

Welcome to Welcome to The Learning Tree Workshop Series on Learning Differences, Learning

Welcome to Welcome to The Learning Tree Workshop Series on Learning Differences, Learning Challenges, and Learning Strengths Strengths As a companion to Dr. Greenspans The Learning Tree : g Overcoming Learning Disabilities From the

367 views • 16 slides
Active Learning Passive Learning Active Learning 1. Think 1. Acquisition of knowledge Ability

Active Learning Passive Learning Active Learning 1. Think 1. Acquisition of knowledge Ability

Active Learning Passive Learning Active Learning 1. Think 1. Acquisition of knowledge Ability 2. Decision making 2. Application of knowledge 3. Explain/communicate 1. Deep learning Approach to Uni-directional 2. Interactive Learning

282 views • 5 slides
Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning

Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning

GCT634/AI613: Musical Applications of Machine Learning (Fall 2020) Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning Pipeline in Classification Tasks A set of hand-designed audio features are selected

398 views • 26 slides
1 Why Study Machine Learning? Why Study Machine Learning? Cognitive Science The Time is Ripe

1 Why Study Machine Learning? Why Study Machine Learning? Cognitive Science The Time is Ripe

What is Learning? Herbert Simon: Learning is any process by which a system improves performance from CS 391L: Machine Learning experience. Introduction What is the task? Classification Problem solving / planning /

603 views • 6 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
MACHINE LEARNING, STATISTICAL LEARNING AND PARALLEL COMPUTING INTRODUCTION VS MACHINE LEARNING

MACHINE LEARNING, STATISTICAL LEARNING AND PARALLEL COMPUTING INTRODUCTION VS MACHINE LEARNING

JACOPO DI IORIO, MATTEO FONTANA, DANIELE OCCHIUTO, CLAUDIA VOLPETTI MACHINE LEARNING, STATISTICAL LEARNING AND PARALLEL COMPUTING INTRODUCTION VS MACHINE LEARNING STATISTICAL LEARNING Separate evolutions, but with shared methodologies MERGE

565 views • 55 slides
Improving Integration of Learning in Learning Communities Through the Development of a Shared

Improving Integration of Learning in Learning Communities Through the Development of a Shared

Improving Integration of Learning in Learning Communities Through the Development of a Shared Learning Space Presented by: Emily Richardson richardsone2@queens.edu Director of the Hayworth Center for Online Learning; CATO School of Education

547 views • 15 slides
UNSUPERVISED LEARNING, CLUSTERING UNSUPERVISED LEARNING UNSUPERVISED LEARNING Supervised

UNSUPERVISED LEARNING, CLUSTERING UNSUPERVISED LEARNING UNSUPERVISED LEARNING Supervised

UNSUPERVISED LEARNING, CLUSTERING UNSUPERVISED LEARNING UNSUPERVISED LEARNING Supervised learning: X - y pairs, f(x) function approximation Unsupervised learning: only X, no y Exploring the space of X measurements,

440 views • 14 slides
3. Preference Learning Techniques a. Learning Utility Functions b. Learning Preference

3. Preference Learning Techniques a. Learning Utility Functions b. Learning Preference

AGENDA 1. Preference Learning Tasks 2. Performance Assessment and Loss Functions 3. Preference Learning Techniques a. Learning Utility Functions b. Learning Preference Relations c. Structured Output Prediction d. Model-Based Preference

583 views • 39 slides
Machine Learning: Study of algorithms that improve their performance P at some task T

Machine Learning: Study of algorithms that improve their performance P at some task T

Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 12, 2015 Today: Readings: What is machine learning? The Discipline of ML Decision tree learning Mitchell, Chapter 3

872 views • 29 slides
Learning about vs learning to be It's not what you know, It's what you want to learn. How to

Learning about vs learning to be It's not what you know, It's what you want to learn. How to

Learning about vs learning to be It's not what you know, It's what you want to learn. How to get a job 1. study hard 2. Get good grades 3. Make a resume 4. Do research 5. Apply to jobs 6. Get interviewed 7. Get an offer 8. Accept the

422 views • 26 slides
12.1 Active Learning: A Review When learning, it may be the case that getting the true labels of

12.1 Active Learning: A Review When learning, it may be the case that getting the true labels of

CS/CNS/EE 253: Advanced Topics in Machine Learning Topic: Active Learning Review and Kernel Methods Lecturer: Andreas Krause Scribe: Jonathan Krause Date: Feb. 17, 2010 12.1 Active Learning: A Review When learning, it may be the case that getting

213 views • 5 slides

More recommend