Toward Personalizing Students' Education with Crowdsourced Tutoring
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| Title: | Toward Personalizing Students' Education with Crowdsourced Tutoring |
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| 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 1917713 1903304 1822830 1759229 1724889 1636782 1535428 1440753 1316736 1252297 1109483 DRL1031398 R305A170137 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 |
| 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.] |
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| DOI: | 10.1145/3430895.3460130 |
