RollCall is a Chrome extension for tracking Google Meet attendance records without manual spreadsheet cleanup. It records join, leave, and rejoin times locally and turns each meeting into an attendance record with participant timelines and session history.
Each local record can include a meeting summary, participant table, and timeline access. RollCall keeps the first join, last leave, total time, live status, and rejoin count for each participant, and it preserves exact join and leave windows so repeated entries and exits remain visible. It also supports reviewing attendance across multiple sessions, and participant reports connect attendance rate, punctuality, session duration, and calendar history. Meetings can be renamed so the record matches a class, training session, or recurring call.
The extension is built for teachers, corporate trainers, and meeting organizers. The page specifically mentions classes, office hours, student follow-up records, cohorts, training sessions, and recurring meetings as use cases. It works with Chrome and Google Meet, and the page says no additional software installation is required. The workflow shown is to add it to Chrome, join Google Meet, then review reports and export a CSV copy when needed.
RollCall stores attendance records in Chrome local storage on the device and does not require a RollCall account or cloud upload. It includes CSV export for moving records into another system, spreadsheet, or gradebook. The page identifies it as a Chrome extension and describes it as a private, local attendance tracker for Google Meet.
In the Forms, surveys & polls space, Google Meet Attendance Tracker Extension takes a focused approach. It focuses on automating and simplifying the process of tracking attendance in Google Meet sessions. It is built as a B2B product for teachers and meeting organizers. Google Meet Attendance Tracker Extension is free to use. It runs on the web and embeddable surfaces.
Among its 5 catalogued features are attendance tracking, CSV export, and local storage.
Summary written by a language model from the project’s public pages.
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