24,694 research outputs found
Hierarchical structuring of Cultural Heritage objects within large aggregations
Huge amounts of cultural content have been digitised and are available
through digital libraries and aggregators like Europeana.eu. However, it is not
easy for a user to have an overall picture of what is available nor to find
related objects. We propose a method for hier- archically structuring cultural
objects at different similarity levels. We describe a fast, scalable clustering
algorithm with an automated field selection method for finding semantic
clusters. We report a qualitative evaluation on the cluster categories based on
records from the UK and a quantitative one on the results from the complete
Europeana dataset.Comment: The paper has been published in the proceedings of the TPDL
conference, see http://tpdl2013.info. For the final version see
http://link.springer.com/chapter/10.1007%2F978-3-642-40501-3_2
Multi-Industry Simplex : A Probabilistic Extension of GICS
Accurate industry classification is a critical tool for many asset management
applications. While the current industry gold-standard GICS (Global Industry
Classification Standard) has proven to be reliable and robust in many settings,
it has limitations that cannot be ignored. Fundamentally, GICS is a
single-industry model, in which every firm is assigned to exactly one group -
regardless of how diversified that firm may be. This approach breaks down for
large conglomerates like Amazon, which have risk exposure spread out across
multiple sectors. We attempt to overcome these limitations by developing MIS
(Multi-Industry Simplex), a probabilistic model that can flexibly assign a firm
to as many industries as can be supported by the data. In particular, we
utilize topic modeling, an natural language processing approach that utilizes
business descriptions to extract and identify corresponding industries. Each
identified industry comes with a relevance probability, allowing for high
interpretability and easy auditing, circumventing the black-box nature of
alternative machine learning approaches. We describe this model in detail and
provide two use-cases that are relevant to asset management - thematic
portfolios and nearest neighbor identification. While our approach has
limitations of its own, we demonstrate the viability of probabilistic industry
classification and hope to inspire future research in this field.Comment: 17 pages, 10 figure
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Educational Technology Topic Guide
This guide aims to contribute to what we know about the relationship between educational technology (edtech) and educational outcomes by addressing the following overarching question: What is the evidence that the use of edtech, by teachers or students, impacts teaching and learning practices, or learning outcomes? It also offers recommendations to support advisors to strengthen the design, implementation and evaluation of programmes that use edtech.
We define edtech as the use of digital or electronic technologies and materials to support teaching and learning. Recognising that technology alone does not enhance learning, evaluations must also consider how programmes are designed and implemented, how teachers are supported, how communities are developed and how outcomes are measured (see http://tel.ac.uk/about-3/, 2014).
Effective edtech programmes are characterised by:
a clear and specific curriculum focus
the use of relevant curriculum materials
a focus on teacher development and pedagogy
evaluation mechanisms that go beyond outputs.
These findings come from a wide range of technology use including:
interactive radio instruction (IRI)
classroom audio or video resources accessed via teachers’ mobile phones
student tablets and eReaders
computer-assisted learning (CAL) to supplement classroom teaching.
However, there are also examples of large-scale investment in edtech – particularly computers for student use – that produce limited educational outcomes. We need to know more about:
how to support teachers to develop appropriate, relevant practices using edtech
how such practices are enacted in schools, and what factors contribute to or mitigate against
successful outcomes.
Recommendations:
1. Edtech programmes should focus on enabling educational change, not delivering technology. In doing so, programmes should provide adequate support for teachers and aim to capture changes in teaching practice and learning outcomes in evaluation.
2. Advisors should support proposals that further develop successful practices or that address gaps in evidence and understanding.
3. Advisors should discourage proposals that have an emphasis on technology over education, weak programmatic support or poor evaluation.
4. In design and evaluation, value-for-money metrics and cost-effectiveness analyses should be carried out
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A conceptual framework for studying collective reactions to events in location-based social media
Events are a core concept of spatial information, but location-based social media (LBSM) provide information on reactions to events. Individuals have varied degrees of agency in initiating, reacting to or modifying the course of events, and reactions include observations of occurrence, expressions containing sentiment or emotions, or a call to action. Key characteristics of reactions include referent events and information about who reacted, when, where and how. Collective reactions are composed of multiple individual reactions sharing common referents. They can be characterized according to the following dimensions: spatial, temporal, social, thematic and interlinkage. We present a conceptual framework, which allows characterization and comparison of collective reactions. For a thematically well-defined class of event such as storms, we can explore differences and similarities in collective attribution of meaning across space and time. Other events may have very complex spatio-temporal signatures (e.g. political processes such as Brexit or elections), which can be decomposed into series of individual events (e.g. a temporal window around the result of a vote). The purpose of our framework is to explore ways in which collective reactions to events in LBSM can be described and underpin the development of methods for analysing and understanding collective reactions to events
A Theme-Rewriting Approach for Generating Algebra Word Problems
Texts present coherent stories that have a particular theme or overall
setting, for example science fiction or western. In this paper, we present a
text generation method called {\it rewriting} that edits existing
human-authored narratives to change their theme without changing the underlying
story. We apply the approach to math word problems, where it might help
students stay more engaged by quickly transforming all of their homework
assignments to the theme of their favorite movie without changing the math
concepts that are being taught. Our rewriting method uses a two-stage decoding
process, which proposes new words from the target theme and scores the
resulting stories according to a number of factors defining aspects of
syntactic, semantic, and thematic coherence. Experiments demonstrate that the
final stories typically represent the new theme well while still testing the
original math concepts, outperforming a number of baselines. We also release a
new dataset of human-authored rewrites of math word problems in several themes.Comment: To appear EMNLP 201
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