Foretelling the future is impossible. But measured predictions, from smart and informed people, using good data, hard work and solid evidence, can be possible and reliable. A good example is Robert S. Feldman‘s Learning Science: Theory, Research, & Practice. Making claims about higher education and technology is inherently risky (Did anyone anticipate Zoom? Remember the buzz over MOOCs?). The book, a collection of works from a wide range of experts, is an informed look into our future. And if you aren’t up on the advances in learning science, you will be surprised about what is happening and what is already with us.
Feldman is a knowledgeable guide and editor. A long-time professor of psychology at the University of Massachusetts Amherst, Feldman has also served as a Dean and Deputy Chancellor. Currently a Senior Advisor to the Chancellor at University of Massachusetts, he is prodigious scholar and an internationally acknowledged leader in behavior and brain science research. The chapters he has assembled in this volume fall into three broad categories: what learning science is about; what is new and exciting in learning science; and applications of learning science to practice.
Learning science, Feldman informs us, is a cross-disciplinary field of inquiry into the science behind teaching, learning and educational practice. It draws on the strengths of psychology, cognitive science, sociology, anthropology, and data science. Advances in technology are speeding advances in learning science. Bigger data sets and more effective software spells more opportunity for advancement – in theory and in application. Feldman cautions us, though, that learning science is not a unified discipline. Things are messy and boundaries and even questions around basic language are still being hashed out.
On the other hand, Feldman is optimistic about the future of learning science and the quality of the work in the chapters bears this out. While learning science may be STEM focused now, it holds tremendous promise across disciplines and in terms of social policy. Advances in research are leading to more research. Feldman believes that we are at “the cusp of a new era in education.” He very well may be right.
The twelve chapters in the book range from studies of undergraduate to postsecondary education, from exploration of basic principles in learning science to complex analysis of different approaches and technologies. The scope of inquiry is vast and the acronyms can be bewildering. Learning science is a field that can quickly become difficult to comprehend to the non-expert. Chapter Four’s title, by way of example, is “Semantic Representation and Analysis (SRA) and its Application in Conversation-Based Intelligent Tutoring Systems (CbITS).” Read slowly and deliberately, though, and it makes sense and offers guidance for the development of new tutoring systems. And if you’re planning to develop an automatic tutoring system that responds to language, make sure that there’s a a clear domain of knowledge and that your system stays within it.
What I found most appealing about the book, above and beyond the simply fascinating work that is taking place in learning science, was the mixture of theoretical and applied. My limited reading into the history of technology has impressed upon me the importance of problem definition. How a problem is defined and in what context matters greatly when it comes to technology and technological change. The greater clarity surrounding the challenge, usually the more readily we can understand and appreciate the pathways of innovation.
Learning science can appear, at times, like a range of complicated solutions in search of a problem. By foregrounding questions of domain and theory, Feldman gives the reader a broader range of lenses to make sense of the rest of the work. We may not have an exact map to the future, but we do have some promising paths being blazed. Learning Science is worth your time and consideration.