Guiding Principles for a Research Schools Network: Successes and Challenges

Building on J. Dewey's (1907) original work with the laboratory school, the College of Education and Health Professions at the University of Texas–Arlington is expanding the original concept to include partners throughout a school system and the community in order to support and advance learning in multiple learning environments. The goal is to establish a network of schools, administrators, teachers, graduate students, and policymakers; not necessarily everyone, but a critical mass of vested partners working collaboratively in what we call a Research Schools Network (RSN). The Network uses the new field of Mind, Brain and Education (MBE) to develop a theoretical and practical foundation for addressing educational challenges in the community. This article explores how the college has partnered with Arlington Independent School District (AISD), neighboring universities, the business community, and our state representative to develop and define new paradigms that not only promote MBE as an academic field, but also inform and are informed by the community it seeks to serve.     

Methodological Issues in Ethnic and Racial Identity Research With Ethnic Minority Populations: Theoretical Precision,Measurement Issues,and Research Designs

This article takes stock of research methods employed in the study of racial and ethnic identity with ethnic minority populations. The article is presented in three parts. The first section reviews theories, conceptualizations, and measurement of ethnic and racial identity (ERI) development. The second section reviews theories, conceptualizations, and measurement of ERI content. The final section reviews key methodological and analytic principles that are important to consider for both ERI development and content. The article concludes with suggestions for future research addressing key methodological limitations when studying ERI.     

Preparing students for future learning with Teachable Agents

One valuable goal of instructional technologies in K-12 education is to prepare students for future learning. Two classroom studies examined whether Teachable Agents (TA) achieves this goal. TA is an instructional technology that draws on the social metaphor of teaching a computer agent to help students learn. Students teach their agent by creating concept maps. Artificial intelligence enables TA to use the concept maps to answer questions, thereby providing interactivity, a model of thinking, and feedback. Elementary schoolchildren learning science with TA exhibited “added-value” learning that did not adversely affect the “basic-value” they gained from their regular curriculum, despite trade-offs in instructional time. Moreover, TA prepared students to learn new science content from their regular lessons, even when they were no longer using the software.