From peers to professors: Study shows the benefits of students encouraging students

Study International | July 23, 2019

From peers to professors: Study shows the benefits of students encouraging students
Aside from technological performance data updates and flashy star charts to boost student progress in the classroom, why not go back to basics? In the EdTech world, there’s always a new gadget that encourages learners to excel at exams and maintain high results. But new research suggests that students don’t always need aid from tech tools and educational enhancement instruments. Instead, they can look to their peers for support and effective motivation. Recently published the Proceedings of the National Academy of Sciences of the United States of America (PNAS), the study’s authors, Lauren Eskreis-Winklera, Katherine L. Milkmana, Dena M. Grometb and Angela L. Duckworth explore the impact of motivational peer-to-peer advice in high schools. Highlighting the fact that some methods, such as additional tutoring or printed motivational materials can be costly for schools, the authors label peer-to-peer support as a cost-effective method that doesn’t necessarily need extra funds or financial backing to enforce it. After conducting a large-scale field test that involved approximately 1,982 students attending seven diverse public high schools in the United States and an advice-giving activity, the study soon revealed interesting results.

Spotlight

For nearly a decade, researchers and technologists have been building algorithms designed to help practitioners identify and support those students who are least likely to persist and graduate. As more data has become available, these risk algorithms have become more sophisticated and diverse.In EAB’s work with the 200+ members of the Student Success Collaborative, we’ve found that risk algorithms almost always involve a trade-off between timeliness, breadth of applicability, and feasibility. Algorithms that are built on core student records (such as coursework and grades) apply to a broad number of students and are relatively inexpensive to maintain, but can only be updated a handful of times a year as more transcript data becomes available.

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Spotlight

For nearly a decade, researchers and technologists have been building algorithms designed to help practitioners identify and support those students who are least likely to persist and graduate. As more data has become available, these risk algorithms have become more sophisticated and diverse.In EAB’s work with the 200+ members of the Student Success Collaborative, we’ve found that risk algorithms almost always involve a trade-off between timeliness, breadth of applicability, and feasibility. Algorithms that are built on core student records (such as coursework and grades) apply to a broad number of students and are relatively inexpensive to maintain, but can only be updated a handful of times a year as more transcript data becomes available.