CERSEI: Cognitive Effort Based Recommender System for Enhancing Inclusiveness

Abstract

Awarding gaps have been commonly observed between different socio-demographic categories of students, especially in the domains of sociology and learning science. Recent research has shown that using Learning Analytics models could be exploited to reduce these gaps, and therefore contribute to making the learning process more inclusive and equitable. This demonstration paper presents CERSEI, a new web-based learning prototype that aims to enhance inclusiveness by exploiting two Learning Analytics models: a cognitive effort model and an activity recommender built upon the cognitive effort model. Previous research has indeed shown a strong interplay between socio-economic status, effort and motivation, e.g., families from higher socio-economic status tend to mobilize more resources to prevent their children from falling down the social ladder. Some categories of students might therefore have fewer sources of motivation and exert less effort, or a higher tendency to exert effort on specific activities that are not the most relevant for succeeding. CERSEI allows students to track their effort by assigning ratings on their activities using the RSME scale and to receive engaging recommendations of learning activities. This will allow us to collect the relevant data to better understand how effort is exerted by different categories of students and how recommendations can impact them. Based on the outcomes of the related analysis, we will then aim at creating better Learning Analytics models. We expect that these models will help to provide more inclusive and equitable learning.