Part 1: What is Deep Learning? Who are the Deep Learning Teachers?
Note: This commentary is the first of a series on deep learning that will be posted on ASCD Edge approximately once a week for the next ten weeks. Each commentary will take on a different aspect of deep learning, such as school culture that supports deep learning, curriculum issues, assessment and instruction for deep learning, and other aspects. If you are interested in this topic, look out for the upcoming installments.
Much has been written recently about the critical importance of deep learning school experiences. Deep learning abilities and competencies are important for living in a 21st century world, and many of the Common Core components suggest a deep learning perspective. Deep learning promotes the qualities children need for success by building understanding and meaning rather than superficial knowledge. Deep learning instruction provides students with the tools necessary to deal with a world in which good jobs are more cognitively demanding. It prepares them for a lifetime of learning, and helps to prepare our children to be productive, active citizens in a democratic society.
Yet even with all the attention deep learning is getting, it is still often not clear what deep learning is and what makes for a deep learning teacher. In this latest series of blogs, I will both try to define deep learning, 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.
So what is deep learning? One way to explore deep learning is to provide two real examples of classroom activity described by a class observer, one that represents deep learning and one that 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? Ms. E built the exploration of these three questions around the design of 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 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 and deepen 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 providing 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 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.
As you look at these two examples, I hope that it is obvious to you which is the example of deep learning and which is the example of “superficial” learning. The differences are stark! In Example A, the teacher is striving for “in-depth understanding” of both physics and mathematics principles and concepts. A cluster of meaningful questions forms the basis of learning. There is a significant amount of interactive learning in which students are constructing meaning, processing information and ideas, and using their intellect to analyze, and interpret what they are learning and their experiences. Students are fully engaged in authentic experiences to help them to understand and apply what they have learned. And, finally, their learning has a value beyond school – it applies to real world experiences that some students may face later in life as engineers, designers, or even as problem solvers.
Few if any of these qualities are apparent in Example B. In this typically traditional math teaching example, students learn the superficial trappings of mathematics by being provided with and applying formulas in a rote way. The teacher is demonstrating the answers to problems, with very little input or thinking by students. Students are generally passive learners. 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 is striving for students to develop more in-depth understanding of key concepts, ideas, and skills to be learned, rather than a superficial exposure to many facts, concepts, ideas, and skills;
- Students take an active-interactive role in the learning process by asking questions, constructing meaning, talking to and learning from and with others, developing alternatives, providing insights, and generally being thoughtful and collaborative.
- Students are frequently engaged in activities that foster deeper learning, such as building on prior learning, organizing information, conducting meaningful research, developing concepts and conceptual understanding, analyzing data, constructing interpretations, developing carefully constructed points of view, figuring out solutions to problems, and applying learning to new situations.
- Students are given many opportunities to apply learning to authentic situations that illustrate the value of the learning beyond school.
Reasons for and Implications of a Deep Learning Education
Using these deep learning criteria, deep learning qualities, approaches and models can be found at all levels of learning, in all content areas. The early childhood deep learning teacher reads entire books to students and raises meaningful questions for discussion. The high school deep learning English teacher might have students read five or six interesting books a year, each examined with open ended discussions, written reflections and projects. The middle school science teacher teaches fewer science topics in greater depth, spends considerable time helping students understand the nature and goals of the scientific method, conducting experiments with students, and developing many science projects that apply learning to new and novel situations. The sixth grade teacher of American history teaches only a portion of American history (e.g. from early explorers to the American Revolution) and uses essential questions and understandings to examine important concepts and ideas for each unit, involves students in interesting and meaningful activities, such as a simulation of the Constitutional Convention, and then applies what has been learned to current issues and challenges. Generally, deep learning teachers are less focused on teaching many topics and providing breadth of information, and more focused on promoting greater meaning and understanding, on making connections and building relationships between important information and ideas, and promoting 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 school culture and beliefs, along with curricular, instructional and assessment models and frameworks, that help make deep learning a realistic alternative to the more traditional teaching still found in most American schools.
 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. I suggest that anyone interested in this topic find a way to get copies of the books referenced here for a unique and timely introduction to deep learning:
Newmann, Fred, Secada, Walter, and Wehlage, Gary, A Guide to Authentic Instruction and Asseessment: Vision, Standards and Scoring. Wisconsin Center for Educational Research, Madison Wisconsin, 1995 (If there is one book to get, this is it);
Newmann, Fred and Wehlage, Gary. Successful School Restructuring: A Report to the Public and Educators by the Center on Organization and Restructuring of Schools. Madison Wisconsin: Wisconsin Center for educational Research, 1995;
Newmann, Fred and Associates. Authentic Achievement: Restructuring Schools for Intellectual Quality. San Francisco: Jossey Bass Publishers, 1996.
Newmann, Fred, Marks, Helen M, and Gamoran, Adam. Authentic Pedagogy: Standards that Boost Student Performance, in Issues in Restructuring Schools, Issue Report No. 8, spring, 1995.
Smith, Julia, B., Lee, Valerie E., and Newmann, Fred. Instruction and Achievement in Chicago Elementary Schools. Improving Chicago’s Schools Report. Chicago, Ill: Consortium on Chicago School Research, 2001.
 Examples A and B adapted from Newmann, Fred and Associates. Authentic Achievement: Restructuring Schools for Intellectual Quality. San Francisco: Jossey Bass Publishers, 1996, pp. 65-66 and pp. 63-65.
Elliott Seif is a long time educator, teacher, college professor, curriculum director, ASCD author and Understanding by Design cadre member and trainer. He currently continues to write about and address educational issues and volunteers his time in the Philadelphia School District. His website can be found at: www.era3learning.org.