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Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video.This integration allows for a more holistic understanding of complex data, improving model performance in tasks like visual question answering, cross-modal retrieval, [1] text-to-image generation, [2] aesthetic ranking, [3] and ...
However, these learning outcomes are now being presented in new forms as multimodality in the classroom which suggests a shift from traditional media such as paper-based text to more modern media such as screen-based texts. The choice to integrate multimodal forms in the classroom is still controversial within educational communities.
Multimodal pedagogy is an approach to the teaching of writing that implements different modes of communication. [ 1 ] [ 2 ] Multimodality refers to the use of visual, aural, linguistic, spatial, and gestural modes in differing pieces of media, each necessary to properly convey the information it presents.
Multimodal interaction, a form of human-machine interaction using multiple modes of input/output; Multimodal therapy, an approach to psychotherapy; Multimodal learning, machine learning methods using multiple input modalities; Multimodal transport, a contract for delivery involving the use of multiple modes of goods transport
Multiliteracy (plural: multiliteracies) is an approach to literacy theory and pedagogy coined in the mid-1990s by the New London Group. [1] The approach is characterized by two key aspects of literacy – linguistic diversity and multimodal forms of linguistic expressions and representation.
Two major groups of multimodal interfaces have merged, one concerned in alternate input methods and the other in combined input/output. The first group of interfaces combined various user input modes beyond the traditional keyboard and mouse input/output, such as speech, pen, touch, manual gestures, [21] gaze and head and body movements. [22]
Multisensory integration, also known as multimodal integration, is the study of how information from the different sensory modalities (such as sight, sound, touch, smell, self-motion, and taste) may be integrated by the nervous system. [1]
Multimodal sentiment analysis is a technology for traditional text-based sentiment analysis, which includes modalities such as audio and visual data. [1] It can be bimodal, which includes different combinations of two modalities, or trimodal, which incorporates three modalities. [ 2 ]