RESUMEN
The global biodiversity crisis requires an engaged citizenry that provides collective support for public policies and recognizes the consequences of personal consumption decisions. Understanding the factors that affect personal engagement in proenvironmental behaviors is essential for the development of actionable conservation solutions. Zoos and aquariums may be some of the only places where many people can explore their relations with wild animals and proenvironmental behaviors. Using a moderated-mediation analysis of a survey of U.S. zoo and aquarium visitors (n = 3588), we explored the relationship between the sense of connection to animals and self-reported engagement in proenvironmental behaviors related to climate change and how this relationship is affected by certainty that climate change is happening, level of concern about climate change, and perceptions of effectiveness in personally addressing climate change. We found a significant, directional relationship between sense of connection to animals and self-reported proenvironmental behaviors. Political inclination within the conservative to liberal spectrum did not affect the relationship. We conclude that a personal sense of connection to animals may provide a foundation for educational and communication strategies to enhance involvement in proenvironmental actions.
Asunto(s)
Biodiversidad , Cambio Climático , Conservación de los Recursos Naturales , Animales , Animales de Zoológico , Humanos , Opinión Pública , AutoinformeRESUMEN
Learning to read--amazing as it is to small children and their parents--is one thing. Reading to learn, explains Susan Goldman of the University of Illinois at Chicago, is quite another. Are today's students able to use reading and writing to acquire knowledge, solve problems, and make decisions in academic, personal, and professional arenas? Do they have the literacy skills necessary to meet the demands of the twenty-first century? To answer these questions, Goldman describes the increasingly complex comprehension, reasoning skills, and knowledge that students need as they progress through school and surveys what researchers and educators know about how to teach those skills. Successfully reading to learn requires the ability to analyze, synthesize, and evaluate information from multiple sources, Goldman writes. Effective readers must be able to apply different knowledge, reading, and reasoning processes to different types of content, from fiction to history and science, to news accounts and user manuals. They must assess sources of information for relevance, reliability, impartiality, and completeness. And they must connect information across multiple sources. In short, successful readers must not only use general reading skills but also pay close attention to discipline-specific processes. Goldman reviews the evidence on three different instructional approaches to reading to learn: general comprehension strategies, classroom discussion, and disciplinary content instruction. She argues that building the literacy skills necessary for U.S. students to read comprehensively and critically and to learn content in a variety of disciplines should be a primary responsibility for all of the nation's teachers. But outside of English, few subject-area teachers are aware of the need to teach subject-area reading comprehension skills, nor have they had opportunities to learn them themselves. Building the capacity of all teachers to meet the literacy needs of today's students requires long-term investment and commitment from the education community as well as society as a whole.
Asunto(s)
Comprensión , Alfabetización Informacional , Aprendizaje , Lectura , Enseñanza/normas , Adolescente , Humanos , Enseñanza/métodosRESUMEN
The present study explored different approaches for automatically scoring student essays that were written on the basis of multiple texts. Specifically, these approaches were developed to classify whether or not important elements of the texts were present in the essays. The first was a simple pattern-matching approach called "multi-word" that allowed for flexible matching of words and phrases in the sentences. The second technique was latent semantic analysis (LSA), which was used to compare student sentences to original source sentences using its high-dimensional vector-based representation. Finally, the third was a machine-learning technique, support vector machines, which learned a classification scheme from the corpus. The results of the study suggested that the LSA-based system was superior for detecting the presence of explicit content from the texts, but the multi-word pattern-matching approach was better for detecting inferences outside or across texts. These results suggest that the best approach for analyzing essays of this nature should draw upon multiple natural language processing approaches.
Asunto(s)
Inteligencia Artificial , Instrucción por Computador/métodos , Evaluación Educacional/métodos , Procesamiento de Lenguaje Natural , Publicaciones Periódicas como Asunto , Semántica , Máquina de Vectores de Soporte , Escritura , Sistemas Especialistas , Humanos , Reconocimiento de Normas Patrones Automatizadas , Programas InformáticosRESUMEN
Latent semantic analysis (LSA) is a computational model of human knowledge representation that approximates semantic relatedness judgments. Two issues are discussed that researchers must attend to when evaluating the utility of LSA for predicting psychological phenomena. First, the role of semantic relatedness in the psychological process of interest must be understood. LSA indices of similarity should then be derived from this theoretical understanding. Second, the knowledge base (semantic space) from which similarity indices are generated must contain 'knowledge' that is appropriate to the task at hand. Proposed solutions are illustrated with data from an experiment in which LSA-based indices were generated from theoretical analysis of the processes involved in understanding two conflicting accounts of a historical event. These indices predict the complexity of subsequent student reasoning about the event, as well as hand-coded predictions generated from think-aloud protocols collected when students were reading the accounts of the event.