Exploring students learning approaches in MOOCs

This study aims at understanding different students approaches for solving assignments in MOOCs. It makes use of a large dataset of logs from students interaction with the MOOC platform Coursera on a course about functional programming with Scala. In total more than 10.000 students participated in the assignments. Learning approaches are divided in two categories: starting with video lectures (V) and start- ing with the assignment (A); and students are divided in three groups: those applying purely the approach V , those applying purely the approach A and mixed-approach student who can apply both approaches. We explore how our grouping correlates with assignment grades, number of submissions, time between submissions and overall performance. Significant difference has been found only on overall performance, while all three groups appear very similar on the other measures. Then we search correlations with approach changes for mixed-approach students. We observed that students are more likely to stay with the same approach, found significant difference on the starting time of learning activity sequences, but not on the time of student’s first assignment submission. We found no correlation between the approach choice and the grade or number of submissions on the previous assignment

Multi-Armed Bandits for Addressing the Exploration/Exploitation Trade-off in Self Improving Learning Environment

This project proposes the use of machine learning techniques such as Multi-Armed Bandits to implement self-improving learning environments. The goal of a self-improving learning environment is to perform good pedagogical choices while measuring the efficiency of these choices. The modeling of students is done using the LFA model and fitted on a dataset of university courses to allow to simulate students. Three experiments with simulated students are carried out and show that the Multi-Armed Bandit approach improves learning outcomes

Iterative Classroom Teaching

We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rates. We prove that a teacher with full knowledge about the learning dynamics of the students can teach a target concept to the entire classroom using O(min{d,N} log(1/eps)) examples, where d is the ambient dimension of the problem, N is the number of learners, and eps is the accuracy parameter. We show the robustness of our teaching strategy when the teacher has limited knowledge of the learners' internal dynamics as provided by a noisy oracle. Further, we study the trade-off between the learners' workload and the teacher's cost in teaching the target concept. Our experiments validate our theoretical results and suggest that appropriately partitioning the classroom into homogenous groups provides a balance between these two objectives.Comment: AAAI'19 (extended version

Semi-Markov model for simulating MOOC students

Large-scale experiments are often expensive and time consuming. Although Massive Online Open Courses (MOOCs) provide a solid and consistent framework for learning analytics, MOOC practitioners are still reluctant to risk resources in experiments. In this study, we suggest a methodology for simulating MOOC students, which allow estimation of distributions, before implementing a large-scale experiment. To this end, we employ generative models to draw independent samples of artificial students in Monte Carlo simulations. We use Semi-Markov Chains for modeling student's activities and Expectation-Maximization algorithm for fitting the model. From the fitted model, we generate simulated students whose processes of weekly activities are similar to these of the real students

Les carences nutritionnelles dans les PVD

Les caractéristiques anthropométriques (poids, taille) de 373 enfants congolais d'âge préscolaire issus de milieux favorisés de Brazzaville ont été relevées. La distribution des critères anthropométriques est très voisine de celle de la population de référence NCHS qu'il s'agisse du poids ou de la taille en fonction de l'âge. Les retards de taille et les maigreurs sont des phénomènes peu fréquents et d'apparition brève contrairement à ce qui est observé dans d'autres échantillons représentatifs de populations urbaines et rurales. Ainsi, les différences ethniques ou raciales s'avèrent négligeables par rapport à celles liées à l'environnement. (Résumé d'auteur

Enquête nationale sur l'état nutritionnel des enfants d'âge préscolaire au Congo

Une enquête nutritionnelle nationale a été réalisée au Congo en milieu rural en 1987 sur un échantillon représentatif des enfants de moins de cinq ans. Dans une perspective de surveillance nutritionnelle, son objectif principal était de fournir des données de base pour l'orientation et l'évaluation ultérieure d'impact du programme national d'éducation nutritionnelle et de surveillance de la croissance (NUTED). Les résultats essentiels portent ainsi sur : l'estimation de l'état nutritionnel mesuré par des indices anthropométriques standardisés; le type, l'ampleur et la distribution des malnutritions; la recherche de facteurs associés aux malnutritions en vue d'une identification des groupes et zones à risque. D'autres données sont également présentées dans les domaines d'intervention de NUTED : modes alimentaires et conduite du sevrage; diarrhées et réhydratation par voie orale; surveillance de la croissance. Sur la base des résultats obtenus des recommandations sont formulées. (Résumé d'auteur

Orchestration Graphs: Enabling Rich Social Pedagogical Scenarios in MOOCs

One of the initial promises of MOOCs was to enable participants from around the world to learn and build knowledge together, however existing MOOC platforms are very limited in their collaborative functionality. Using a recent educational modeling language which can express a broad diversity of educational scenarios, we present a technical infrastructure design and prototype which enables instructors to design and run pedagogically rich and therefore complex scenarios. We present this as a theoretical and technical contribution to support a broad program of research and innovation related to collaborative learning at scale

Recommendation Algorithms, a Neglected Opportunity for Public Health

The public discussion on artificial intelligence for public health often revolves around future applications like drug discovery or personalized medicine. But already deployed artificial intelligence for content recommendation, especially on social networks, arguably plays a far greater role. After all, such algorithms are used on a daily basis by billions of users worldwide. In this paper, we argue that, left unchecked, this enormous influence of recommendation algorithms poses serious risks for public health, e.g., in terms of misinformation and mental health. But more importantly, we argue that this enormous influence also yields a fabulous opportunity to provide quality information and to encourage healthier habits at scale. We also discuss the philosophical, technical and socio-economical challenges to seize this immense opportunity, and sketch the outlines of potential solutions. In particular, we argue that it would be extremely helpful if public and private institutions could publicly take a stand, as this may then generate the necessary social, economical and political pressure to massively invest in the research, development and deployment of the potential solutions

FROG: rapid prototyping of collaborative learning scenarios

We describe FROG, an integrated environment for authoring and running collaborative learning scenarios, called Orchestration Graphs. We describe the pedagogical background and the technical architecture, and present a case study of a teacher using FROG to experiment with a variation of a jigsaw script

Tournesol: Permissionless Collaborative Algorithmic Governance with Security Guarantees

Recommendation algorithms play an increasingly central role in our societies. However, thus far, these algorithms are mostly designed and parameterized unilaterally by private groups or governmental authorities. In this paper, we present an end-to-end permissionless collaborative algorithmic governance method with security guarantees. Our proposed method is deployed as part of an open-source content recommendation platform https://tournesol.app, whose recommender is collaboratively parameterized by a community of (non-technical) contributors. This algorithmic governance is achieved through three main steps. First, the platform contains a mechanism to assign voting rights to the contributors. Second, the platform uses a comparison-based model to evaluate the individual preferences of contributors. Third, the platform aggregates the judgements of all contributors into collective scores for content recommendations. We stress that the first and third steps are vulnerable to attacks from malicious contributors. To guarantee the resilience against fake accounts, the first step combines email authentication, a vouching mechanism, a novel variant of the reputation-based EigenTrust algorithm and an adaptive voting rights assignment for alternatives that are scored by too many untrusted accounts. To provide resilience against malicious authenticated contributors, we adapt Mehestan, an algorithm previously proposed for robust sparse voting. We believe that these algorithms provide an appealing foundation for a collaborative, effective, scalable, fair, contributor-friendly, interpretable and secure governance. We conclude by highlighting key challenges to make our solution applicable to larger-scale settings.Comment: 31 pages, 5 figure