Identifying Struggling Students by Comparing Online Tutor Clickstreams
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open in viewerNew ways to identify students in need of assistance are imperative to the evolution of online tutoring platforms. Currently implemented models to identify struggling students use costly and tedious classroom observation paired with student's platform usage, and are often suitable for only a subset of students. With the recent influx of new students to online tutoring platforms due to COVID-19, a simple method to quickly identify struggling students could help facilitate effective remote learning. To this end, we created an anomaly detection algorithm that models the normal behavior of students during remote learning and recognizes when students deviate from this behavior. We demonstrated how anomalous behavior not only revealed which students needed additional assistance, but also helped predict student learning outcomes and reduced the confidence intervals in research experiments performed within the online tutoring platform.
- Creator
- 贡献者
- Degree
- Unit
- Publisher
- Identifier
- etd-5496
- 关键词
- Advisor
- Defense date
- Year
- 2021
- Date created
- 2021-03-02
- Resource type
- Rights statement
- License
- 最新修改
- 2023-09-20
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项目
Permanent link to this page: https://digital.wpi.edu/show/5d86p315b