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Visible learning is a meta-study that analyzes effect sizes of measurable influences on learning outcomes in educational settings. [1] It was published by John Hattie in 2008 and draws upon results from 815 other Meta-analyses. The Times Educational Supplement described Hattie's meta-study as "teaching's holy grail". [2]
Database of grayscale handwritten digits. 60,000 image, label classification 1994 [1] LeCun et al. Extended MNIST: Database of grayscale handwritten digits and letters. 810,000 image, label classification 2010 [2] NIST 80 Million Tiny Images: 80 million 32×32 images labelled with 75,062 non-abstract nouns. 80,000,000 image, label 2008 [3 ...
The IEEE 1484.12.1-2020 – Standard for Learning Object Metadata [1] is the latest revision of an internationally recognised open standard (published by the Institute of Electrical and Electronics Engineers Standards Association, New York) under the LTSC sponsorship for the description of “learning objects".
Meta-learning [1] [2] is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing ...
Meta-modeling is the analysis, construction and development of the frames, rules, constraints, models and theories applicable and useful for the modeling in a predefined class of problems. The meta-data side of the diagram consists of a concept diagram. This is basically an adjusted class diagram as described in Booch, Rumbaugh and Jacobson (1999).
Meta’s open-source family of models may give it a competitive edge, but that doesn’t mean it’s going to knock OpenAI, Google, Microsoft, Anthropic, or any of the host of other companies ...
The question pool in OpenOlat is a database of individual test questions, so-called items, usually in QTI format. Each item contains all associated information and metadata captured and compiled according to the Learning Objects Metadata. Items are exported in groups from the question pool and made available as an OpenOlat test learning resource.
The XML Metadata Interchange (XMI) is an Object Management Group (OMG) standard for exchanging metadata information via Extensible Markup Language (XML). It can be used for any metadata whose metamodel can be expressed in Meta-Object Facility (MOF) , a platform-independent model (PIM).