{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/1eca53bd57d34292914c4a9bb2bf21d4\" frameborder=\"0\" width=\"2184\" height=\"1638\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1638,"width":2184,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1638,"thumbnail_width":2184,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/1eca53bd57d34292914c4a9bb2bf21d4-631d2d251ee204e3.gif","duration":240.418,"title":"Dropout Detective: Overview","description":"This Loom explains how to use Dropout Detective to review student risk and course engagement data. The risk index is updated each evening, with the last update date and time shown at the bottom of the solution, and can be filtered by high, medium, and low using custom filters set by an Access Manager admin. It reviews a student profile such as Michael, showing active courses with grades, zeros, missing assignments, last LMS access, and the last upload time, including details of assignments with zeros via show buttons. The video also covers sending individual or group messages through Canvas, adding and filtering notes by type and tags, and viewing student history data."}