What Is Deep Learning In Education?

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What Is Deep Learning In Education
What is deep learning? ‘When engaged in deeper learning, students think critically and communicate and work with others effectively across all subjects. Students learn to self-direct their own education and to adopt what is known as ‘academic mindsets’ and they learn to be lifelong learners.’
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What is an example of deep learning in education?

An Important Understanding of Deep Learning: – Deep learning can be implemented at all levels of learning, in all subject areas and programs. This definition of deep learning might lead some to think that this approach is geared only to older students and/or “gifted” students.

In fact, deep learning teaching and approaches can and should be found at all levels of learning, in all content areas, with all students, and in all types of school programs. For example, the early childhood deep learning teacher might read to students, summarize ideas, and learn information from an entire piece of literature (or non-fiction) and then raise meaningful, open-ended questions for discussion, interpretation, and analysis.

The high school deep learning English teacher might have students read five or six interesting books a year, and after reading and understanding the plot, story and vocabulary of each book, ask students to develop analyses and interpretations of the book through open-ended discussions, research, written reflections, and projects.

The middle school science teacher teaches fewer science topics in greater depth and spends considerable time helping students conceptualize key ideas, understand the nature and goals of the scientific method, conducting experiments with students, implementing science projects that require students to apply their science learning to new and novel situations, and even developing their own experiments! The sixth-grade teacher of American history uses essential questions and understandings to focus on important concepts and ideas for each unit, creates authentic experiences, such as a simulation of the Constitutional Convention, and asks students to use what has been learned to analyze and understand current issues and challenges.

Once the automotive teacher provides basic information, vocabulary, and simple problems, he provides students with more difficult automotive problems that require complex thinking and analysis for solutions. Generally, deep learning teachers are less focused on teaching many topics and providing a breadth of information, and more focused on promoting meaning and understanding, on making connections and building relationships between important information and ideas, and on promoting advanced analysis, interpretation, and application.

There are many opportunities for students to process information and ideas as they develop and use literacy and thinking skills. Students are less passive and more engaged in the learning process. Efforts are made to apply what is being learned to real life situations beyond school. In the next parts of this deep learning series, we will look at a more specific way to think about the dimensions of deep learning instruction, specifically focusing on levels of engagement and levels of learning.

NOTE: I want to give thanks to the many educators over many years that have contributed to our understanding of deep learning. One could argue that deep learning goes all the way back to Socrates and that John Dewey was a leading proponent of a deep learning education perspective.

Other, more recent researchers and educators include Norman L. Webb, Lynn Erickson, Jacqueline Grennon, and Martin Brooks, Grant Wiggins, and Jay McTighe, Howard Gardner, and Ron Ritchhart. Many others, some of whom are not very well known or read, also contributed to our knowledge of this topic. One to whom I owe a great debt is Fred Newmann, Professor Emeritus at the University of Wisconsin, and his colleagues.

His interpretations of his extensive, seminal classroom and school research provided me with detailed information regarding what constitutes deep learning, along with numerous examples of what constitutes deep learning instruction and assessment and what does not.

This article is part of a bi-weekly series primarily focused around basic and deep teaching and learning. There will also be occasional articles around other topics of interest to educators. You can find all the articles around basic and deep teaching and learning by clicking here or on the tag ‘Deep Learning’.

Elliott Seif is a long time educator, teacher, college professor, curriculum director, staff developer, author and Understanding by Design cadre member and ASCD faculty member. He currently writes about and addresses key educational issues, and volunteers his time in the Philadelphia School District.
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Why is deep learning important in education?

Deeper learning develops students’ abilities to think critically and solve complex problems, communicate effectively, work collaboratively, and learn independently. The teaching approaches that support deeper learning enable students to succeed and thrive in an ever-evolving and interconnected society.
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What are the characteristics of deep learning in education?

What is surface and deep learning? Expert perspectives – Click here to watch video lecture (Video 4:27 minutes) A range of experts in different fields describe their understanding of surface and deep learning, {video transcript] PANKAJ SAH: From a neuroscience perspective, the learning that takes place in the classroom is essentially about memory formation – it’s storage and its retrieval.

  • An understanding of brain functions will help us understand how that memory formation retrieval system operates.
  • Now in terms of an understanding of surface and deep learning, neuroscience tells us that the brain is a plastic organ which changes during learning, and there are changes to both the structure and function of the brain.

