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1. Identifying Students' Attentive Fidelity for Calculus Instructional Videos

   Weinberg, A.; Tornai, J.; Thomas, M.; Martin, J.; Tallman, M.; Newman, K. (January 2019, Proceedings of the forty-first annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education.)

   Growing interest in “flipped” classrooms has made video lessons an increasingly prominent component of post-secondary mathematics curricula. However, relatively little is known about how students watch and learn from instructional videos. We describe and use an eye-tracking methodology to investigate attentive fidelity—the degree to which students attend to the visual imagery that is the subject of the video narration at each moment in time. Our preliminary study suggests that students’ attentive fidelity varies widely, but there was no evidence that this fidelity is connected to students’ ability to solve calculus problems. 

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2. Identifying students’ attentive fidelity for calculus instructional videos.

   Weinberg, A.; Tornai, J.; Thomas, M.; Martin, J; Tallman, M.; Newman, K. (October 2019, Proceedings of the 41st annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education)

   Growing interest in “flipped” classrooms has made video lessons an increasingly prominent component of post-secondary mathematics curricula. However, relatively little is known about how students watch and learn from instructional videos. We describe and use an eye-tracking methodology to investigate attentive fidelity—the degree to which students attend to the visual imagery that is the subject of the video narration at each moment in time. Our preliminary study suggests that students’ attentive fidelity varies widely, but there was no evidence that this fidelity is connected to students’ ability to solve calculus problems. 

   more » « less
3. Identifying students’ attentive fidelity for calculus instructional videos

   Weinberg, A.; Tornai, J.; Thomas, M.; Martin, J.; Tallman, M.; Newman, K. (October 2019, Proceedings of the 41st annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education)

   Growing interest in “flipped” classrooms has made video lessons an increasingly prominent component of post-secondary mathematics curricula. However, relatively little is known about how students watch and learn from instructional videos. We describe and use an eye-tracking methodology to investigate attentive fidelity—the degree to which students attend to the visual imagery that is the subject of the video narration at each moment in time. Our preliminary study suggests that students’ attentive fidelity varies widely, but there was no evidence that this fidelity is connected to students’ ability to solve calculus problems. 

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4. Investigating the Impact of Skill-Related Videos on Online Learning.

   Prihar, E. (July 2023, Proceedings of the Tenth ACM Conference on Learning@Scale (L@S '23), July 20-22, 2023, Copenhagen, Denmark)

   Many online learning platforms and MOOCs incorporate some amount of video-based content into their platform, but there are few randomized controlled experiments that evaluate the effective- ness of the different methods of video integration. Given the large amount of publicly available educational videos, an investigation into this content’s impact on students could help lead to more ef- fective and accessible video integration within learning platforms. In this work, a new feature was added into an existing online learn- ing platform that allowed students to request skill-related videos while completing their online middle-school mathematics assign- ments. A total of 18,535 students participated in two large-scale randomized controlled experiments related to providing students with publicly available educational videos. The first experiment investigated the effect of providing students with the opportunity to request these videos, and the second experiment investigated the effect of using a multi-armed bandit algorithm to recommend relevant videos. Additionally, this work investigated which features of the videos were significantly predictive of students’ performance and which features could be used to personalize students’ learning. Ultimately, students were mostly disinterested in the skill-related videos, preferring instead to use the platforms existing problem- specific support, and there was no statistically significant findings in either experiment. Additionally, while no video features were significantly predictive of students’ performance, two video fea- tures had significant qualitative interactions with students’ prior knowledge, which showed that different content creators were more effective for different groups of students. These findings can be used to inform the design of future video-based features within online learning platforms and the creation of different educational videos specifically targeting higher or lower knowledge students. 

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5. Investigating the Impact of Skill-Related Videos on Online Learning

   Prihar, E; Haim, A; Shen, T; Sales, A; Lee, D; Wu; X (July 2023, Proceedings of the Tenth ACM Conference on Learning@Scale (L@S '23))

   Many online learning platforms and MOOCs incorporate some amount of video-based content into their platform, but there are few randomized controlled experiments that evaluate the effective- ness of the different methods of video integration. Given the large amount of publicly available educational videos, an investigation into this content’s impact on students could help lead to more ef- fective and accessible video integration within learning platforms. In this work, a new feature was added into an existing online learn- ing platform that allowed students to request skill-related videos while completing their online middle-school mathematics assign- ments. A total of 18,535 students participated in two large-scale randomized controlled experiments related to providing students with publicly available educational videos. The first experiment investigated the effect of providing students with the opportunity to request these videos, and the second experiment investigated the effect of using a multi-armed bandit algorithm to recommend relevant videos. Additionally, this work investigated which features of the videos were significantly predictive of students’ performance and which features could be used to personalize students’ learning. Ultimately, students were mostly disinterested in the skill-related videos, preferring instead to use the platforms existing problem- specific support, and there was no statistically significant findings in either experiment. Additionally, while no video features were significantly predictive of students’ performance, two video fea- tures had significant qualitative interactions with students’ prior knowledge, which showed that different content creators were more effective for different groups of students. These findings can be used to inform the design of future video-based features within online learning platforms and the creation of different educational videos specifically targeting higher or lower knowledge students. The data and code used in this work is hosted by the Open Science Foundation. 

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