81,582 research outputs found
eProfile v3.2 guidance manual
"Context: The eProfile is an electronic data collection tool that has been funded by the
Department for Education (DfE) and developed in partnership with QCDA.
It provides an electronic means of recording the final judgements made of a child's attainment
at the end of the EYFS. It offers an invaluable source of information to support transition and
enables year 1 teachers to plan an effective, responsive and appropriate curriculum that will
meet all children's needs and next learning steps.
The eProfile will assist practitioners and headteachers in building a picture of a childās
attainment during the reception year across all six areas of learning and development. It gives
vital information to inform the schoolās self evaluation process and can also be used to
evaluate provision." - Page 3
Generating Explanatory Captions for Information Graphics
Graphical presentations can be used to communicate information in relational data sets succinctly and effectively. However, novel graphical presentations about numerous attributes and their relationships are often difficult to understand completely until explained. Automatically generated graphical presentations must therefore either be limited to simple, conventional ones, or risk incomprehensibility. One way of alleviating this problem is to design graphical presentation systems that can work in conjunction with a natural language generator to produce "explanatory captions." This paper presents three strategies for generating explanatory captions to accompany information graphics based on: (1) a representation of the structure of the graphical presentation (2) a framework for identifyingthe perceptual complexity of graphical elements, and (3) the structure of the data expressed in the graphic. We describe an implemented system and illustrate how it is used to generate explanatory cap..
Prediction Scores as a Window into Classifier Behavior
Most multi-class classifiers make their prediction for a test sample by
scoring the classes and selecting the one with the highest score. Analyzing
these prediction scores is useful to understand the classifier behavior and to
assess its reliability. We present an interactive visualization that
facilitates per-class analysis of these scores. Our system, called Classilist,
enables relating these scores to the classification correctness and to the
underlying samples and their features. We illustrate how such analysis reveals
varying behavior of different classifiers. Classilist is available for use
online, along with source code, video tutorials, and plugins for R, RapidMiner,
and KNIME at https://katehara.github.io/classilist-site/.Comment: Presented at NIPS 2017 Symposium on Interpretable Machine Learnin
Progression guidance 2009ā10: improving data to raise attainment and maximise the progress of learners with special educational needs, learning difficulties and disabilities (National Strategies)
"The purpose of this guidance is to raise expectations and set out the evidence of the progress already
being made by learners with special educational needs, learning difficulties and disabilities (SEN/LDD). The focus is on those working below age-related expectations." - Page 4
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