These changes are different depending on whether the learning is surface or deep. There are many different views in neuroscience about what is surface learning and what is deep learning. One way of distinguishing them is to think of surface learning as immediate or short-term learning, and deep learning as a consolidation process that leads to long term changes.

  1. In terms of the biology of taking learning from surface to deep, we think what is happening in the brain during consolidation is protein synthesis and development of new connections.
  2. In surface learning, while proteins are modified, they change and decay over a short period of time.
  3. It is in the consolidation process that this surface or immediate learning becomes long-term or deep learning through changes in gene transcription and new protein synthesis.
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In animal models, we’ve found that if you block the protein synthesis the long term or deep learning is blocked, but not the short term or surface learning. Professor DAVID REUTENS, Director of the Center for Advanced Imaging: The world we live in contains a lot of information, a lot of facts, and we encounter new information all the time.

My understanding of deep learning is that it’s a process of integrating new facts about the world into our existing semantic framework. It’s not like storing and retrieving information from a computer. Deep learning is not about memorizing things, but integrating the facts that we have into aggregates of information and models about how the world works.

I know that my memory isn’t great, but I can know plenty of things by integrating and connecting information. MERRILYN GOOS: From an educator’s perspective, surface learning involves recalling and reproducing content and skills. Deep learning involves things like extending ideas, detecting patterns, applying knowledge and skills in new contexts or in creative ways, and being critical of arguments and evidence.

  • One way we can understand how students are making meaning from what they are learning is to see how they engage in problem solving – either by themselves or with others.
  • PIETER ROSSOUW (From Mediros Clinical Solutions): Is there a difference between so-called deep learning and surface learning? Are there benefits in each of these? The research shows that there are big benefits in surface learning.

However, surface learning is a linear process, which is also quite often a very small neural process. Deep learning means we add neural connections so that we can see the consequences, the benefits, applied in different environments, et cetera. That’s more of an extensive neural network, rather than a simple neural network.
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How can teachers promote deep learning?

4. Flip the classroom – A “flipped” classroom promotes deeper learning by allowing students to take agency over their education by accessing lectures and instructional content at home, and then focusing on discussing the ideas and putting that learned information to use in the classroom.

  1. For example, teachers can create lessons using Google Slideshow or Docs, and record their explanations using the Loom or Screencastify apps, then assign the lessons for students to access the assignments at home.
  2. In the following class period, students would be prepared with that knowledge, and the teacher could facilitate the learning process as students synthesize what they have learned through various projects.

Hāpara Workspace is an excellent tool to aid in a flipped classroom, because it allows teachers to post videos and other teaching resources that students can access individually for review. To create a flipped classroom lesson, a teacher can post a learning objective in a Hāpara Workspace column.

  1. For example, “I will be able to develop a model describing gravity’s role in the motions within galaxies and the solar system.” The students would then access videos, articles, and practice activities about that topic from home on the Workspace to develop their understanding.
  2. Then, during the next class meeting, students would come in with the necessary background knowledge and begin constructing their models.

At the same time, the teacher can walk around the class and help students individually with the process.
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What is the biggest example of deep learning?

3 things you need to know – Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.

  1. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers.
  2. Deep learning is getting lots of attention lately and for good reason.
  3. It’s achieving results that were not possible before.
  4. In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound.

Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.
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What is the biggest advantages of deep learning?

6.5- Elimination of the need for data labeling – Data labeling can be an expensive and time-consuming job. With a deep learning approach, the need for well-labeled data becomes obsolete as the algorithms excel at learning without any guideline. Other types of machine learning approaches aren’t nearly as successful as this type of learning. K eeping in mind the above and more advantages of using deep learning approach, it can be said that it’s obvious to experience the impact of deep learning in different high-end technologies like Advanced System Architecture or Internet of Things in the future.
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What algorithm is used in deep learning?

Q1. Which Algorithm is Best in Deep Learning? – Multilayer Perceptrons (MLPs) are the best deep learning algorithm. It is one of the oldest deep learning techniques used by several social media sites, including Instagram and Meta. This helps to load the images in weak networks, assists in data compression, and is often used in speed and image recognition applications.
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What are the four elements of deep learning?

