EDUCATIONAL DATA MINING AND ITS USES TO PREDICT THE MOST PROSPEROUS LEARNING ENVIRONMENT

The use of technology and data analysis within the classroom has been a resourceful tool in order to collect, study, and compare a student's level of success. With the large amount of regularly collected data from student behaviors, and course structure there is more than enough resources in order to find student success with data analysis. A method of data analysis within a learning environment is called Educational Data Mining (EDM), which has proven to be an emerging trend when it involves the development of exploration techniques and the analysis of educational data. EDM has been able to contribute to the understanding of student behavior, as well as factors that influence both student actions and their success. The study of student success within EDM has focused on student learning patterns, student to teacher culture, and teaching techniques. In this research we will look at uses of technology and data mining in an EDM setting and compare the success of findings. Using past experience of other research we will determine which method would be best in order to look at a learning environment, and try to find which factors will affect a student's academic performance

Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine

EDUCATIONAL DATA MINING AND ITS USES TO PREDICT THE MOST PROSPEROUS LEARNING ENVIRONMENT

The use of technology and data analysis within the classroom has been a resourceful tool in order to collect , study , and compare a student's level of success. With the large amount of regularly collected data from student behaviors , and course structure there is more than enough resources in order to find student success with data analysis. A method of data analysis within a learning environment is called Educational Data Mining (EDM) , which has proven to be an emerging trend when it involves the development of exploration techniques and the analysis of educational data. EDM has been able to contribute to the understanding of student behavior , as well as factors that influence both student actions and their success. The study of student success within EDM has focused on student learning patterns , student to teacher culture , and teaching techniques. In this research we will look at uses of technology and data mining in an EDM setting and compare the success of findings. Using past experience of other research we will determine which method would be best in order to look at a learning environment , and try to find which factors will affect a student's academic performance

sasview: SasView 4.0 Beta 1

Beta Release This release is a beta, meaning it is intended as a relatively stable test release prior to the final 4.0 production release. There are many improvements over the last production release (3.1.2) as detailed in the previous alpha release notes. The main improvement is the restructuring of the models interface meaning that user supplied models are written in the same way as core models and can be added without rebuilding SasView. User models can also take advantage of polydispersity and GPU acceleration in the same way as core models. Bug Reporting Please support the project by reporting bugs that you find to [email protected] All the known bugs/feature requests can be found at: http://trac.sasview.org/report/3 Regular developer builds are also available from https://jenkins.esss.dk/sasview/ if you wish to test the very latest (most likely unstable) versions of SasView. New Features This beta release adds support for the magnetic and multilevel models of 3.1.2 along with a host of bug fixes found in the alpha. Model package changes and improvements All 3.1.2 models now available in new interface Old custom models should now still work '''NOTE:''' These will be deprecated in a future version. Old custom models should be converted to the new model format which is now the same as the built in models and offers much better support. Custom model editor now creates new style models Custom model editor supports better error checking Documentation improvements Continued general cleanup Other improvements/additions Support for new canSAS 2D data files added Plot axes range can now be set manually as well as by zooming Plot annotations can now be moved around after being placed on plot. The active optimizer is now listed on the top of the fit panel. Linear fits now update qmin and max when the x scale limits are changed. Also the plot range no longer resets after a fit. Bug fixes Fixes bug #511 Errors in linearized fits and clean up of interface including Kratky representation Fixes bug #186 Data operation Tool now executes when something is entered in the text box and does not wait for the user to hit enter Fixes bug #459 plot context menu bug Fixes bug #559 copy to clipboard in graph menu broken Fixes bug #466 cannot remove a linear fit from graph Numerous bugs introduced in the alph

Sensual, material, and technological understanding: exploring prehistoric soundscapes in south India

Recent years have witnessed an increased interest within archaeology in the non-visual senses, and particularly sound. To date, however, most studies have focused on the evidence for musical instruments and the acoustic properties of special structures and spaces, like monuments and caves. This study reports on further evidence for special musical activities at the prehistoric site of Sanganakallu-Kupgal in south India, but then also moves on to a discussion of the acoustic dimension of more mundane Neolithic technological and productive activities, like flint-knapping, axe-grinding, and crop production. It focuses on the evidence for links between such activities at Sanganakallu-Kupgal, based on shared material, gestural, and acoustic properties, and argues that the hammering of ringing rocks to make music was only one aspect of a wider Southern Neolithic cultural propensity to address technological and ritual requirements by applying stone against stone. The article attempts to bring to recent discussions of the senses an awareness of the materiality of sensory experience, which, despite recent interest in the body, remains marginalized in theoretical accounts