Building a student effort dataset: what can we learn from behavioral and physiological data

Barbara Moissa (1) , Geoffray Bonnin (1) , Anne Boyer (1)

1 KIWI - Knowledge Information and Web Intelligence

Decades of studies have shown that student's success is strongly dependent on their effort. Recently, this concept made its way into the domain of Learning Analytics. One of the major difficulties of these works is to correctly define the effort and to find relevant means of measuring it. Our approach is based on the Cognitive Load Theory, which provides a theoretical background issued from Learning Sciences, desired by the Learning Analytics domain. The cognitive load is a multidimensional construct that represents the load that performing a given task imposes on the cognitive system, and is often considered by researchers as being equivalent to mental effort. The cognitive load has long been studied in educational sciences, and several types of measures have been proposed that can be classified into four categories: (1) subjective measures, i., students' perceived effort, (2) performance measures, e.g., the outcome of student work assessments, (3) physiological measures, such as pupil dilation and heart rate, and (4) behavioral measures, such as points of fixations, and keyboard and mouse usage. In an exploratory work, we proposed a new cognitive load measurement model based on behavioral data. Our data consisted in keyboard and mouse usage, as well as page views and fixation points from an eye tracker, and were collected in the context of an online Esperanto course. Our results showed that eye tracking data provided a better indication of effort than keyboard, mouse and page view data, and that a slight complementarity exists between these two types of information. In the same spirit, Larmuseau et al. (2019) investigated the correlation between the cognitive load and two physiological measures from smart watches: skin conductance and skin temperature. The participants were future school teachers taking a course as part of their training. One of their main findings is a moderate correlation between effort and skin conductance. However, both these last approaches are preliminary and only focused on small samples (less than 20 participants).

Domaines: Informatique [cs]

Barbara Moissa, Geoffray Bonnin, Anne Boyer. Building a student effort dataset: what can we learn from behavioral and physiological data. Learning & Student Analytics Conference, 2019, Nancy, France. ⟨hal-02476965⟩