To the extent that we can make education a science, we will gain some power to predict future directions for educational improvements. This chapter begins with quotations from some famous people that indicate that in the past, we have not learned from our mistakes. If we can succeed in creating a viable science of education and apply this in all educational settings, we may change the course of history in a positive way. This chapter presents a critique of some of the things we have done, and a description of more promising alternatives.

The chapter begins with a description of the first chance experience that shaped the future of my career, a meeting with a former Cornell PhD student, Bruce Dunn, who was interested in collaborating on research and invited me to do a sabbatical leave at the University of West Florida in 1987-1988. This in turn led to conversation with Dunn’s friend, Kenneth Ford, a new faculty member interested in artificial intelligence. We found that the use of concept mapping was highly facilitated for capturing expert knowledge in a fashion that rendered the knowledge easily applied in artificial intelligence settings. Ford became the director of the Institute for Human and Machine Cognition (IHMC) and he invited his friend, Alberto Cañas, to serve as associate director and to lead a team to create computer software for making concept maps electronically. We soon had available to us software that would work on almost any computer and that would not only allow the construction of concept maps, but also permit attaching digital resources to any map that could be accessed by simply clicking on icons on individual concepts. The software suite created became known as CmapTools, and this software suite is now used all over the world in virtually every field where organized knowledge is important.

In part to illustrate the slow progress in secondary school facilities and programs, I introduce findings from a study done some 50 years ago. Most of the positive changes that occurred in the last 100 years are the result of an occasional creative administrator or school leader. To the best of my knowledge, none of these innovations were introduced on the basis of a comprehensive theory of education. I present evidence to suggest that this situation is changing.

The chapter begins by addressing the question: Why do young children learn so quickly? The short answer is that they are learning names for objects and events they are experiencing directly. These words are concept labels and they are engaged in what we call meaningful learning. In contrast, school learning is too often rote learning where the concepts and principles children are learning are not related to direct experiences with objects and events. David Ausubel’s cognitive psychology was introduced in 1963 and we immediately applied this new psychology as the foundation for all of our future work. We rejected totally the behavioral psychology that had dominated the field of education for some one hundred years. We also rejected positivist epistemology in favor of the emerging constructivist epistemology. It was not until the late 1980s that cognitive psychology and constructivist epistemology became widely adopted.

This chapter opens with the question: Can education become a science? I seek to answer to answer this question by asserting that education is a human activity and like any other human activity, it can be studied scientifically. This means that we can construct concepts, principles, and theories that explain how human beings acquire, use, and construct new knowledge. A comprehensive theory of education must address the question of the nature of knowledge and how human beings build new knowledge, and how to organize education to facilitate these processes. I argue that the major problem with education in the past has been the use of faulty theories of learning and invalid theories of knowledge and knowledge creation, resulting in inadequate instructional practices.

Understanding individuals’ interest, motivation, and engagement is essential to designing for meaningful learning. We typically think of engaged learners as those who have a more developed interest in content (e.g., math, robotics, swimming) and are motivated to learn. But learners who are not engaged or who are unmotivated can also be assisted to meaningfully engage with content in ways that lead to deep learning. This chapter summarizes research on two questions for how to design for meaningful learning: What supports unmotivated individuals to become motivated to learn? How do we design tasks that enable those who are already engaged to continue to deepen their interest? The chapter summarizes five research studies that provide converging evidence that designing for meaningful learning requires (1) addressing the differences in learners’ interest, motivation, and engagement; (2) supporting learners in engaging in thinking about content with others. Learning environments can be designed to enable all learners, regardless of their initial engagement with material, to develop meaningful connections to content, thus optimizing their learning.

Multimedia learning is learning from words and pictures. The rationale for studying multimedia learning is that people can learn more deeply from words and pictures than from words alone. A goal of research on multimedia learning is to understand how to design multimedia learning environments that promote meaningful learning. The research base concerning multimedia learning is reflected in the 46 chapters of this Handbook, and includes 30 design principles that we have organized into three categories: principles based on reducing extraneous processing, principles based on managing essential processing, and principles based on fostering generative processing.