Modelling students' effort using behavioral data

International audienceStudents' effort is often considered a key factor for students' success. It has several related definitions, none of which is widely adopted. In this paper, we define students' effort as the experienced cognitive load, which is the total amount of cognitive resources used during the execution of a given task. We propose an effort model to quantify students' effort based on this construct. Our approach uses behavioral measures (i.e., interaction and eye gaze data). Our preliminary results show that the eye gaze measures have an intermediary relationship with effort, while the interaction measures have a weak relationship with effort and seem slightly complementary to eye gaze measures

First Attempt to Predict User Memory from Gaze Data

International audienceMany recommenders compute predictions by inferring the users' preferences. However, in some cases, such as in e-education, the recommendations of pedagogical resources should rather be based on users' memory. In order to estimate in real time and with low involvement what has been recalled by users, we designed a user study to highlight the link between gaze features and visual memory. Our protocol consisted in asking different subjects to remember a large set of images. During this memory test, we collected about 19,000 fixation points. Among other results, we show in this paper a a strong correlation between the relative path angles and the memorized items. We then applied various classifiers and showed that it is possible to predict the users' memory status by analyzing their gaze data. This is the first step so as to provide recommendations that fits users' learning curve