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Faucon, Louis; Olsen, Jennifer K.; Haklev, Stian; Dillenbourg, Pierre – Journal of Learning Analytics, 2020
In classrooms, some transitions between activities impose (quasi-)synchronicity, meaning there is a need for learners to move between activities at the same time. To make real-time decisions about when to move to the next activity, teachers need to be able to balance the progress of their students as they work at different paces. In this paper, we…
Descriptors: Classroom Techniques, Prediction, Learning Activities, Student Behavior
Boroujeni, Mina Shirvani; Dillenbourg, Pierre – Journal of Learning Analytics, 2019
The large-scale and granular interaction data collected in online learning platforms such as massive open online courses (MOOCs) provide unique opportunities to better understand individuals' learning processes and could facilitate the design of personalized and more effective support mechanisms for learners. In this paper, we present two…
Descriptors: Online Courses, Large Group Instruction, Learning Processes, Study Habits
Sharma, Kshitij; Chavez-Demoulin, Valérie; Dillenbourg, Pierre – Journal of Learning Analytics, 2017
The statistics used in education research are based on central trends such as the mean or standard deviation, discarding outliers. This paper adopts another viewpoint that has emerged in statistics, called extreme value theory (EVT). EVT claims that the bulk of normal distribution is comprised mainly of uninteresting variations while the most…
Descriptors: Foreign Countries, Educational Research, Statistical Distributions, Theories