Riccardo Zucca | Search arXiv

arXiv2018

Hints vs Distractions in Intelligent Tutoring Systems: Looking for the proper type of help

The kind of help a student receives during a task has been shown to play a significant role in their learning process. We designed an interaction scenario with a robotic tutor, in real-life settings based on an inquiry-based learning task. We aim to explore how learners' performance is affected by the various strategies of a robotic tutor. We explored two kinds of(presumable) help: hints (which were specific to the level or general to the task) or distractions (information not relevant to the task: either a joke or a curious fact). Our results suggest providing hints to the learner and distracting them with curious facts as more effective than distracting them with humour.

cs.HC↗

arXiv2017

Scaling Properties of Human Brain Functional Networks

We investigate scaling properties of human brain functional networks in the resting-state. Analyzing network degree distributions, we statistically test whether their tails scale as power-law or not. Initial studies, based on least-squares fitting, were shown to be inadequate for precise estimation of power-law distributions. Subsequently, methods based on maximum-likelihood estimators have been proposed and applied to address this question. Nevertheless, no clear consensus has emerged, mainly because results have shown substantial variability depending on the data-set used or its resolution. In this study, we work with high-resolution data (10K nodes) from the Human Connectome Project and take into account network weights. We test for the power-law, exponential, log-normal and generalized Pareto distributions. Our results show that the statistics generally do not support a power-law, but instead these degree distributions tend towards the thin-tail limit of the generalized Pareto model. This may have implications for the number of hubs in human brain functional networks.

q-bio.NC↗