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Peer assessment in a Social Media course
This document summarizes a study on the reliability of peer assessment in a social media course where students rated assignments using a social media app. Sixty students in the course completed individual assignments and then rated 20 assignments from peers. Their ratings were also compared to ratings from instructors and 11-year old students. The results found moderate agreement between peers and instructors, moderate agreement between peers and students, and high agreement between instructors and students, indicating that peer assessment can be a reliable approach. The researchers are working to fully integrate this peer assessment method into the Graasp collaboration platform.
Peer assessment in a Social Media course
- 1. Peer Assessment Based onRatings in a Social Media Course Andrii Vozniuk, Adrian Holzer, Denis Gillet Andrii Vozniuk, Adrian Holzer, Denis Gillet. “Peer Assessment Based on Ratings in a Social Media Course”. In the proceedings of LAK’14. The copyright of images belong to their authors. I will remove them on demand. Contact me at adrian.holzer@epfl.ch
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- 5. Practice what youpreach social evaluation in a social media course ! using a social media app
- 6. Social Media Course age23-28 60students
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- 8. Goal Extract relevant information forthe class from a social network in the context of the Long Tail and plot the result
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- 15. Grading Your grade isstrongly influenced by the grade from your peers, but might differ 5 - I love it, it’s amazing 4 - I like the report 3 - It’s OK 2 - I don’t like it 1 - I hate the report as in social media
- 16. Criteria 1 - Isthe report about a social media? 3 - Is the dataset representative? 4 - Is it technically advanced? 5 - Is the report well written? 2 - Is the long tail hypothesis clear? 6 - Is it interesting and creative?
- 17. Graasp create a space, dropresources & apps, invite people to collaborate
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- 28. 40 kids 15 reportseach 11 years old
- 29. Statistics Consensus Group mean AgreementCohen’s weighted Kappa
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- 34. Agreement Weighted Kappa Lower Bound (95%) Upper Bound (95%) SigLevel 0.196 -0.59 0.427 0.065
- 35. Agreement Weighted Kappa Lower Bound (95%) Upper Bound (95%) SigLevel 0.285 0.035 0.501 0.013
- 36. Agreement Weighted Kappa Lower Bound (95%) Upper Bound (95%) SigLevel 0.774 0.648 0.858 0.00001
- 37. Conclusion Peer assessment seems quitereliable Based not only on appearance Working on the full integration in Graasp
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