Analysis scripts (R / Rmd) are located in the
They are numbered in the intended order of execution.
01_collect_data.Rreads in the raw CSV files and creates usable data frames for the rest of the analysis.
02_calculate_model_estimates.Rreconstructs the relevant parameter values of the adaptive fact learning model (incl. rate of forgetting).
03_descriptive_stats_session1.Rmdis a notebook with descriptives of performance in the first experimental session.
04_alpha_analysis.Rmdis a notebook analysing the rate of forgetting estimates obtained in the first session.
05_generate_fact_predictions.Rmakes fact- and domain-level predictions of rate of forgetting to be used in the second session.
06_plot_examples.Rcreates some visualisations of session 1 rate of forgetting estimates and model predictions.
07_make_session2_predictions.Ruses the data from session 1 and the first block of session 2 to reconstruct the predictions used in the second block of session 2.
08_visualise_predictions.Rplots the prediction matrix and map shown on the poster.
09_session2_H1_learning_outcomes.Rmdis a notebook testing Hypothesis 1 (improved learning outcomes).
10_session2_H2_prediction_accuracy_1.Rmdis a notebook testing Hypothesis 2 (more accurate predictions when combining learner and fact information).
11_session2_H3_prediction_accuracy_2.Rmdis a notebook testing Hypothesis 3 (more accurate predictions when predicting individual facts and/or learners instead of making a domain-level prediction).
bayes_funs.Rimplements the Bayesian model functions.
geom_flat_violin.Rimplements the half-violin plot that is used in some of the figures.
slimstampen_model_funs.Rimplements the adaptive fact learning model functions.
Figures generated by the analysis scripts can be found in the
The stimuli used in both sessions are located in the