A descriptive study of the sociodemographic, health and developmental factors associated with slow learning in children from upper to middle social class families in Texas

"Slow Learners" is a term used to describe children with an IQ range of 70-89 on a standardized individual intelligence test (i.e. with a standard deviation of either 15 or 16). They have above retarded, but below average intelligence and potential to learn. If the factors associated with the etiology of slow learning in children can be identified, it may be possible to hypothesize causal relationships which can be tested by intervention studies specifically designed to prevent slow learning. If effective, these may ultimately reduce the incidence of school dropouts and their cost to society. To date, there is little information about variables which may be etiologically significant. In an attempt to identify such etiologic factors this study examines the sociodemographic characteristics, prenatal history (hypertension, smoking, infections, medication, vaginal bleeding, etc.), natal history (length of delivery, Apgar score, birth trauma, resuscitation, etc.), neonatal history (infections, seizures, head trauma, etc.), developmental history (health problems, developmental milestones and growth during infancy and early childhood), and family history (educational level of the parents, occupation, history of similar condition in the family, etc.) of a series of children defined as slow learners. The study is limited to children from middle to high socioeconomic families in order to exclude the possible confounding variable of low socioeconomic status, and because a descriptive study of this group has not been previously reported. ^

Learning What Works: Demonstrating Practice Effectiveness with Children and Families Through Retrospective Investigation

Intensive family preservation services (IFPS), designed to stabilize at-risk families and avert out-of-home care, have been the focus of many randomized, experimental studies. Employing a retrospective “clinical data-mining” (CDM) methodology (Epstein, 2001), this study makes use of available information extracted from client records in one IFPS agency over the course of two years. The primary goal of this descriptive and associational study was to gain a clearer understanding of IFPS service delivery and effectiveness. Interventions provided to families are delineated and assessed for their impact on improved family functioning, their impact on the reduction of family violence, as well as placement prevention. Findings confirm the use of a wide range of services consistent with IFPS program theory. Because the study employs a quasi-experimental, retrospective use of available information, clinical outcomes described cannot be causally attributed to interventions employed as with randomized controlled trials. With regard to service outcomes, findings suggest that family education, empowerment services and advocacy are most influential in placement prevention and in ameliorating unmanageable behaviors in children as well as the incidence of family violence.

"Innovatio educativa tertio millennio": 12 years evoluting from learning from learning to teaching in courses on educational innovation and collaboration with the UNESCO chair of mining and industrial heritage

Twelve years ago a group of teachers began to work in educational innovation. In 2002 we received an award for educational innovation, undergoing several stages. Recently, we have decided to focus on being teachers of educational innovation. We create a web scheduled in Joomla offering various services, among which we emphasize teaching courses of educational innovation. The “Instituto de Ciencias de la Educacion” in “Universidad Politécnica de Madrid” has recently incorporated two of these courses, which has been highly praised. These courses will be reissued in new calls, and we are going to offer them to more Universities. We are in contact with several institutions, radio programs, the UNESCO Chair of Mining and Industrial Heritage, and we are working with them in the creation of heritage courses using methods that we have developed.

Learning Analytics and Interactions in Virtual Learning Environments. A Comparative Study of Typologies and their Relationship with Academic Performance

Analysis of learning data (learning analytics) is a new research field with high growth potential. The main objective of Learning analytics is the analysis of data (interactions being the basic data unit) generated in virtual learning environments, in order to maximize the outcomes of the learning process; however, a consensus has not been reached yet on which interactions must be measured and what is their influence on learning outcomes. This research is grounded on the study of e-learning interaction typologies and their relationship with students? academic performance, by means of a comparative study between different interaction typologies (based on the agents involved, frequency of use and participation mode). The main conclusions are a) that classifications based on agents offer a better explanation of academic performance; and b) that each of the three typologies are able to explain academic performance in terms of some of their components (student-teacher and student-student interactions, evaluating students interactions and active interactions, respectively), with the other components being nonrelevant.

