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Toward Personalizing Students' Education with Crowdsourced Tutoring

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Title: Toward Personalizing Students' Education with Crowdsourced Tutoring
Language: English
Authors: Prihar, Ethan, Patikorn, Thanaporn, Botelho, Anthony, Sales, Adam, Heffernan, Neil T.
Source: Grantee Submission. 2021.
Peer Reviewed: Y
Page Count: 9
Publication Date: 2021
Sponsoring Agency: National Science Foundation (NSF)
Institute of Education Sciences (ED)
Office of Postsecondary Education (ED)
Office of Elementary and Secondary Education (OESE) (ED), Education Innovation and Research (EIR)
Office of Naval Research (ONR) (DOD)
Contract Number: 1917808
1931523
1940236
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R305A170243
R305A180401
R305A120125
R305C100024
P200A180088
P200A150306
N000141812768
R305A170641
Document Type: Speeches/Meeting Papers
Reports - Research
Education Level: Junior High Schools
Middle Schools
Secondary Education
Descriptors: Electronic Publishing, Group Experience, Tutoring, Individualized Instruction, Instructional Materials, Electronic Learning, Mathematics Instruction, Educational Technology, Video Technology, Middle School Mathematics, Teacher Effectiveness
DOI: 10.1145/3430895.3460130
Abstract: As more educators integrate their curricula with online learning, it is easier to crowdsource content from them. Crowdsourced tutoring has been proven to reliably increase students' next problem correctness. In this work, we confirmed the findings of a previous study in this area, with stronger confidence margins than previously, and revealed that only a portion of crowdsourced content creators had a reliable benefit to students. Furthermore, this work provides a method to rank content creators relative to each other, which was used to determine which content creators were most effective overall, and which content creators were most effective for specific groups of students. When exploring data from TeacherASSIST, a feature within the ASSISTments learning platform that crowdsources tutoring from teachers, we found that while overall this program provides a benefit to students, some teachers created more effective content than others. Despite this finding, we did not find evidence that the effectiveness of content reliably varied by student knowledge-level, suggesting that the content is unlikely suitable for personalizing instruction based on student knowledge alone. These findings are promising for the future of crowdsourced tutoring as they help provide a foundation for assessing the quality of crowdsourced content and investigating content for opportunities to personalize students' education. [This paper was published in: " L@S '21, June 22-25, 2021, Virtual Event, Germany," ACM, 2021.]
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2022
Accession Number: ED623499
Database: ERIC
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  Data: As more educators integrate their curricula with online learning, it is easier to crowdsource content from them. Crowdsourced tutoring has been proven to reliably increase students' next problem correctness. In this work, we confirmed the findings of a previous study in this area, with stronger confidence margins than previously, and revealed that only a portion of crowdsourced content creators had a reliable benefit to students. Furthermore, this work provides a method to rank content creators relative to each other, which was used to determine which content creators were most effective overall, and which content creators were most effective for specific groups of students. When exploring data from TeacherASSIST, a feature within the ASSISTments learning platform that crowdsources tutoring from teachers, we found that while overall this program provides a benefit to students, some teachers created more effective content than others. Despite this finding, we did not find evidence that the effectiveness of content reliably varied by student knowledge-level, suggesting that the content is unlikely suitable for personalizing instruction based on student knowledge alone. These findings are promising for the future of crowdsourced tutoring as they help provide a foundation for assessing the quality of crowdsourced content and investigating content for opportunities to personalize students' education. [This paper was published in: " L@S '21, June 22-25, 2021, Virtual Event, Germany," ACM, 2021.]
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