Four elements combine to create the new pedagogies and foster deep learning: pedagogical practices, learning environments, learning partnerships, leveraging digital.
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What are the three principles of deep learning?

Success is staying on course to your desired outcomes and experiences. What is the main ingredient in success? What are the 3 principles of deep and lasting learning? Prior Learning – The contribution of past learning to new learning. Quality of processing – Using deep processing learning strategies. Quantity of processing – Distributed and frequent practicing of the deep processing strategies. What is the CORE learning system? Collect information through reading and attending class. Organize it in a way that makes sense to you. Rehearse it (practice it). Evaluate it (get feedback). What are some wise choices in college? Read your college catalogue. See your advisor. Attend the first day of class on time. Sit where you can focus on learning. Complete your general requirements. What’s personal responsibility? Personal responsibility is responding wisely to life’s opportunities and challenges. Asses – What’s my present situation? Diagnose – How would I like it to be? Plan – What are my possible choices? Implement – What’s the likely outcome of each choice? How do you dispute irrational beliefs? Offer evidence that your judgements are incorrect. Offer a positive explanation. Question the importance of the problem. If judgements are true- change. What is mindless reading? Mindless reading is running your eyes over the words only to find you don’t recall anything. Active reading is intense mental engagement in what you read. Active Reading: What’s the big picture? Collecting key concepts, ideas, and supporting details. A key concept is the main idea. A secondary idea is a topic sentence elaborating on a main idea. What’s a supporting detail(s)? Examples, evidence, explanation, and experiences. What are some possible choices to become a better reader? Approach reading with a positive attitude. Create a distributed reading schedule. Read when you’re most alert. Read where you can concentrate. Review past readings/preview before reading. What’s the fastest way to preview? The fasted way to preview is by looking at your table of contents, looking at focus questions, look at chapter titles and headings. What are some ways to become a better reader? Read in chunks. Concentrate on reading faster. Pause to recite. Read for answers to questions on your list. Write new questions and their answers. Mark/annotate your text. Take notes. Active Reading: What’s the big picture? Collecting key concepts, ideas, and supporting details. What should I do after I read? Reflect on what you read – key concepts, ideas, etc. Reread difficult passages. Recite the marked text. Talk about what you read. Seek assistance. A key concept is the main idea. What’s the number 1 barrier to student success? A secondary idea is a topic sentence elaborating on a main idea. What 5 qualities does a goal need? Dated – a deadline. Achievable – realistic. Personal – what you want. Positive Specific What’s a supporting detail(s)? Examples, evidence, explanation, and experiences. What are some possible choices to become a better reader? Approach reading with a positive attitude. Create a distributed reading schedule. Read when you’re most alert. Read where you can concentrate. Review past readings/preview before reading. What’s the fastest way to preview? The fasted way to preview is by looking at your table of contents, looking at focus questions, look at chapter titles and headings. What are some ways to become a better reader? Read in chunks. Concentrate on reading faster. Pause to recite. Read for answers to questions on your list. Write new questions and their answers. Mark/annotate your text. Take notes. What should I do after I read? Reflect on what you read – key concepts, ideas, etc. Reread difficult passages. Recite the marked text. Talk about what you read. Seek assistance. What’s the number 1 barrier to student success? What 5 qualities does a goal need? Dated – a deadline. Achievable – realistic. Personal – what you want. Positive Specific Relax Use present tense Use all 5 senses Feel the feelings How do you learn what your instructors cover in class? You learn by taking notes. What is the goal of note taking? Collecting key concepts, ideas, and supporting details. What two categories does good note taking fall into? What can you do before taking notes? Create a positive affirmation. Assemble appropriate supplies. Complete HW assignments before class. Go over previous notes. Prepare a list of questions. Be organized. What do you do while taking notes? Listen for key concepts, main ideas, and supporting details. Ask and answers Q’s. Listen/watch for verbal/visual cues. Take notes on discussion. Stay focused. What does linear note taking mean? When you take notes in linear fashion, you record ideas in the order they are presented. What is the graphic note taking method? The graphic method is where you place key concepts, main ideas, and supporting details on the page shows both their level of importance and their relationship to one another. How can you speed up note taking? Condense – Listen and identify key concepts, etc. Leave a blank space – When you miss something, skip a few lines and pick up again. Use abbreviations – Create your own shorthand. What do you do after taking notes? Finish partical sentences. Expand on key words. Fill in spaces. Correct misspellings. Clarify unreadable words. Revise drawings or charts. What other things can help me polish my notes? Another thing that can help you polish your notes is comparing to other classmates. When and how will I evaluate my note taking plan? An effective way to evaluate your plan is to choose a future date, and, at that time, compare your situation with how you would like it to be. What are the 4 quadrants? Q1 – Important and urgent. Q2 – Important and not urgent. Q3 – Not important and urgent. Q4 – Not important and not urgent. What 3 tools can help you manage time? A monthly calender. A next actions list (record everything you need to do next). Tracking forms (directs your outer and inner steps toward employment goals). What 3 essential ingredients does self-discipline have? Commitment, focus, and persistence. Organizing study materials: What’s the big picture? Prior learning Quality of processing Quantity of processing CORE What are my possible choices for studying? Employ prime learning times. Tackle challenging subjects first. Take regular breaks. Change subjects periodically. Adopt a good mindset. What do you do before organizing study materials? Create an ideal space. Arrange to be undisturbed. Create a distributed study schedule. Gather course materials. What to do while organizing my study materials? Condense/organize course materials. Create outlines (linear). CORE
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Why deep learning should be applied to the modern teaching environment?

