Explaining Student Behavior at Scale: The Influence of Video Complexity on Student Dwelling Time

TY - GEN

T1 - Explaining Student Behavior at Scale

T2 - 3rd Annual ACM Conference on Learning at Scale, L@S 2016

AU - Van der Sluis, Frans

AU - Ginn, Jasper

AU - Van der Zee, Tim

PY - 2016/4/25

Y1 - 2016/4/25

N2 - Understanding why and how students interact with educational videos is essential to further improve the quality of Massive Online Open Courses (MOOCs). In this paper, we look at the complexity of videos to explain two related aspects of student behavior: the dwelling time (how much time students spend watching a video) and the dwelling rate (how much of the video they actually see). Building on a strong tradition of psycholinguistics, we formalize a definition for information complexity in videos. Furthermore, building on recent advancements in time-on-task measures we formalize dwelling time and dwelling rate based on click-stream trace data. The resulting computational model of video complexity explains 22.44% of the variance in the dwelling rate for students that finish watching a paragraph of a video. Video complexity and student dwelling show a polynomial relationship, where both low and high complexity increases dwelling. These results indicate why students spend more time watching (and possibly contemplating about) a video. Furthermore, they show that even fairly straightforward proxies of student behavior such as dwelling can already have multiple interpretations; illustrating the challenge of sense-making from learning analytics.

AB - Understanding why and how students interact with educational videos is essential to further improve the quality of Massive Online Open Courses (MOOCs). In this paper, we look at the complexity of videos to explain two related aspects of student behavior: the dwelling time (how much time students spend watching a video) and the dwelling rate (how much of the video they actually see). Building on a strong tradition of psycholinguistics, we formalize a definition for information complexity in videos. Furthermore, building on recent advancements in time-on-task measures we formalize dwelling time and dwelling rate based on click-stream trace data. The resulting computational model of video complexity explains 22.44% of the variance in the dwelling rate for students that finish watching a paragraph of a video. Video complexity and student dwelling show a polynomial relationship, where both low and high complexity increases dwelling. These results indicate why students spend more time watching (and possibly contemplating about) a video. Furthermore, they show that even fairly straightforward proxies of student behavior such as dwelling can already have multiple interpretations; illustrating the challenge of sense-making from learning analytics.

KW - dwelling time, information complexity, learning analytics, moocs, student behavior., video

U2 - 10.1145/2876034.2876051

DO - 10.1145/2876034.2876051

M3 - Article in proceedings

SN - 978-1-4503-3726-7

T3 - L@S '16

SP - 51

EP - 60

BT - Proceedings of the Third (2016) ACM Conference on Learning @ Scale

PB - ACM

CY - New York, NY, USA

Y2 - 25 April 2016 through 26 April 2016

ER -