Exploring Common Trends in Online Educational Experiments
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| Title: | Exploring Common Trends in Online Educational Experiments |
|---|---|
| Language: | English |
| Authors: | Prihar, Ethan, Syed, Manaal, Ostrow, Korinn, Shaw, Stacy, Sales, Adam, Heffernan, Neil |
| Source: | Grantee Submission. 2022Paper presented at the Annual Meeting of the International Educational Data Mining Society (15th, Durham, United Kingdom, Jul 2022). |
| Peer Reviewed: | Y |
| Page Count: | 12 |
| Publication Date: | 2022 |
| Sponsoring Agency: | National Science Foundation (NSF) Institute of Education Sciences (ED) Office of Elementary and Secondary Education (OESE) (ED), Education Innovation and Research (EIR) Office of Naval Research (ONR) (DOD) |
| Contract Number: | 2118725 2118904 1950683 1917808 1931523 1940236 1917713 1903304 1822830 1759229 1724889 1636782 1535428 R305N210049 R305D210031 R305A170137 R305A170243 R305A180401 R305A120125 U411B190024 S411B210024 N000141812768 R305A170641 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Descriptors: | Educational Trends, Electronic Learning, Educational Experience, Educational Experiments, Intelligent Tutoring Systems, Randomized Controlled Trials, Skill Development, Mastery Learning, Individualized Instruction, Design |
| DOI: | 10.5281/zenodo.6853041 |
| Abstract: | As online learning platforms become more ubiquitous throughout various curricula, there is a growing need to evaluate the effectiveness of these platforms and the different methods used to structure online education and tutoring. Towards this endeavor, some platforms have performed randomized controlled experiments to compare different user experiences, curriculum structures, and tutoring strategies in order to ensure the effectiveness of their platform and personalize the education of the students using it. These experiments are typically analyzed on an individual basis in order to reveal insights on a specific aspect of students' online educational experience. In this work, the data from 50,752 instances of 30,408 students participating in 50 different experiments conducted at scale within the online learning platform ASSISTments were aggregated and analyzed for consistent trends across experiments. By combining common experimental conditions and normalizing the dependent measures between experiments, this work has identified multiple statistically significant insights on the impact of various skill mastery requirements, strategies for personalization, and methods for tutoring in an online setting. This work can help direct further experimentation and inform the design and improvement of new and existing online learning platforms. The anonymized data compiled for this work are hosted by the Open Science Foundation and can be found at https://osf.io/59shv/. [This paper was published in: "Proceedings of the 15th International Conference on Educational Data Mining," edited by A. Mitrovic and N. Bosch, International Educational Data Mining Society, 2022, pp. 27-38.] |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2022 |
| Accession Number: | ED623511 |
| Database: | ERIC |
| Abstract: | As online learning platforms become more ubiquitous throughout various curricula, there is a growing need to evaluate the effectiveness of these platforms and the different methods used to structure online education and tutoring. Towards this endeavor, some platforms have performed randomized controlled experiments to compare different user experiences, curriculum structures, and tutoring strategies in order to ensure the effectiveness of their platform and personalize the education of the students using it. These experiments are typically analyzed on an individual basis in order to reveal insights on a specific aspect of students' online educational experience. In this work, the data from 50,752 instances of 30,408 students participating in 50 different experiments conducted at scale within the online learning platform ASSISTments were aggregated and analyzed for consistent trends across experiments. By combining common experimental conditions and normalizing the dependent measures between experiments, this work has identified multiple statistically significant insights on the impact of various skill mastery requirements, strategies for personalization, and methods for tutoring in an online setting. This work can help direct further experimentation and inform the design and improvement of new and existing online learning platforms. The anonymized data compiled for this work are hosted by the Open Science Foundation and can be found at https://osf.io/59shv/. [This paper was published in: "Proceedings of the 15th International Conference on Educational Data Mining," edited by A. Mitrovic and N. Bosch, International Educational Data Mining Society, 2022, pp. 27-38.] |
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| DOI: | 10.5281/zenodo.6853041 |
