Francesco Sella

  “One”, ●●, 3, Four: The Emergence of Early Numerical Knowledge  Chair: Francesco Sella (1), Attila Krajcsi (2)  Early numerical knowledge strongly predicts later mathematical achievement. Yet, we have an unclear picture of how children progressively acquire such knowledge. This symposium brings together four studies that have implemented specific numerical and non-numerical tasks to unveil how different numerical competencies develop and interact. We provide evidence of the limited number knowledge in 5- and 6-knowers (Talk 1), the role of inhibition in moderating the relation between non-symbolic and symbolic number skills (Talk 2), the influence of non-symbolic and symbolic mapping on maths achievement (Talk 3), and the role of number ordering in early numerical development (Talk 4).  Keywords: Early numeracy, Counting, Preschool children, Number development, Symbolic number processing Intro slide.

  Title: Learning the number sequence: preschool children make sense of numbers Here you can find the slides of my talk.

Here you can find the slides of my talk at MEC seminar planned on the 6th of May 2020.

10th annual BCCCD meeting Budapest, Hungary  January 9-11, 2020. Poster Session B  Date: 10th January Time: 14:30 - 16:30 Location: First floor Poster:  PB.e-037 A-0111  Learning the number sequence: preschool children make sense of numbers Francesco Sella 1 , Daniela Lucangeli 2 , Roi Cohen Kadosh 3 , Marco Zorzi 2 1 University of Sheffield, UK; 2 University of Padova, Italy; 3 University of Oxford, UK. Abstract: Learning how symbolic numbers represent exact quantities is a cornerstone in the development of early numeracy skills. We explored how the mastering of different numerical concepts relate to symbolic magnitude knowledge, as assessed in number comparison tasks. We used a give-a-number task to classify preschool children in subset-knowers and cardinal-principle (CP) knowers. We implemented a direction task to assess children’s understanding of the directional property of the counting list: that is, adding one item to a set leads to the nex...

Below you can find the R code to create a publication-ready plot. You can change the details of the theme() functions to tail the plot to your needs. # Loading required libraries library(ggplot2) library(grid) # Load the function theme_science1 <- function(base_size = 18,                            facet_border_col = "white",                            legend_justification = c(0,1),                            legend_position = c(0,1)){   theme_bw(base_size = base_size) +   theme(axis.line = element_line(size = 0.7, color = 'black'),             panel.border = element_blank(),             panel.grid.major = element_line(size = 0.1, color = "grey90", linetype = "dotted"),       ...

There are several ways to create an output of regression analysis in R. This is a simple custom made function. # Clear all rm(list=ls()) # Libraries library(QuantPsyc) # Regression summary function table_lm <- function(lm_object, digit_rounding){   summary_lmobject = summary(lm_object)   df_output = data.frame(   predictors = names(lm_object$coefficients),   beta = round(as.numeric(summary_lmobject$coefficients[,1]), digit_rounding),   SE = round(as.numeric(summary_lmobject$coefficients[,2]), digit_rounding),   SE95ci_low = round(as.numeric(confint(lm_object, level = 0.95)[,1]),digit_rounding),   SE95ci_high = round(as.numeric(confint(lm_object, level = 0.95)[,2]),digit_rounding),   stdbeta = c(NA,round(lm.beta(lm_object), digit_rounding)),   ttest = round(summary_lmobject$coefficients[,3], digit_rounding),   pvalues = sapply(as.numeric(summary_lmobject$coefficients[,4]),             ...

A quick code to create a useful plot to visualise pre- and post-test results. I was inspired by this  post , this  post , and this  post . A scatterplot between pre-test and post-test scores for the control and the training group is presented. The straight black lines represent correlations between pre-test and post-test scores within the two groups. The straight lightgrey line represents the same values at pre-test and post-test –> Subjects above the line display a better performance at post-test compared to pre-test. At the margins, the boxplots are reported as well as the mean (i.e., diamond) and 95% confidence intervals (i.e., error bars). In the case of the post-test scores (plot on the right margin), the adjusted means (i.e., black triangles) and associated 95% confidence intervals (i.e., error bars) are reported. The table reports the results for the ANCOVA and its assumptions. In this example, participants in the training group show an improvement in a Working ...