this is learning
play

This is learning for students and teachers Ashwin Brouwer - PowerPoint PPT Presentation

Oct 29, 2023 •205 likes •598 views

This is learning for students and teachers Ashwin Brouwer Friesland College New perspective on learning In a world full of possibilities Lets talk about how learning and webtools come together Sharp view of learning for students and teachers

  1. This is learning for students and teachers Ashwin Brouwer Friesland College

  2. New perspective on learning

  3. In a world full of possibilities

  4. Let’s talk about how learning and webtools come together

  5. Sharp view of learning for students and teachers

  6. Being able to is more complex than doing

  7. Being able to requires more Being able to Doing [FC X-Stream]

  8. 21 st century calls for an ‘ A employee’

  9. ‘C employee’ ‘A employee’ Needs control Needs freedom Operates out (continuous) directing Works from an expectation Takes no initiative Takes initiative Does not think along Checks own quality Does not come with a proposal Comes with a proposal Asks no help Asks for help if needed Is only industrious Communicates with others [FC-Sprint²] Who has the future?

  10. The ‘ A employee’ has a growth mindset

  11. Intelligence Fixed is not fixed mindset Growth mindset [Carol Dweck]

  12. The brain never stops learning

  13. You can train the brain The brain Learning something new provides connections By repeating the connections strengthens New connections can be created always [Carol Dweck]

  14. Growth mindset has high expectations

  15. Out of your comfort zone Expectation High Expectation [FC-Sprint²]

  16. These high expectations need to offer clear frames

  17. Frames No frame give space [FC X-Stream] Clear frame

  18. Offer different resources within the framework

  19. Mix per person fitting to the target Learning styles do not exist [Tesia Marshik]

  20. Combining and mixing these activities gives motivation

  21. Autonomy Space for the student Bright framework Several sources Mastery Motivation Getting better and better Being involved with the objective Purpose Clear result Contributes to the whole High expectations [Daniël Pink]

  22. The student is responsible for the final results

  23. Teacher Student Student Teacher Give space to learn [Pareto Principe]

  24. The teacher encourages learning

  25. Different role for the teacher Present what you High expextation have learned Teacher Process à resources explains [Jan Deutekom]

  26. Targeting the process Targeting the good Targeting options Feedback to learn from [Carol Dweck]

  27. The student learns from reflection

  28. Processing Meaningful Acquire Processing Act with (reflection) experience insights (decide) insight Reflection is a tool to learn to act [Kariene Mittendorff]

  29. Reflection is more than just acting

  30. Reflection brings you In learning and insight effective learning In personal development In professional identity [Friesland College]

  31. Let’s reflect on what we have learned

  32. In a world full of possibilities

  33. Matching theory with social media and webtools

  34. Tool Theory Build a Wordpress portfolio Complex tasks Youtube, OneDrive, Google Drive ‘A employee’ Kahoot, Socrative, Quizizz Growth mindset Studyblue, Formative, Testmoz The brain Canva, Youtube, Facebook High expectations Padlet, Prezi, Popplet Clear frames Blendspace, Symbaloo, Start.me Different resources Facebook, Youtube, Twitter Motivation Swipe.to, Blendspace Student space Tackk, PowerPoint, Canva Different teacher role AnswerGarden, Mentimeter Feedback TodaysMeet, Kahoot, Youtube Reflection to act Google forms, ParticiPoll, Kahoot Reflection for insight

  35. What are you going to change, try or use?

  36. Get inspired and visit socialmediainhetmbo.nl/metalearn

  37. Fan a flame? a.brouwer@fcroc.nl 088 060 2070 Ashwin Brouwer @ashwinbrouwer Ashwin Brouwer

  38. Special thanks to Albert van der Meulen, Jan Deutekom en Jos Nijkrake [By Ashwin Brouwer, Friesland College, november 2015] translated by Sjoerd Wijkel

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
The JOY of LEARNING Think of a topic you are passionate about Think about your learning

The JOY of LEARNING Think of a topic you are passionate about Think about your learning

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

1.03k 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
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

404 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,

441 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

878 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

More recommend