How can we help skyrocket the AI precision of your learning platform?
Data annotation for AI-enabled learning platforms- Our approach.
- Topic tagging: Labeling learning materials with relevant subject areas and subtopics helps in categorizing content effectively.
- Skill level tagging: Identifying the difficulty level of content (beginner, intermediate, advanced).
- Learning objective tagging: Labeling content based on specific learning objectives it aims to achieve.
- Key concept extraction: Identifying key concepts and terms within text or video content.
- Engagement level: Labeling learner/student interactions like clicks, time spent on a page, or quiz attempts to indicate their engagement level.
- Question difficulty: Classifying the difficulty level of assessment questions.
- Answer correctness: Labeling learner/student responses as correct or incorrect.
- Learner progress: Tracking learner/student progress through different learning modules and identifying areas of struggle.
- Performance patterns: Identifying patterns in learner/student performance based on their learning style or demographics.
Data annotation techniques for AI-enabled learning platforms.
- Named Entity Recognition (NER): Identifying and classifying specific entities like people, locations, or organizations within text.
- Sentiment analysis: Labeling text as positive, negative, or neutral to understand learner/student feedback.
- Object detection: Identifying and bounding specific objects within an educational video (e.g., key visual aids).
- Action recognition: Labeling specific actions or events taking place in a video.
- Speech transcription: Converting spoken language in educational videos to text for analysis.
- Speaker identification: Identifying different speakers in a dialogue.
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