DeepClover
Lead Participant:
HABITAT LEARN
Abstract
Note Taking Express (NTE) provides an online personalised learning solution (NTE Learning) delivering curated content, live captioning and summary notes to students at 180 higher education institutions (HEIs) in the UK and North America.
NTE is seeking to solve a commercial, educational and attainment problem by developing DeepClover through:
AI based multimedia segmentation and metadata extraction: using speech recognition, Optical Character Recognition (OCR) and NLP to quickly segment the multimedia resources and extract metadata from each segment for indexing.
Contextual, semantic interlinking and search for multimedia fragments: automatically interlink personal notes to segmented and modular education resources created by lecturers or external publishers. Then users can quickly locate a relevant place in a long lecture, so that those resources can be repurposed for adaptive learning in fine granularity.
Question-answer model: Instead of searching keywords, NTE will design QA models to provide formative answers to students' questions based on the private DeepClover repository. This is a completely novel way of utilising their original note taking and content repository.
Simplified user experience: curation of a personal learning repository by collecting personal or public multimedia segments into one place for rapid searching and referencing.
Development of DeepClover has the following goals:
To create a web application to complement NTE Learning which provides users with a personalised learning platform to easily discover and consume segments in the lecture recordings to improve learning outcomes through individual empowerment. This will
To transform the way in which users store and manage their notes, media and data portfolios.
Enabling users to access relevant curated information to enhance their learning repositories using an AI-driven recommendation engine.
To create 20 highly skilled jobs in the UK over the next 9 years.
NTE is seeking to solve a commercial, educational and attainment problem by developing DeepClover through:
AI based multimedia segmentation and metadata extraction: using speech recognition, Optical Character Recognition (OCR) and NLP to quickly segment the multimedia resources and extract metadata from each segment for indexing.
Contextual, semantic interlinking and search for multimedia fragments: automatically interlink personal notes to segmented and modular education resources created by lecturers or external publishers. Then users can quickly locate a relevant place in a long lecture, so that those resources can be repurposed for adaptive learning in fine granularity.
Question-answer model: Instead of searching keywords, NTE will design QA models to provide formative answers to students' questions based on the private DeepClover repository. This is a completely novel way of utilising their original note taking and content repository.
Simplified user experience: curation of a personal learning repository by collecting personal or public multimedia segments into one place for rapid searching and referencing.
Development of DeepClover has the following goals:
To create a web application to complement NTE Learning which provides users with a personalised learning platform to easily discover and consume segments in the lecture recordings to improve learning outcomes through individual empowerment. This will
To transform the way in which users store and manage their notes, media and data portfolios.
Enabling users to access relevant curated information to enhance their learning repositories using an AI-driven recommendation engine.
To create 20 highly skilled jobs in the UK over the next 9 years.
Lead Participant | Project Cost | Grant Offer |
|---|---|---|
Participant |
||
| HABITAT LEARN |
People |
ORCID iD |
| Jeremy Brassington (Project Manager) |