Community TAs scale high-touch learning, provide student-staff brokering, and build esprit de corps
Abstract
Massive online courses introduced Community TAs (CTAs) to help scale teaching staff support. CTAs are former top students who return as volunteer course staff. We studied CTAs in 3 classes on Coursera, including interviews and surveys from a Human-Computer Interaction (HCI) class. A key benefit of CTAs is their brokering role that mediates staff and student goals. CTAs provide greater discussion forum coverage (both in quantity and time of day) compared to instructor and Head TA (HTA) capabilities and contribute to peer assessment. As CTAs are new teachers, physically distributed, and culturally diverse, clear division of responsibilities is especially important.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Techniques such as community TAs, peer assessment, rubrics, video feedback are available to help MOOC instructors scale up provision of perceived personal attention.
“Techniques such as involvement of community teaching assistants (TAs),21 peer assessment tools,22 well-designed elaborated rubrics with specific content feedback23 and video feedbacks24 are available to help MOOC instructors scale up the provision of perceived personal attention to students.”
From Towards AI-Powered Personalization in MOOC Learning (2017) · cites this paper as [21]
The citation lists haven’t been fetched yet
References 7 · Cited by 7
- Authors
- Papadopoulos, K., Sritanyaratana, L., & Klemmer, S. R.
- Affiliations
- Citrix (United States) (US)
- Stanford University (US)
- University of California San Diego (US)
- ORCID
- Klemmer, Scott R.
- Venue
- Proceedings of the first ACM conference on Learning @ scale conference
- Publisher
- ACM
- Published
- 2014-03-04
- Pages
- 163-164
- OpenAlex
- W1966673560
- Cited by
- 7
- References
- 7
- Citations by year
- 2018: 2 · 2017: 2 · 2016: 1 · 2015: 2
- FWCI
- 1.06
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Conference on Learning @ Scale (L@S) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Online and Blended Learning
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Keywords
- massive open online courses, peer assessment, division of responsibilities, team spirit
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review