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A s m or e e d u c at or s i nt e gr at e t h eir c urri c ul a wit h o nli n e l e ar ni n g, it i s e a si er t o cr o w d s o ur c e c o nt e nt fr o m t h e m. Cr o w ds o ur c e d t ut ori n g h a s b e e n pr o v e n t o r eli a bl y i n cr e a s e st u d e nt s’ n e xt pr o bl e m c orr e ct n e s s. I n t hi s w or k, w e c o n fir m e d t h e fi n di n g s of a pr e vi o u s st u d y i n t hi s ar e a, wit h str o n g er c o n fi d e n c e m ar gi n s t h a n pr e vi o u sl y, a n d r e v e al e d t h at o nl y a p orti o n of cr o w d s o ur c e d c o nt e nt cr e at or s h a d a r eli a bl e b e n e fit t o st ud e nt s. F urt h er m or e, t hi s w or k pr o vi d e s a m et h o d t o r a n k c o nt e nt cr e at or s r el ati v e t o e a c h ot h er, w hi c h w a s u s e d t o d et er mi n e w hi c h c o nt e nt cr e at or s w er e m o st eff e cti v e o v er all, a n d w hi c h c o nt e nt cr e at or s w er e m o st eff e cti v e f or s p e ci fi c gr o u p s of st u d e nt s. W h e n e x pl ori n g d at a fr o m Te a c h er A SSI S T, a f e at ur e wit hi n t h e A S SI S T m e nt s l e ar ni n g pl atf or m t h at cr o w d s o ur c e s t ut ori n g fr o m t e a c h er s, w e f o u n d t h at w hil e o v erall t hi s pr o gr a m pr o vi d e s a b e n e fit t o st u d e nt s, s o m e t e a c h er s cr e at e d m or e eff e cti v e c o nt e nt t h a n ot h er s. D e s pit e t hi s fi n di n g, w e di d n ot fi n d e vi d e n c e t h at t h e eff e cti v e n e s s of c o nt e nt r eli a bl y v ari e d b y st u d e nt k n o wl e d g e-l e v el, s u g g e sti n g t h at t h e c o nt e nt i s u nli k el y s uit a bl e f or p er s o n ali zi n g i n str u cti o n b a s e d o n st u d e nt k n o wl e d g e al o n e. T h e s e fi n di n g s ar e pr o mi si n g f or t h e f ut ur e of cr o w d s o ur c e d t ut ori n g a s t h e y h el p pr o vi d e a f o u n d ati o n f or a s s e s si n g t h e q u alit y of cr o w d s o ur c e d c o nt e nt a n d i n v e sti g ati n g c o nt e nt f or o p p ort u niti e s t o p er s o n ali z e st u d e nt s’ e d u c ati o n. 

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. 

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. 

The increased usage of computer-based learning platforms and online tools in classrooms presents new opportunities to not only study the underlying constructs involved in the learning process, but also use this information to identify and aid struggling students. Many learning platforms, particularly those driving or supplementing instruction, are only able to provide aid to students who interact with the system. With this in mind, student persistence emerges as a prominent learning construct contributing to students success when learning new material. Conversely, high persistence is not always productive for students, where additional practice does not help the student move toward a state of mastery of the material. In this paper, we apply a transfer learning methodology using deep learning and traditional modeling techniques to study high and low representations of unproductive persistence. We focus on two prominent problems in the fields of educational data mining and learner analytics representing low persistence, characterized as student "stopout," and unproductive high persistence, operationalized through student "wheel spinning," in an effort to better understand the relationship between these measures of unproductive persistence (i.e. stopout and wheel spinning) and develop early detectors of these behaviors. We find that models developed to detect each within and across-assignment stopout and wheel spinning are able to learn sets of features that generalize to predict the other. We further observe how these models perform at each learning opportunity within student assignments to identify when interventions may be deployed to best aid students who are likely to exhibit unproductive persistence.