I love to think about learning, and there are many directions we can go, but today, I’m going to give background about the learning sciences. Why? For one reason, because I believe that with better knowledge of learning sciences, practitioners can help researchers do a better job designing Cyberlearning tools and environments. A second reason is that researchers can help practitioners in understanding when learning is occurring or why it isn’t occurring, and even how to help make it occur. I think that as partners, we can do far more than we can alone. To become better partners, we need to speak the same language. This post is a start; I hope that you will join in the conversation about learning and learning science.
In the class I teach for first year doctoral students at Pepperdine, many of whom are (fabulous) K-12 teachers, my students and I think deeply about how people learn. Our conversation starts by asking, “What are learning sciences?” We use this book, The Cambridge Handbook of the Learning Sciences (2nd Edition), to guide much of our thinking. It’s a big book, and some students were dubious, but after reading it, they told me they enjoyed reading it. Below I share some of the topics we discuss as we read the first chapter in the book. If you like, the first (introductory) chapter is available as a free sample from Amazon so you can read it. If you want a quick summary, here are some of the things my students and I typically discuss when we read:
What are learning scientists? Learning scientists are people from diverse backgrounds who care about how people learn in schools, in museums, after school organizations, on the job, or anywhere. They have a deep understanding of cognitive processes (what happens in learning in the minds of the learner), and social processes (what happens in situations and interactions between students with other students, students and teachers) and use their knowledge of learning to design and improve the settings that they study. Their research and studies are often done in partnership with practitioners and students to see learning theories at work in the real world, not just as theory. Some learning scientists started their careers as teachers or other practitioners and so they have a very good understanding what the real world is like.
Traditional approach of schooling versus new thinking. Instructionism, or the teacher asthe experttelling students what they need to knowand the students accepting it without questioning it, is the traditional model of schooling (Papert, 1993). Many of us have experiences in beinglectured to by teachers who subscribe to instructionism. Instructionism makes the assumption that we can fill empty minds with knowledge andthat presenting the same materials to different learners will have the same results. Instructionism is contrasted to constructivism where weassume learners are different and need to construct their own understanding based on what they already know through interacting with newinformation and others. I have heard from teachers that as Common Core and Next Generation Science Standards (NGSS) become more widelyimplemented in schools, different methods for helping learners--not just telling students what they should know--are needed. In informal settingsand in some schools, hands-on, active learning with inquiry- or production-centered methods are used to help learners.
Learning scientists (Sawyer, 2006) take the view that in learning situations, knowledge needs to be generated, constructed, and practiced, andthat learning with others in collaborative situations works well. (That's not to say that there's not a place for lecture, because there sometimes is,but lecture shouldn't be the only mode.) Learning scientists use what they know from the research in the learning sciences to design goodlearning situations and environments.
What is Deep Learning? There’s a lovely table on page 5 of the book, or at 33% of the sample (if you downloaded it), that discusses deep learning versus traditional classroom practices.
According to the book, deep learning is about making new knowledge interrelated and interconnected into the other knowledge a person already has. It is about helping learners see patterns and understand the underlying similarities, differences, or principles. It is often done in collaboration with others through discussion of a topic. After a discussion, you’ll also have a better understanding of what you know and don’t know (even if you feel as though you have more questions than answers). Discussions allow participants to reflect to see what they understand and that others see things differently. Unfortunately, in many schools, because learners aren’t really exposed to methods that make learning relevant to them, they often focus on grades or certification. So they decide to cram knowledge into their heads just in time for a test.
Sengupta-Irving and Enyedy, N. (2015) show that when teachers and researchers think about learning goals and implement pedagogical strategies that focus on deep learning, learning becomes more relevant and enjoyable (as it should). I’ll share more about their paper in the next post and talk more about specific work learning scientists do.
This post has gotten long, so I’ll end here. In future posts, I plan to provide you with examples from Cyberlearning research projects that have taken technologies and designed new approaches, based on learning science research, to help people learn. In this blog, I want to start a conversation because conversations with diverse groups help advance thinking. Please post comments and questions. I look forward to thinking about learning with you.