As I indicated in my first commentary on deep learning, deep learning knowledge, abilities and competencies are important for living, working and being a good citizen in a 21st-century world. Deep learning promotes the qualities children need for success by building complex understanding and meaning rather than focusing on the learning of superficial knowledge that can today be gleaned through search engines. Deep learning instruction provides students with the advanced skills necessary to deal with a world in which good jobs are becoming more cognitively demanding. It prepares them to be curious, continuous, independent learners as well as thoughtful, productive, active citizens in a democratic society.
Yet even with all the attention deep learning is getting, it is often not clear what deep learning looks like and what makes for a deep learning teacher. In this and future commentaries, I will both try to further define a deep learning education, explain what it means to be a deep learning teacher, and provide numerous examples of the types of programs, policies, and teaching behaviors that support a deep learning perspective. I hope that this will be helpful to all who are interested in learning more about this topic and also in how to implement a deep learning school program[1].
So what is deep learning? What does it look like in classrooms? One way to explore what is a deep learning education is to provide two examples of classroom activity adapted from Newmann, Fred, and Associates. Authentic Achievement: Restructuring Schools for Intellectual Quality, pp. 63. One of these descriptions represents deep learning, and the other does not. As you read these, think about the differences in teacher behavior and student learning:
Example A: Ninth grade students in an interdisciplinary math and science course spent the school year focusing on three essential questions: How do things move? What makes them move? How can we describe that motion? After some initial learning designed to provide a basic understanding of motion through a series of interactive activities, Ms. E built the culminating exploration of these three questions around the students’ ability to apply their learning and design an amusement park ride.
As a field experience related to their class project, Ms. E and her students spent a day gathering data at Adventures Unlimited, an amusement park. Equipped with stopwatches and a meter to measure gravity, they spent the day analyzing the bus ride and the rides in the park. During the debriefing of the trip, students applied concepts they had learned like inertia, centrifugal force, and centripetal force to both the bus and the amusement park rides.
During classes after the field trip, the students graphed and discussed acceleration and deceleration problems. They used their information to help them design their amusement park ride. The teacher pushed the students to think about, further study, and apply their knowledge with each new problem. According to an observer, the teacher helped students wrestle with “…time, distance, velocity, acceleration, deceleration, and the relationships among them…”. At the completion of the unit, students wrote an extensive paper explaining and detailing their ride design, including diagrams of the design, and provided technical information to show that their design was realistic and doable.
Example B: Mr. H’s math classes typically consisted of students’ applying formulas to problems, such as finding the volume of a prism that the teacher drew on the chalkboard. “There are many ways to do this”, he told the class – meaning that there are many ways to do the calculation. He then said:” You can cancel out or you can multiply the tops and bottoms” and then demonstrated the calculations both ways in great detail. As he emphasized the use of worksheets and drills, Mr. H failed to pick up on students’ questions to dig deeper into the topic or to challenge students to devise and explain different approaches to finding the volume.
At the end of a unit on angle measures in interconnected figures and geometric polygons, Mr. H assigned students the unit test provided with the textbook. The test consisted of finding and writing the degrees of angles in a complex figure, and matching the addition of several angles to a correct description of the angles (e.g. complementary angles). Through the test, the students demonstrated technical proficiency in calculating angles. There was no opportunity to apply this skill or to discuss their computations in a substantive manner.
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As you look at these two examples, I hope that it is obvious to you which is the example of the more traditional learning and which is the example of deep learning. The differences are stark! In Example A, a cluster of meaningful open-ended questions forms the basis of learning, and the teacher is striving for in-depth student understanding of both physics and mathematics principles and concepts. Throughout these lessons, there is a significant amount of interactive learning in which students are constructing meaning and processing information and ideas. Students actively use their intellect to first understand some basic principles and ideas, and then apply, analyze and interpret their basic learning through new authentic experiences. They are fully engaged in an authentic experience that helps them deepen their understanding and develop cognitively complex learning skills and habits of mind. And, finally, their learning has a value beyond school – they are working from potentially real-world experiences like those that students may face later in life as engineers, designers, or general problem solvers.
Few if any of these qualities are apparent in Example B. In this often typical math teaching example, students learn the superficial trappings of mathematics by being provided with and applying formulas in a rote way. In this primarily teacher-centered class, students are finding answers to math problems with very little input or thinking. Students are generally passive learners, using algorithms to find pat answers. There is no opportunity to consider how interconnected figures or geometric polygons matter in or apply to the world outside of and beyond school.
Based on these differences, and on the work done by many researchers, I suggest four key criteria that define and describe deep learning and help to determine whether a teacher is a deep learning teacher:
The teacher has a deep learning mindset. She makes a significant effort to go beyond basic understanding and skill development and is striving for students to develop more in-depth understanding, the ability to apply key concepts, ideas, and skills, and advanced understanding, critical and creative thinking, and “learning to learn” skill development.
Students are heavily engaged in the learning process. They are given greater opportunity to ask questions, construct meaning, talk to and learn from and with others, develop alternatives and solutions, provide their own insights and solutions, and think more deeply with greater complexity. They are generally more independent learners, thoughtful, collaborative, and pro-active.
Instructional activities promote “high cognitive challenge”, such as analyzing data, constructing interpretations, developing carefully constructed points of view, and figuring out complex solutions to problems.
Students are given opportunities to apply to learn to authentic situations that build deep learning understanding and skills, develop curiosity and interest, promote critical habits of mind, and illustrate the value of the learning beyond school.
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’.
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