Principal Component Analysis of Molecular Clouds: Can CO reveal the dynamics?

We use Principal Component Analysis (PCA) to study the gas dynamics in numerical simulations of typical MCs. Our simulations account for the non-isothermal nature of the gas and include a simplified treatment of the time-dependent gas chemistry. We model the CO line emission in a post-processing step using a 3D radiative transfer code. We consider mean number densities n_0 = 30, 100, 300 cm^{-3} that span the range of values typical for MCs in the solar neighbourhood and investigate the slope \alpha_{PCA} of the pseudo structure function computed by PCA for several components: the total density, H2 density, 12CO density, 12CO J = 1 -> 0 intensity and 13CO J = 1 -> 0 intensity. We estimate power-law indices \alpha_{PCA} for different chemical species that range from 0.5 to 0.9, in good agreement with observations, and demonstrate that optical depth effects can influence the PCA. We show that when the PCA succeeds, the combination of chemical inhomogeneity and radiative transfer effects can influence the observed PCA slopes by as much as ~ +/- 0.1. The method can fail if the CO distribution is very intermittent, e.g. in low-density clouds where CO is confined to small fragments.Comment: 12 pages, 8 figures, accepted for publication in MNRA

The Simple Publishing Interface (SPI)

Ternier, S., Massart, D., Totschnig, M., Klerkx, J., & Duval, E. (2010). The Simple Publishing Interface (SPI). D-Lib Magazine, September/October 2010, Volume 16 Number 9/10, doi:10.1045/september2010-ternierThe Simple Publishing Interface (SPI) is a new publishing protocol, developed under the auspices of the European Committee for Standardization (CEN) workshop on learning technologies. This protocol aims to facilitate the communication between content producing tools and repositories that persistently manage learning resources and metadata. The SPI work focuses on two problems: (1) facilitating the metadata and resource publication process (publication in this context refers to the ability to ingest metadata and resources); and (2) enabling interoperability between various components in a federation of repositories. This article discusses the different contexts where a protocol for publishing resources is relevant. SPI contains an abstract domain model and presents several methods that a repository can support. An Atom Publishing Protocol binding is proposed that allows for implementing SPI with a concrete technology and enables interoperability between applications.European Committee for Standardization (CEN), CEN/Expert/2009/3

Dataset-driven research for improving recommender systems for learning

Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., & Duval, E. (2011). Dataset-driven research for improving recommender systems for learning. In Ph. Long, & G. Siemens (Eds.), Proceedings of 1st International Conference Learning Analytics & Knowledge (pp. 44-53). February, 27-March, 1, 2011, Banff, Alberta, Canada. http://dl.acm.org/citation.cfm?id=2090122&CFID=77368864&CFTOKEN=72282583In the world of recommender systems, it is a common practice to use public available datasets from different application environments (e.g. MovieLens, Book-Crossing, or EachMovie) in order to evaluate recommendation algorithms. These datasets are used as benchmarks to develop new recommendation algorithms and to compare them to other algorithms in given settings. In this paper, we explore datasets that capture learner interactions with tools and resources. We use the datasets to evaluate and compare the performance of different recommendation algorithms for Technology Enhanced Learning (TEL). We present an experimental comparison of the accuracy of several collaborative filtering algorithms applied to these TEL datasets and elaborate on implicit relevance data, such as downloads and tags, that can be used to augment explicit relevance evidence in order to improve the performance of recommendation algorithms.dataTEL, STELLAR, AlterEgo, VOA3

Learning Analytics Dashboards

status: publishe

Tracking Data in Open Learning Environments

The collection and management of learning traces, metadata about actions that students perform while they learn, is a core topic in the domain of Learning Analytics. In this paper, we present a simple architecture for collecting and managing learning traces. We describe requirements, different components of the architecture, and our experiences with the successful deployment of the architecture in two different case studies: a blended learning university course and an enquiry based learning secondary school course. The architecture relies on trackers, collecting agents that fetch data from external services, for flexibility and configurability. In addition, we discuss how our architecture meets the requirements of different learning environments, critical reflections and remarks on future work

Principal component analysis of molecular clouds: Can CO reveal the dynamics?

We use principal component analysis (PCA) to study the gas dynamics in numerical simulations of typical molecular clouds (MCs). Our simulations account for the non-isothermal nature of the gas and include a simplified treatment of the time-dependent gas chemistry. We model the CO line emission in a post-processing step using a 3D radiative transfer code. We consider mean number densities n0 = 30, 100, 300 cm−3 that span the range of values typical for MCs in the solar neighbourhood and investigate the slope αPCA of the pseudo-structure function computed by PCA for several components: the total density, H2 density, 12CO density, 12CO J = 1 → 0 intensity and 13CO J = 1 → 0 intensity. We estimate power-law indices αPCA for different chemical species that range from 0.5 to 0.9, in good agreement with observations, and demonstrate that optical depth effects can influence the PCA. We show that when the PCA succeeds, the combination of chemical inhomogeneity and radiative transfer effects can influence the observed PCA slopes by as much as ≈±0.1. The method can fail if the CO distribution is very intermittent, e.g. in low-density clouds where CO is confined to small fragments