Predicting academic performance with learning analytics in virtual learning environments: A comparative study of three interaction classifications

Learning analytics is the analysis of static and dynamic data extracted from virtual learning environments, in order to understand and optimize the learning process. Generally, this dynamic data is generated by the interactions which take place in the virtual learning environment. At the present time, many implementations for grouping of data have been proposed, but there is no consensus yet on which interactions and groups must be measured and analyzed. There is also no agreement on what is the influence of these interactions, if any, on learning outcomes, academic performance or student success. This study presents three different extant interaction typologies in e-learning and analyzes the relation of their components with students? academic performance. The three different classifications are based on the agents involved in the learning process, the frequency of use and the participation mode, respectively. The main findings from the research are: a) that agent-based classifications offer a better explanation of student academic performance; b) that at least one component in each typology predicts academic performance; and c) that student-teacher and student-student, evaluating students, and active interactions, respectively, have a significant impact on academic performance, while the other interaction types are not significantly related to academic performance.

TIMPANO: Technology for complex Human-Machine conversational interaction with dynamic learning

El proyecto TIMPANO tiene por objetivo profundizar en el desarrollo de sistemas de comunicación oral hombre-máquina atendiendo principalmente a la capacidad de dar respuesta a múltiples requerimientos de los usuarios, como pueden ser el acceso a información, la extracción de información, o el análisis de grandes repositorios de información en audio. En el proyecto se hace especial énfasis en la adaptación dinámica de los modelos a diversos contextos, tanto de tipo acústico, como semántico o de idioma.

Generation of human computational models with machine learning

Services in smart environments pursue to increase the quality of people?s lives. The most important issues when developing this kind of environments is testing and validating such services. These tasks usually imply high costs and annoying or unfeasible real-world testing. In such cases, artificial societies may be used to simulate the smart environment (i.e. physical environment, equipment and humans). With this aim, the CHROMUBE methodology guides test engineers when modeling human beings. Such models reproduce behaviors which are highly similar to the real ones. Originally, these models are based on automata whose transitions are governed by random variables. Automaton?s structure and the probability distribution functions of each random variable are determined by a manual test and error process. In this paper, it is presented an alternative extension of this methodology which avoids the said manual process. It is based on learning human behavior patterns automatically from sensor data by using machine learning techniques. The presented approach has been tested on a real scenario, where this extension has given highly accurate human behavior models,

Enhancing web-based learning resources with quizzes through an Authoring Tool and an Audience Response System

Quizzes are among the most widely used resources in web-based education due to their many benefits. However, educators need suitable authoring tools that can be used to create reusable quizzes and to enhance existing materials with them. On the other hand, if teachers use Audience Response Systems (ARSs) they can get instant feedback from their students and thereby enhance their instruction. This paper presents an online authoring tool for creating reusable quizzes and enhancing existing learning resources with them, and a web-based ARS that enables teachers to launch the created quizzes and get instant feedback from the class. Both the authoring tool and the ARS were evaluated. The evaluation of the authoring tool showed that educators can effectively enhance existing learning resources in an easy way by creating and adding quizzes using that tool. Besides, the different factors that assure the reusability of the created quizzes are also exposed. Finally, the evaluation of the developed ARS showed an excellent acceptance of the system by teachers and students, and also it indicated that teachers found the system easy to set up and use in their classrooms.

Characteristics of patients making serious inhaler errors with a dry powder inhaler and association with asthma-related events in a primary care setting

Acknowledgements The iHARP database was funded by unrestricted grants from Mundipharma International Ltd and Research in Real-Life Ltd; these analyses were funded by an unrestricted grant from Teva Pharmaceuticals. Mundipharma and Teva played no role in study conduct or analysis and did not modify or approve the manuscript. The authors wish to direct a special appreciation to all the participants of the iHARP group who contributed data to this study and to Mundipharma, sponsors of the iHARP group. In addition, we thank Julie von Ziegenweidt for assistance with data extraction and Anna Gilchrist and Valerie L. Ashton, PhD, for editorial assistance. Elizabeth V. Hillyer, DVM, provided editorial and writing support, funded by Research in Real-Life, Ltd.

