This repository contains code written for the ESF-financed project Demokratisk Digitalisering. The project is a collaboration between Arbetsförmedlingen (AF) and Kungliga Tekniska Högskolan (KTH). The purpose of the project is to increase the digital competence - as defined at https://ec.europa.eu/jrc/en/digcomp - of people working at AF, and by extension also job seekers. This will be done by learning modules at KTH using OLI-Torus.

The current contents of the repository are:

README.md: This text

requirements.txt: File listing the Python modules used in this repository, and which versions I used.

factorial_experiment.py: Python module for analysing the results of a factorial experiment that we plan to do as part of the study. It can also be used for simulating similar studies, to test that the analysis at least makes sense on paper.

FE_demonstration_script.py: Script that demonstrates the use of the module factorial_experiment by implementing a simple simulated study. To make that easier, it has verbose comments explaining what happens at every step along the way.

FE_test_script.py: Script for testing that the module factorial_experiment actually works as intended.

FE_minimal_size_script.py: Script using the module factorial_experiment to try to find the minimal number of participants that we need in order to measure the effects that we are interested in.

KL_analysis_script.py: Script for analysing data from the learning module Kartläggning. The data must first be rewritten into a format readable by the Factorial_experiment module by the module preprocessing, which is found in the directory Utilities.

science_analysis_script.py: Script for analysing data for the article (name pending). The data comes from the learning module IT-säkerhet. The data must first be rewritten into a format readable by the Factorial_experiment module by the script PP_science_script which uses the module preprocessing, both in the directory Utilities.

science_survey_script.py: Script for analysing the answers to the survey given as part of the learning module IT-säkerhet.

science_time_script.py: Script for estimating the time participants spent on the learning module IT-säkerhet.

behavioural_experiment.py: Python module for simulating a behavioural experiment that we plan to do as part of the study. With time, I will also add code for analysing the results of the actual experiment.

BE_test_script.py: Script intended as a test of the module behavioural_experiment, by running some functions and plotting the results.

Pedagogics: Directory containing code written to demonstrate the underlying ideas behind our analysis.

Utilities: Directory containing code written to solve specific technical problems of no scientific interest.