The Effect of Direct Instruction versus Discovery Learning on the Understanding of Science Lessons by Second Grade Students

This study examined the effectiveness of discovery learning and direct instruction in a diverse second grade classroom. An assessment test and transfer task were given to students to examine which method of instruction enabled the students to grasp the content of a science lesson to a greater extent. Results demonstrated that students in the direct instruction group scored higher on the assessment test and completed the transfer task at a faster pace; however, this was not statistically significant. Results also suggest that a mixture of instructional styles would serve to effectively disseminate information, as well as motivate students to learn.

Knowledge Presentation and Reasoning with Loglinear Models

Our approach for knowledge presentation is based on the idea of expert system shell. At first we will build a graph shell of both possible dependencies and possible actions. Then, reasoning by means of Loglinear models, we will activate some nodes and some directed links. In this way a Bayesian network and networks presenting loglinear models are generated.

Authentizität und Lernen - das Fach in der Fachdidaktik. Gesellschaft für Didaktik der Chemie und Physik Jahrestagung in Berlin 2015

Die 42. Jahrestagung der Gesellschaft für Didaktik der Chemie und Physik (GDCP) wurde im September 2015 an der Humboldt-Universität zu Berlin ausgerichtet. Zum Tagungsthema „Authentizität und Lernen - Das Fach in der Fachdidaktik“ diskutierten neben den Plenarreferentinnen und -referenten eine große Anzahl an Tagungsgästen. Der vorliegende Band umfasst die ausgearbeiteten Beiträge der Teilnehmerinnen und Teilnehmer. (DIPF/Orig.)

Entdeckendes Lernen als didaktisches Konzept in einem interdisziplinären Lehr-Lernprogramm zur Statistik

Der Methodenlehre-Baukasten1 ist ein interaktives Lehrprogramm für Methodenlehre und Statistik, das versucht, mithilfe didaktischer Interventionen die Problematik des Phänomens „Statistikangst“, das unter Studierenden der Geistes- und Sozialwissenschaften identifiziert wurde, entgegenzuwirken. Auf dem Konzept des Entdeckenden Lernens basierend, bietet das Lernprogramm den Studierenden die Möglichkeit, anhand realer Forschungsdaten und fachspezifischer Zugänge zu aktuellen wissenschaftlichen Fragestellungen, ihr Verständnis der Statistik und Methodenlehre ausgehend von ihren naiven Konzepten in kleinen kognitiven Schritten hin zu einem wissenschaftlichen Verständnis zu erweitern. Der vorliegende Beitrag stellt zunächst kurz die Struktur des Lernprogramms dar, um im Folgenden auf die didaktischen Konzepte einzugehen, mit denen diesen motivationalen Problemen begegnet wird. Das Beispiel einer Übung konkretisiert die Anwendung des didaktischen Konzepts im Methodenlehre-Baukasten.(DIPF/Orig.)

Beiträge zum Mathematikunterricht 2015. Vorträge auf der 49. Tagung für Didaktik der Mathematik vom 09.02.2015 bis 13.02.2015 in Basel [Band 1]

Die Jahrestagung der Gesellschaft für Didaktik der Mathematik fand im Jahr 2015 zum dritten Mal in der Schweiz statt. [...] Mit rund 300 Vorträgen, 16 moderierten Sektionen, 15 Arbeitskreistreffen und 21 Posterpräsentationen eröffnete sich ein breites Spektrum an Themen und unterschiedlichen Zugangsweisen zur Erforschung von Fragen rund um das Lernen und Lehren von Mathematik. (DIPF/Orig.)

La enseñanza tradicional de las ciencias versus las nuevas tendencias educativas

To think of an educational proposal that teaches how to learn, it is necessary to consider a change not only educationally but also political, social, economical, ecological, cultural, among others, to enable an understanding of reality and in which there can be a construction of knowledge and a crucial role of sciences. But we must not forget that the development of science has been marked by the so-called positivistic science that it is characterized by interpreting phenomena and how this function through theories and laws, where the context and humans have a very poor leading role, if any, to which one can call scientism, which has allowed development even above human needs. However, since the 90s, there is a resurgence of progressive humanism in the educational fields, where there is a search of a revaluation of what it is considered human, which involves a series of epistemological and methodological changes that drives us towards new ways of working. This calls us to reflect on extreme choices to build knowledge, beyond the traditional teaching of the sciences, which are comprehensive, systematic, and flexible and rooted in a humanistic culture. Some models of the new trends are: directed research, discovery learning, inquiry learning and teaching of science and new technologies.