What is deep learning? – Deep learning allows a student to take principles from one situation and apply it to another. Preparing students for the future can be incredibly challenging for teachers. Even more so in an increasingly data-driven, automated world; today’s pupils are growing up in a scenario in which half of tomorrow’s jobs don’t yet exist.

Collaboration Creativity Critical thinking Citizenship Character Communication

Read why deep learning should be applied to the modern teaching environment in our blog.
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What is the difference between surface learning and deep learning?

Our Students as Learners Students’ approach to learning describes what they do when they learn and why. The basic distinction is between a deep approach to learning, where students are aiming towards understanding, and a surface approach to learning, where they are aiming to reproduce material in a test or exam, rather than actually understanding it. What Is Deep Learning In Education Figure 6. Approach to learning. Adapted from “Scuba diving Indonesia – Bali – Komodo – Bunaken 2008” by Ilse Reijs and Jan-Noud Hutten. Licensed under a Creative Commons Attribution 2.0 Generic (CC-BY 2.0). https://www.flickr.com/photos/39891373@N07/3665458233/ : Our Students as Learners
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What is deep learning in simple words?

What is Deep Learning? – Deep Learning Explained – AWS Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions.

Digital assistants Voice-activated television remotes Fraud detection Automatic facial recognition

It is also a critical component of emerging technologies such as self-driving cars, virtual reality, and more. Deep learning models are computer files that data scientists have trained to perform tasks using an algorithm or a predefined set of steps. Businesses use deep learning models to analyze data and make predictions in various applications.

Self-driving cars use deep learning models to automatically detect road signs and pedestrians. Defense systems use deep learning to automatically flag areas of interest in satellite images. Medical image analysis uses deep learning to automatically detect cancer cells for medical diagnosis. Factories use deep learning applications to automatically detect when people or objects are within an unsafe distance of machines.

You can group these various use cases of deep learning into four broad categories—computer vision, speech recognition, natural language processing (NLP), and recommendation engines.
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What is deep learning in one sentence?

What is deep learning? – Deep learning is a type of machine learning and artificial intelligence ( AI ) that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science, which includes statistics and predictive modeling,

  1. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier.
  2. At its simplest, deep learning can be thought of as a way to automate predictive analytics,
  3. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction.

To understand deep learning, imagine a toddler whose first word is dog, The toddler learns what a dog is – and is not – by pointing to objects and saying the word dog, The parent says, “Yes, that is a dog,” or, “No, that is not a dog.” As the toddler continues to point to objects, he becomes more aware of the features that all dogs possess.
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How is deep learning used in everyday life?

Vocal AI – When it comes to recreating human speech or translating voice to text, deep learning has a critical role to play. Deep learning models enable tools like Google Voice Search and Siri to take in audio, identify speech patterns and translate it into text.

