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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
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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
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  Data: <searchLink fieldCode="SO" term="%22Grantee+Submission%22"><i>Grantee Submission</i></searchLink>. 2022Paper presented at the Annual Meeting of the International Educational Data Mining Society (15th, Durham, United Kingdom, Jul 2022).
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  Data: 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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