Faring with Facets: Building and Using Databases of Student Misconceptions

A number of educational researchers have developed pedagogical approaches that involve the teacher in discovering and helping to correct misconceptions that students bring to their study of their subject matter. During the last decade, several computer systems have been developed to support teaching and learning using this kind of approach. A central conceptual construct used by these systems is the "facet" of understanding: an atomic diagnosable unit of belief. A formidable challenge to applying such pedagogical approaches to new topic areas is the task of discovering and organizing the facets for the new subject area. This paper presents a taxonomy of misconceptions and a methodology for going about the task of preparing a database of facets. Important issues include the generality and diagnosability of facets, granularity of facets, and their placement on a scale of problematicity. Examples are drawn from the subjects of physics and computer science and in the context of two computer systems: the Diagnoser and INFACT.Editors: Patrick McAndrew (Open University, UK).Reviewers: Paul Horwitz (Concord Consortium, USA) and Ruth Thomas (Jelsim Partnership, UK)

Empirical comparison of diffusion kurtosis imaging and diffusion basis spectrum imaging using the same acquisition in healthy young adults

As diffusion tensor imaging gains widespread use, many researchers have been motivated to go beyond the tensor model and fit more complex diffusion models, to gain a more complete description of white matter microstructure and associated pathology. Two such models are diffusion kurtosis imaging (DKI) and diffusion basis spectrum imaging (DBSI). It is not clear which DKI parameters are most closely related to DBSI parameters, so in the interest of enabling comparisons between DKI and DBSI studies, we conducted an empirical survey of the interrelation of these models in 12 healthy volunteers using the same diffusion acquisition. We found that mean kurtosis is positively associated with the DBSI fiber ratio and negatively associated with the hindered ratio. This was primarily driven by the radial component of kurtosis. The axial component of kurtosis was strongly and specifically correlated with the restricted ratio. The joint spatial distributions of DBSI and DKI parameters are tissue-dependent and stable across healthy individuals. Our contribution is a better understanding of the biological interpretability of the parameters generated by the two models in healthy individuals

Neuroimaging workflows in the cloud

Analysis of large neuroimaging datasets requires scalable computing power and storage, plus methods for secure collaboration and for reproducibility. The application of cloud computing can address many of these requirements, providing a very flexible model that is generally far less expensive than a lab trying to purchase the most computer equipment they would ever need. This chapter describes how researchers can change the way that they traditionally run neuroimaging workflows in order to leverage cloud-computing capabilities. It describes various considerations and options related to cloud-based neuroimaging analyses, including cost models and architectures. Next, using data from the AOMIC-PIOP2 project hosted on OpenNEURO, it shows how to use NextFlow to create a very simple skull stripping and tissue segmentation workflow using FSL’s bet and fast programs installed on a local computer. Nextflow allows scalability from a laptop to a cluster to cloud-native services with no code changes

1.2 Neuroimaging workflows in the cloud

https://osf.io/7synk Analysis of large neuroimaging datasets requires scalable computing power and storage, plus methods for secure collaboration and for reproducibility. The application of cloud computing can address many of these requirements, providing a very flexible model that is generally far less expensive than a lab trying to purchase the most computer equipment they would ever need. This chapter describes how researchers can change the way that they traditionally run neuroimaging workflows in order to leverage cloud-computing capabilities. It describes various considerations and options related to cloud-based neuroimaging analyses, including cost models and architectures. Next, using data from the AOMIC-PIOP2 project hosted on OpenNEURO, it shows how to use NextFlow to create a very simple skull stripping and tissue segmentation workflow using FSL’s bet and fast programs installed on a local computer. Nextflow allows scalability from a laptop to a cluster to cloud-native services with no code changes

Automatic Classification of Input/output Access Patterns

153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997.We have implemented this classification framework as extensions to the Portable Parallel File System (PPFS) testbed. Experimental results on sequential and parallel scientific applications demonstrate the utility of this approach.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Porsonify: A Portable System for Data Sonification

This document can be read in two ways: as a guide to using the current software, or as a reference manual for those wishing to extend the system to support new features. If you have not yet built and installed the system, before reading further you will probably want to consult Appendix C for installation instructions. The system software (e.g., g++, X11R5/Motif) necessary to build and run Porsonify is described in Appendix B. Throughout this text, the following font conventions are used

A Portable System For Data Sonification

omplaints. My friend David Zimmerman gets credit for the part of East Coast Voice, although it is unlikely to earn him his big break. And finally, I thank my family for their constant love and support. And in particular, my parents, for believing in me unconditionally. This work was funded in part by National Science Foundation grants NSF CCR86--57696, NSF CCR87--06653 and NSF CDA87--22836 (Tapestry), NASA ICLASS Contract No. NAG-- 1--613, DARPA Contract No. DABT63-91-K-0004, and by grants from the Digital Equipment Corporation External Research Program and Apple Computer. iii TABLE OF CONTENTS Chapter 1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 1.1 Background : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 1.1.1 Previous Work : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 1.2 Sound Synthesis Approaches : : :

Exploiting Global Input/Output Access Pattern Classification

Parallel input/output systems attempt to alleviate the performance bottleneck that affects many input/output intensive applications. In such systems, an understanding of the application access pattern, especially how requests from multiple processors for different file regions are logically related, is important for optimizing file system performance. We propose a method for automatically classifying these global access patterns and using these global classifications to select and tune file system policies to improve input/output performance. We demonstrate this approach on benchmarks and scientific applications using global classification to automatically select appropriate underlying Intel PFS input/output modes and server buffering strategies. 1 Introduction Despite continued innovations in disk design, input/output performance has not kept pace with concurrent increases in processor speeds. File systems that utilize parallel disks to improve system throughput offer some hope of al..