  • Then there’s DeepMind’s WaveNet model, which employs neural networks to take text and identify syllable patterns, inflection points and more.
  • This enables companies like Google to train their virtual assistants to sound more human.
  • In addition, Mozilla’s 2017 RRNoise Project used it to identify and suppress background noise in audio files, providing users with clearer audio.

Relevant companies: Mozilla, DeepMind, Apple
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Why is it called deep learning?

Why does deep learning have the name “deep”? In this series, you can ask questions and get your doubts cleared. The expert team from INDIAai will answer your questions. Send your questions to: [email protected], How does deep learning work? Why does it have the name “deep”? – Sinduja Sunder, Chennai.

Deep learning is a subset of machine learning. A neural network with three or more layers is essentially what deep learning is. These neural networks mimic how the human brain works, even though they aren’t very good at it. It lets them “learn” from large amounts of data. A neural network with only one layer can still make rough predictions, but adding more hidden layers can help to optimize and refine for accuracy.

Deep learning is at the heart of many applications and services that use artificial intelligence (AI) to improve automation by doing analytical and physical tasks without human help. Deep learning is used in everyday products and services like digital assistants, voice-enabled TV remotes, and credit card fraud detection.

It is also used in new technologies (such as self-driving cars). Deep Learning gets its name from the fact that we add more “Layers” to learn from the data. If you don’t already know, when a deep learning model learns, it just changes the weights using an optimization function. A Layer is a row of so-called “Neurons” in the middle.

Why do we use Machine Learning? Why is it essential? – Parvathi S, Palakad. With machine learning, a user can give a computer algorithm a vast amount of data and have the computer analyze the data and make decisions and suggestions based on the data. All businesses need data to run.

Making decisions based on data is becoming increasingly important to keep up with the competition or fall further behind. Machine learning can be the key to unlocking the value of corporate and customer data and making decisions that keep a company ahead of the competition. Can AI predict natural disasters? – Gopinath, Delhi Yes, scientists have figured out how big a tsunami will be even when they don’t know much about the earthquake that caused it.

Together, we can make predictions using Global Navigation Satellite System (GNSS) data and artificial intelligence (AI). What is responsible AI? Can you give an example? – Sai Aadvik, Pune Responsible AI is designing, developing, and deploying AI to empower employees and businesses while positively impacting customers and society, allowing companies to build trust and scale AI confidently.

A self-driving car, for example, can collect images from sensors. A machine learning model can use these images to make predictions (e.g. the object in front of us is a tree). Then, the car uses these predictions to make decisions (e.g. turn left to avoid the tree). Their principles emphasize fairness, transparency and explainability, humanity, privacy and security.

What is computer vision? Is computer vision and CNN the same thing? – Monisha Kesavan, Hyderabad Computer vision is an interdisciplinary field of science that looks at how computers can understand complex things from digital pictures or videos. From an engineering point of view, it tries to figure out how the human visual system works and find ways to automate tasks that it can do.

  • CNN is a deep learning network for computer vision that can recognize and classify different picture parts.
  • CNN’s design was influenced by how the visual cortex is set up and what it does.
  • It is made to look like how neurons in the brain connect.
  • Facial recognition applications, which let computers match pictures of people’s faces to their names, also use computer vision in a big way.

Algorithms for computer vision can find facial features in images and compare them to facial profiles in a database. How does natural language processing work? How is it used in real life? – Ajith Kumar, Vellore. In natural language processing, human language is broken up into pieces so that the grammatical structure of sentences and the meaning of words can be analyzed and understood in context.
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Is deep learning artificial intelligence?

Unlike machine learning, deep learning is a young subfield of artificial intelligence based on artificial neural networks. Since deep learning algorithms also require data in order to learn and solve problems, we can also call it a subfield of machine learning.
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What is an example of learning by doing in education?

For example, students learning about floating and sinking in a science classroom might do an experiment in which they fold a piece of paper to make a boat and see how many coins it can carry. This kind of activity may be fun and engaging. The students may well learn how to make boats out of paper.
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Which of the following is an example of deep learning applications?

3. Healthcare – Deep Learning has found its application in the Healthcare sector. Computer-aided disease detection and computer-aided diagnosis have been possible using Deep Learning. It is widely used for medical research, drug discovery, and diagnosis of life-threatening diseases such as cancer and diabetic retinopathy through the process of medical imaging.
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