人是如何学习的--兼谈技术在学习中的作用

在去年春天的教育大会中,我参加了一次有关教学技术在转变学校的教与学中的潜力的讨论。其中有一个发言人讲述了发生在他14岁儿子身上的故事。儿子跟他一样,对教育技术的新鲜玩艺非常钟情,并总是希望拥有即将在市面上流行的最新和最先进的技术产品。有一天,儿子将一个电视机遥控     

Supplemental reading strategy instruction for adolescents: A randomized trial and follow-up study

In this study, the authors examine the impact of a yearlong supplemental reading course involving daily instruction in the learning strategies curriculum on lower achieving adolescent students' reading achievement and motivation. Using a multiple-cohort randomized treatment-control group design over 4 years, they compared achievement and motivation outcomes for 605 sixth-grade students who participated in the intervention with 530 students who did not participate and 593 ninth-grade students who participated in the intervention with 535 students who did not participate. Results indicated significant impacts of the intervention on reading achievement for ninth-grade students but not for sixth-grade students. Significant impacts on reading motivation were found for both sixth- and ninth-grade students. An exploratory follow-up study indicated potential benefits of a second year of intervention for sixth-grade students who are still low achieving after 1 year of intervention.     

MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus

MplusAutomation is a package for R that facilitates complex latent variable analyses in Mplus involving comparisons among many models and parameters. More specifically, MplusAutomation provides tools to accomplish 3 objectives: to create and manage Mplus syntax for groups of related models; to automate the estimation of many models; and to extract, aggregate, and compare fit statistics, parameter estimates, and ancillary model outputs. We provide an introduction to the package using applied examples including a large-scale simulation study. By reducing the effort required for large-scale studies, a broad goal of MplusAutomation is to support methodological developments in structural equation modeling using Mplus.     

Income Level, Gender, Ethnicity, and Household Composition as Predictors of Children''s School-based Competence

In the United States, being black, male, or growing up in a low-income and/or single-parent household have all been identified as risk factors for maladjustment during childhood. Interpretation of these findings is, however, often difficult because of the well-known associations among these variables. In the present study, we compared predictions of 3 different forms of children's competence from each of these 4 variables. In a sample of 868 black and white elementary school children from 2-parent and mother-headed 1-parent homes, we studied 3 aspects of school-based competence: conduct, peer relations, and academic achievement. Results showed that although the independent variables accounted for different amounts of variance in each domain of competence, income level and gender were better overall predictors of children's competence in conduct and peer relations than were ethnicity or household composition. Income level and ethnicity were better overall predictors of academic achievement than were gender or household composition, although each of the 4 variables made a significant contribution. Overall, income level and gender were thus the strongest predictors of children's competence. Black children were, however, more likely than white children to live in low-income homes. Our results thus highlighted some correlates of the unequal distribution of economic resources among black and white children growing up in the United States today.     

Note on Sources of Sampling Variability in Science Performance Assessments

In 1993, we reported in Journal of Educational Measurement that task-sampling variability was the Achilles' heel of science performance assessment. To reduce measurement error, tasks needed to be stratified before sampling, sampled in large number, or possibly both. However, Cronbach, Linn, Brennan, & Haertel (1997) pointed out that a task-sampling interpretation of a large person x task variance component might be incorrect. Task and occasion sampling are confounded because tasks are typically given on only a single occasion. The person x task source of measurement error is then confounded with the pt x occasion source. If pto variability accounts for a substantial part of the commonly observed pt interaction, stratifying tasks into homogenous subsets—a cost-effective way of addressing task sampling variability—might not increase accuracy. Stratification would not address the pro source of error. Another conclusion reported in JEM was that only direct observation (DO) and notebook (NB) methods of collecting performance assessment data were exchangeable; computer simulation, short-answer, and multiple-choice methods were not. However, if Cronbach et al. were right, our exchangeability conclusion might be incorrect. After re-examining and re-analyzing data, we found support for Conbach et al. We concluded that large task-sampling variability was due to both the person x task interaction and person x task x occasion interaction. Moreover, we found that direct observation, notebook and computer simulation methods were equally exchangeable, but their exchangeability was limited by the volatility of student performances across tasks and occasions.     

Researcher Development of Doctoral Students: Supports and Barriers Across Time

We used content analysis to analyze 42 counseling doctoral students' reports of supports and barriers influencing their researcher development across 3 years. In line with social cognitive career theory, the students named cognitive-person, environmental, and social-context influences. Analyses indicated no differences in frequencies of supports and barriers across time but significant differences by cohort.     

The social and emotional world of gifted students: Moving beyond the label

Students who are identified as gifted have academic, social, and emotional experiences that are informed by both their cognition and their identification. Similarly, highly able students who are overlooked and remain unidentified have extra layers of social and emotional complexity to negotiate. While the label should not uniquely define the treatment of the student, this review of the literature offers some supported themes that might inform service. Research on traditional and contemporary concerns is placed in the context of conceptual and methodological limitations, concluding with recommendations for the practitioner as a beginning to the conversation.     

Associations between blocking,monitoring, and filtering software on the home computer and youth-reported unwanted exposure to sexual material online

ObjectiveTo examine the relationship between the use of preventive software on the home computer and unwanted exposure to sexual material online.MethodsThe Youth Internet Safety Survey-2 was a national, RDD telephone survey conducted in March–June 2005. Eight hundred households (one caregiver and one child between the ages of 10 and 17 years) with home Internet access answered questions pertaining to Internet prevention activities in the household and adolescent behaviors and exposures online.ResultsUnwanted exposure to sexual material occurred in 32% of youth in homes with pop-up/spam blockers and 25% of youth in homes with filtering, blocking, or monitoring software on the home computer, compared to 43% of households without preventive software installed on the home computer. Among otherwise similar youth, pop-up/spam blockers installed on the home computer were significantly associated with 59% lower odds of reporting unwanted exposure to sexual material on the home computer; and filtering, blocking, or monitoring software was significantly associated with 65% lower odds. When data were stratified by youth sex, associations between preventive software and unwanted exposure were similar for boys and girls. When stratified by age, preventive software was associated with significantly reduced risk of unwanted exposure for 10–12-year olds and 13–15-year olds, but not for 16–17-year olds.ConclusionAlthough these correlational analyses are far from providing conclusive evidence that preventive software protects children from unwanted exposure to sexual material online, findings suggest that caregivers of boys and girls 15 years of age and younger who want to reduce the likelihood of unwanted exposure to sexual material on the home computer should consider including preventive software—especially filtering, blocking, or monitoring software—in their Internet safety plan.Practice implicationsPractitioners should partner with caregivers in developing an Internet safety plan, including proactive caregiver-youth discussions about expected Internet behavior appropriate for their household.