e-Babylab: An open-source browser-based tool for unmoderated online developmental studies.

The COVID-19 pandemic massively changed the context and feasibility of developmental research. This new reality, as well as considerations about sample diversity and naturalistic settings for developmental research, highlights the need for solutions for online studies. In this article, we present e-Babylab, an open-source browser-based tool for unmoderated online studies targeted for young children and babies.

Tablet assessment of word comprehension reveals coarse word representations in 18-⁠20-month-old toddlers.

The present study explores the viability of using tablets in assessing early word comprehension by means of a two-alternative forced-choice task. Forty-nine 18-20-month-old Norwegian toddlers performed a touch-based word recognition task, in which they were prompted to identify the labeled target out of two displayed items on a touchscreen tablet. In each trial, the distractor item was either semantically related (e.

Word learning from a tablet app: Toddlers perform better in a passive context.

In recent years, the popularity of tablets has skyrocketed and there has been an explosive growth in apps designed for children. Howhever, many of these apps are released without tests for their effectiveness. This is worrying given that the factors influencing children's learning from touchscreen devices need to be examined in detail.

A Bayesian-Inspired Item Response Theory-Based Framework to Produce Very Short Versions of MacArthur-Bates Communicative Development Inventories.

Purpose This study introduces a framework to produce very short versions of the MacArthur-Bates Communicative Development Inventories (CDIs) by combining the Bayesian-inspired approach introduced by Mayor and Mani (2019) with an item response theory-based computerized adaptive testing that adapts to the ability of each child, in line with Makransky et al. (2016). Method We evaluated the performance of our approach-dynamically selecting maximally informative words from the CDI and combining parental response with prior vocabulary data-by conducting real-data simulations using four CDI versions having varying sample sizes on Wordbank-the online repository of digitalized CDIs: American English (a very large data set), Danish (a large data set), Beijing Mandarin (a medium-sized data set), and Italian (a small data set).