Effect of reduced myristoylated alanine-rich C kinase substrate expression on hippocampal mossy fiber development and spatial learning in mutant mice: Transgenic rescue and interactions with gene background

The myristoylated alanine-rich C kinase substrate (MARCKS) is a prominent protein kinase C (PKC) substrate in brain that is expressed highly in hippocampal granule cells and their axons, the mossy fibers. Here, we examined hippocampal infrapyramidal mossy fiber (IP-MF) limb length and spatial learning in heterozygous Macs mutant mice that exhibit an ≈50% reduction in MARCKS expression relative to wild-type controls. On a 129B6(N3) background, the Macs mutation produced IP-MF hyperplasia, a significant increase in hippocampal PKCɛ expression, and proficient spatial learning relative to wild-type controls. However, wild-type 129B6(N3) mice exhibited phenotypic characteristics resembling inbred 129Sv mice, including IP-MF hypoplasia relative to inbred C57BL/6J mice and impaired spatial-reversal learning, suggesting a significant contribution of 129Sv background genes to wild-type and possibly mutant phenotypes. Indeed, when these mice were backcrossed with inbred C57BL/6J mice for nine generations to reduce 129Sv background genes, the Macs mutation did not effect IP-MF length or hippocampal PKCɛ expression and impaired spatial learning relative to wild-type controls, which now showed proficient spatial learning. Moreover, in a different strain (B6SJL(N1), the Macs mutation also produced a significant impairment in spatial learning that was reversed by transgenic expression of MARCKS. Collectively, these data indicate that the heterozygous Macs mutation modifies the expression of linked 129Sv gene(s), affecting hippocampal mossy fiber development and spatial learning performance, and that MARCKS plays a significant role in spatial learning processes.

Prenatal stress produces learning deficits associated with an inhibition of neurogenesis in the hippocampus

Early experiences such as prenatal stress significantly influence the development of the brain and the organization of behavior. In particular, prenatal stress impairs memory processes but the mechanism for this effect is not known. Hippocampal granule neurons are generated throughout life and are involved in hippocampal-dependent learning. Here, we report that prenatal stress in rats induced lifespan reduction of neurogenesis in the dentate gyrus and produced impairment in hippocampal-related spatial tasks. Prenatal stress blocked the increase of learning-induced neurogenesis. These data strengthen pathophysiological hypotheses that propose an early neurodevelopmental origin for psychopathological vulnerabilities in aging.

Discovery of genes involved with learning and memory: An experimental synthesis of Hirschian and Benzerian perspectives

The biological bases of learning and memory are being revealed today with a wide array of molecular approaches, most of which entail the analysis of dysfunction produced by gene disruptions. This perspective derives both from early “genetic dissections” of learning in mutant Drosophila by Seymour Benzer and colleagues and from earlier behavior-genetic analyses of learning and in Diptera by Jerry Hirsch and coworkers. Three quantitative-genetic insights derived from these latter studies serve as guiding principles for the former. First, interacting polygenes underlie complex traits. Consequently, learning/memory defects associated with single-gene mutants can be quantified accurately only in equilibrated, heterogeneous genetic backgrounds. Second, complex behavioral responses will be composed of genetically distinct functional components. Thus, genetic dissection of complex traits into specific biobehavioral properties is likely. Finally, disruptions of genes involved with learning/memory are likely to have pleiotropic effects. As a result, task-relevant sensorimotor responses required for normal learning must be assessed carefully to interpret performance in learning/memory experiments. In addition, more specific conclusions will be obtained from reverse-genetic experiments, in which gene disruptions are restricted in time and/or space.