arXiv subjects

Guilherme Lichand

Publications and source records attributed to Guilherme Lichand.

3 recordsLinked to original sources

arXiv2026

Revisiting the Regularity of Student Learning Rate: Sensitivity to Which Observations Are Included

Mixed-effects models fit to observational practice data are widely used in learning analytics to estimate student-level variation in initial knowledge and learning rate, and the resulting estimates increasingly inform substantive claims about learners. We examine whether such estimates can be read as properties of learners or whether they depend on choices about which observations the model is fit to. As a case study, we revisit the ``astonishing regularity'' reported by Koedinger et al. (2023): that students vary substantially in initial knowledge but much less in learning rate. The finding is based on fits of the individual Additive Factors Model (iAFM) to 27 educational datasets, and rests on a model-derived estimate of student-level learning-rate variation being small in absolute terms. We refit the same model on the same datasets under two specifications, each varying how much of each student's practice on a given skill is used in fitting. The estimate of student-level variation in initial knowledge stays approximately stable across both specifications. The estimate of student-level variation in learning rate does not: it inflates by a median of 118\% under one specification and is several times larger under the other. The same model, fit to the same data, returns substantially different estimates of how much students vary in learning rate depending on which observations are included. When estimates from mixed-effects models on observational practice data are used to support substantive claims about learners, sensitivity to such choices deserves a central place in how those estimates are reported and read.

cs.CY↗

arXiv2020

Using wearable proximity sensors to characterize social contact patterns in a village of rural Malawi

Measuring close proximity interactions between individuals can provide key information on social contacts in human communities. With the present study, we report the quantitative assessment of contact patterns in a village in rural Malawi, based on proximity sensors technology that allows for high-resolution measurements of social contacts. The system provided information on community structure of the village, on social relationships and social assortment between individuals, and on daily contacts activity within the village. Our findings revealed that the social network presented communities that were highly correlated with household membership, thus confirming the importance of family ties within the village. Contacts within households occur mainly between adults and children, and adults and adolescents. This result suggests that the principal role of adults within the family is the care for the youngest. Most of the inter-household interactions occurred among caregivers and among adolescents. We studied the tendency of participants to interact with individuals with whom they shared similar attributes (i.e., assortativity). Age and gender assortativity were observed in inter-household network, showing that individuals not belonging to the same family group prefer to interact with people with whom they share similar age and gender. Age disassortativity is observed in intra-household networks. Family members congregate in the early morning, during lunch time and dinner time. In contrast, individuals not belonging to the same household displayed a growing contact activity from the morning, reaching a maximum in the afternoon. The data collection infrastructure used in this study seems to be very effective to capture the dynamics of contacts by collecting high resolution temporal data and to give access to the level of information needed to understand the social context of the village.

physics.soc-ph↗

arXiv2020

Using Nudges to Prevent Student Dropouts in the Pandemic

The impacts of COVID-19 reach far beyond the hundreds of lives lost to the disease; in particular, the pre-existing learning crisis is expected to be magnified during school shutdown. Despite efforts to put distance learning strategies in place, the threat of student dropouts, especially among adolescents, looms as a major concern. Are interventions to motivate adolescents to stay in school effective amidst the pandemic? Here we show that, in Brazil, nudges via text messages to high-school students, to motivate them to stay engaged with school activities, substantially reduced dropouts during school shutdown, and greatly increased their motivation to go back to school when classes resume. While such nudges had been shown to decrease dropouts during normal times, it is surprising that those impacts replicate in the absence of regular classes because their effects are typically mediated by teachers (whose effort in the classroom changes in response to the nudges). Results show that insights from the science of adolescent psychology can be leveraged to shift developmental trajectories at a critical juncture. They also qualify those insights: effects increase with exposure and gradually fade out once communication stops, providing novel evidence that motivational interventions work by redirecting adolescents' attention.

econ.GN↗