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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2407.03118 |
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| _version_ | 1866909268875149312 |
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| author | Klausmann, Tim Köppel, Marius Schunk, Daniel Zipperle, Isabell |
| author_facet | Klausmann, Tim Köppel, Marius Schunk, Daniel Zipperle, Isabell |
| contents | The individualization of learning contents based on digital technologies promises large individual and social benefits. However, it remains an open question how this individualization can be implemented. To tackle this question we conduct a randomized controlled trial on a large digital self-learning platform. We develop an algorithm based on two convolutional neural networks that assigns tasks to $4,365$ learners according to their learning paths. Learners are randomized into three groups: two treatment groups -- a group-based adaptive treatment group and an individual adaptive treatment group -- and one control group. We analyze the difference between the three groups with respect to effort learners provide and their performance on the platform. Our null results shed light on the multiple challenges associated with the individualization of learning paths. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03118 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can machine learning solve the challenge of adaptive learning and the individualization of learning paths? A field experiment in an online learning platform Klausmann, Tim Köppel, Marius Schunk, Daniel Zipperle, Isabell Machine Learning The individualization of learning contents based on digital technologies promises large individual and social benefits. However, it remains an open question how this individualization can be implemented. To tackle this question we conduct a randomized controlled trial on a large digital self-learning platform. We develop an algorithm based on two convolutional neural networks that assigns tasks to $4,365$ learners according to their learning paths. Learners are randomized into three groups: two treatment groups -- a group-based adaptive treatment group and an individual adaptive treatment group -- and one control group. We analyze the difference between the three groups with respect to effort learners provide and their performance on the platform. Our null results shed light on the multiple challenges associated with the individualization of learning paths. |
| title | Can machine learning solve the challenge of adaptive learning and the individualization of learning paths? A field experiment in an online learning platform |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.03118 |