2026-09-19T20:45:31Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1173202026-06-06T00:49:08Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Kalyan Veeramachaneni.
O'Sullivan, John J. D
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-08-08T19:49:16Z
2018-08-08T19:49:16Z
2017
2017
http://hdl.handle.net/1721.1/117320
1046087534
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 65-66).
Teach2Learn is a website which crowd-sources the problem of labeling natural text samples using gamified education as an incentive. Students assign labels to text samples from an unlabeled data set, thereby teaching superised machine learning algorithms how to interpret new samples. In return, students can learn how that algorithm works by unlocking lessons written by researchers. This aligns the incentives of researchers and learners to help both achieve their goals. The application used current best practices in gamification to create a motivating structure around that labeling task. Testing showed that 27.7% of the user base (5/18 users) engaged with the content and labeled enough samples to unlock all of the lessons, suggesting that learning modules are sufficient motivation for the right users. Attempts to grow the platform through paid social media advertising were unsuccessful, likely because users aren't looking for a class when they browse those sites. Unpaid posts on subreddits discussing related topics, where users were more likely to be searching for learning opportunities, were more successful. Future research should seek users through comparable sites and explore how Teach2Learn can be used as an additional learning resource in classrooms.
by John J.D. O'Sullivan
M.Eng.
66 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Teach2Learn : gamifying education to gather training data for natural language processing
Teach 2 Learn : gamifying education to gather training data for natural language processing
Gamifying education to gather training data for natural language processing
Thesis
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<Abstract>Teach2Learn is a website which crowd-sources the problem of labeling natural text samples using gamified education as an incentive. Students assign labels to text samples from an unlabeled data set, thereby teaching superised machine learning algorithms how to interpret new samples. In return, students can learn how that algorithm works by unlocking lessons written by researchers. This aligns the incentives of researchers and learners to help both achieve their goals. The application used current best practices in gamification to create a motivating structure around that labeling task. Testing showed that 27.7% of the user base (5/18 users) engaged with the content and labeled enough samples to unlock all of the lessons, suggesting that learning modules are sufficient motivation for the right users. Attempts to grow the platform through paid social media advertising were unsuccessful, likely because users aren't looking for a class when they browse those sites. Unpaid posts on subreddits discussing related topics, where users were more likely to be searching for learning opportunities, were more successful. Future research should seek users through comparable sites and explore how Teach2Learn can be used as an additional learning resource in classrooms.</Abstract>
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