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.

Assessing intercultural communicative competence can be challenging for a number of reasons. First, there is little consensus regarding the precise definitions of subcomponents of intercultural competence, making it difficult to identify specific constructs to assess. Second, intercultural competence is locally situated, so the “same behavior or skill may be perceived as competent in one context but not another or one perceiver but not another, and thus no particular skill or ability is likely to ever be universally ‘competent’” (Spitzberg & Changnon, 2009, p. 6). Third, in a related vein, some aspects of intercultural communicative competence might not be suitable for classroom assessment, an issue that is addressed at the end of this chapter. In spite of these challenges, for personal and institutional purposes, assessing learners’ progress in intercultural communicative competence remains an important educational objective. Thus, in this chapter, I review several relevant concepts in L2/Lx assessment, then explore how they relate to understanding intercultural communicative competence, illustrating the theory with three case studies.

This chapter addresses multicultural perspectives of intelligence in the United States. Topics include fairness in testing; environment, social location, and cultural context; measures of intelligence; and outcome implications in testing ethnocultural populations. Definitions of intelligence from a cultural perspective are highlighted. Contextual factors include: poverty, home environment, education, fluency in English, and acculturation. Testing constructs such as fairness in testing, test bias, cultural loading, and various forms of testing equivalence are discussed. Alternative assessment practices focus on nonverbal intelligence tests; dynamic assessment procedures; performance-based, authentic, and curriculum-based assessment; response to intervention, think aloud protocols, cross-battery assessment; and a multidimensional bilingual assessment model. Usage of mainstream intelligence tests is discussed in relation to Black, Asian, American Indian/Native American, and Hispanic and Latino/a communities. The numerous challenges, controversies, and complexities of interpreting test scores in cultural contexts are discussed as intelligence tests are transported, renormed, and restandardized globally.