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It has been shown in multiple studies that expert-created on-demand assistance, such as hint messages, improves student learning in online learning environments. However, there are also evident that certain types of assistance may be detrimental to student learning. In addition, creating and maintaining on-demand assistance are hard and time-consuming. In 2017-2018 academic year, 132,738 distinct problems were assigned inside ASSISTments, but only 38,194 of those problems had on-demand assistance. In order to take on-demand assistance to scale, we needed a system that is able to gather new on-demand assistance and allows us to test and measure its effectiveness. Thus, we designed and deployed TeacherASSIST inside ASSISTments. TeacherASSIST allowed teachers to create on-demand assistance for any problems as they assigned those problems to their students. TeacherASSIST then redistributed on-demand assistance by one teacher to students outside of their classrooms. We found that teachers inside ASSISTments had created 40,292 new instances of assistance for 25,957 different problems in three years. There were 14 teachers who created more than 1,000 instances of on-demand assistance. We also conducted two large-scale randomized controlled experiments to investigate how on-demand assistance created by one teacher affected students outside of their classes. Students who received on-demand assistance for one problem resulted in significant statistical improvement on the next problem performance. The students' improvement in this experiment confirmed our hypothesis that crowd-sourced on-demand assistance was sufficient in quality to improve student learning, allowing us to take on-demand assistance to scale.
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Testing the Validity and Reliability of Intrinsic Motivation Inventory Subscales within ASSISTments
Online learning environments allow for the implementation of psychometric scales on diverse samples of students participating in authentic learning tasks. One such scale, the Intrinsic Motivation Inventory (IMI) can be used to inform stakeholders of students’ subjective motivational and regulatory styles. The IMI is a multidimensional scale developed in support of Self-Determination Theory [1, 2, 3], a strongly validated theory stating that motivation and regulation are moderated by three innate needs: autonomy, belonging, and competence. As applied to education, the theory posits that students who perceive volition in a task, those who report stronger connections with peers and teachers, and those who perceive themselves as competent in a task are more likely to internalize the task and excel. ASSISTments, an online mathematics platform, is hosting a series of randomized controlled trials targeting these needs to promote integrated learning. The present work supports these studies by attempting to validate four subscales of the IMI within ASSISTments. Iterative factor analysis and item reduction techniques are used to optimize the reliability of these subscales and limit the obtrusive nature of future data collection efforts. Such scale validation efforts are valuable because student perceptions can serve as powerful covariates in differentiating effective learning interventions.
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- Award ID(s):
- 1724889
- PAR ID:
- 10095363
- Date Published:
- Journal Name:
- Proceedings of the Nineteenth International Conference on Artificial Intelligence in Education
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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It has been shown in multiple studies that expert-created on demand assistance, such as hint messages, improves student learning in online learning environments. However, there are also evident that certain types of assistance may be detrimental to student learning. In addition, creating and maintaining on-demand assistance are hard and time-consuming. In 2017-2018 academic year, 132,738 distinct problems were assigned inside ASSISTments, but only 38,194 of those problems had on-demand assistance. In order to take on-demand assistance to scale, we needed a system that is able to gather new on-demand assistance and allows us to test and measure its effectiveness. Thus, we designed and deployed TeacherASSIST inside ASSISTments. TeacherASSIST allowed teachers to create on demand assistance for any problems as they assigned those problems to their students. TeacherASSIST then redistributed on-demand assistance by one teacher to students outside of their classrooms. We found that teachers inside ASSISTments had created 40,292 new instances of assistance for 25,957 different problems in three years. There were 14 teachers who created more than 1,000 instances of on-demand assistance. We also conducted two large-scale randomized controlled experiments to investigate how on-demand assistance created by one teacher affected students outside of their classes. Students who received on-demand assistance for one problem resulted in significant statistical improvement on the next problem performance. The students’ improvement in this experiment confirmed our hypothesis that crowd-sourced on demand assistance was sufficient in quality to improve student learning, allowing us to take on-demand assistance to scale.more » « less
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