This repository holds the code that I used in a study to investigate the effects of studying in Dutch versus studying in English. I looked at students' language development in the language in which they study, as well as the influence the study language has on the students' grades, obtained number of European Credits (ECs) and drop-out rates. The outcomes of this results are reported in two chapters in my PhD thesis, which can be downloaded here.

Some additional info on how to use the script preprocessing.py:

This is a command line script which does the following:

• Reading the raw files in the data/raw_data directory
• Outputting one file per subject in the data/indiv_files directory. This file contains the subject's answer in the format lemma_POStag lemma_POStag etc. The data directory is untracked by Git.
• Reading the files from indiv_files and calculating their lexical complexity, by calling lca.py, which is a revised version of the original folder-lc.py created by Xiaofei Lu (2013). The lexical complexity is written to one results file per exam, in the results directory.

The script should be called from the command line in the following format: python preprocessing.py {language} {minimal sample size for LCA}

For example: python3 preprocessing.py EN 50

The code can also be run from within Spyder, as long as the command line options have been entered in Run>Configuration per file.

Preprocessing of the Dutch data requires Frog, which is part of the LaMachine NLP software distribution.