Discovery-Based Learning - Daniel Rowan

This is one of a series of short case studies describing how academic tutors at the University of Southampton have made use of learning technologies to support their students.

Radiation metabolomics. 3. Biomarker discovery in the urine of gamma-irradiated rats using a simplified metabolomics protocol of gas chromatography-mass spectrometry combined with random forests machine learning algorithm

Abstract Radiation metabolomics employing mass spectral technologies represents a plausible means of high-throughput minimally invasive radiation biodosimetry. A simplified metabolomics protocol is described that employs ubiquitous gas chromatography-mass spectrometry and open source software including random forests machine learning algorithm to uncover latent biomarkers of 3 Gy gamma radiation in rats. Urine was collected from six male Wistar rats and six sham-irradiated controls for 7 days, 4 prior to irradiation and 3 after irradiation. Water and food consumption, urine volume, body weight, and sodium, potassium, calcium, chloride, phosphate and urea excretion showed major effects from exposure to gamma radiation. The metabolomics protocol uncovered several urinary metabolites that were significantly up-regulated (glyoxylate, threonate, thymine, uracil, p-cresol) and down-regulated (citrate, 2-oxoglutarate, adipate, pimelate, suberate, azelaate) as a result of radiation exposure. Thymine and uracil were shown to derive largely from thymidine and 2'-deoxyuridine, which are known radiation biomarkers in the mouse. The radiation metabolomic phenotype in rats appeared to derive from oxidative stress and effects on kidney function. Gas chromatography-mass spectrometry is a promising platform on which to develop the field of radiation metabolomics further and to assist in the design of instrumentation for use in detecting biological consequences of environmental radiation release.

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.

Communities and emerging semantics in semantic link network:discovery and learning

The World Wide Web provides plentiful contents for Web-based learning, but its hyperlink-based architecture connects Web resources for browsing freely rather than for effective learning. To support effective learning, an e-learning system should be able to discover and make use of the semantic communities and the emerging semantic relations in a dynamic complex network of learning resources. Previous graph-based community discovery approaches are limited in ability to discover semantic communities. This paper first suggests the Semantic Link Network (SLN), a loosely coupled semantic data model that can semantically link resources and derive out implicit semantic links according to a set of relational reasoning rules. By studying the intrinsic relationship between semantic communities and the semantic space of SLN, approaches to discovering reasoning-constraint, rule-constraint, and classification-constraint semantic communities are proposed. Further, the approaches, principles, and strategies for discovering emerging semantics in dynamic SLNs are studied. The basic laws of the semantic link network motion are revealed for the first time. An e-learning environment incorporating the proposed approaches, principles, and strategies to support effective discovery and learning is suggested.

Automated Discovery of Social Networks in Online Learning Communities

As a way to gain greater insights into the operation of online communities, this dissertation applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks among online community members. The main thrust of the study is to automate the discovery of social ties that form between community members, using only the digital footprints left behind in their online forum postings. Currently, one of the most common but time consuming methods for discovering social ties between people is to ask questions about their perceived social ties. However, such a survey is difficult to collect due to the high investment in time associated with data collection and the sensitive nature of the types of questions that may be asked. To overcome these limitations, the dissertation presents a new, content-based method for automated discovery of social networks from threaded discussions, referred to as ‘name network’. As a case study, the proposed automated method is evaluated in the context of online learning communities. The results suggest that the proposed ‘name network’ method for collecting social network data is a viable alternative to costly and time-consuming collection of users’ data using surveys. The study also demonstrates how social networks produced by the ‘name network’ method can be used to study online classes and to look for evidence of collaborative learning in online learning communities. For example, educators can use name networks as a real time diagnostic tool to identify students who might need additional help or students who may provide such help to others. Future research will evaluate the usefulness of the ‘name network’ method in other types of online communities.

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.