Skip to content

Latest commit

 

History

History
82 lines (55 loc) · 4.49 KB

README.md

File metadata and controls

82 lines (55 loc) · 4.49 KB
Raw

Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning

Inroduction

This repository contains a reference implementation for Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning, as accepted at the ACM Conference on Learning @ Scale (L@S ’24) and for Gaining Insights into Course Difficulty Variations Using Item Response Theory, as accepted at the 14th International Learning Analytics and Knowledge Conference 2024. If you use this implementation in academic work to detect differential course function, please cite the following paper:

  • Baucks*, F., Schmucker*, R., Borcher, C., Pardos, Z., Wiskott, L. (2024). Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning. In: To be updated (Eds.). Proceedings of the 11th ACM Conference on Learning @ Scale (L@S ’24). accepted.
@inproceedings{BaucksSchmucker2024DCF,
    author       = {Baucks*, Frederik and Schmucker*, Robin and Borchers, Conrad and Pardos, Zachary A. and Wiskott, Laurenz},
    title        = {Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning},
    booktitle    = {{Proceedings of the 11th ACM Conference on Learning @ Scale (L@S ’24)}},
    date         = {2024-07-18},
    year         = {2024},
    venue        = {Atlanta, GA, USA},
    editor       = {To be updated},
    note         = {accepted}
}

If you use this implementation to only employ item response theory and calculate course difficutlies, please cite the following paper:

  • Baucks*, F., Schmucker*, R., Wiskott, L. (2024). Gaining Insights into Course Difficulty Variations Using Item Response Theory. In LAK24: 14th International Learning Analytics and Knowledge Conference (LAK ’24). (pp. 450–461) New York, NY, USA: Association for Computing Machinery
@inproceedings{BaucksSchmucker2024IRT,
	author		=	{Baucks, Frederik and Schmucker, Robin and Wiskott, Laurenz},
	title		=	{Gaining Insights into Course Difficulty Variations Using Item Response Theory},
	booktitle	=	{LAK24: 14th International Learning Analytics and Knowledge Conference},
	pages		=	{450–461},
	publisher	=	{Association for Computing Machinery},
	address		=	{New York, NY, USA},
	month		=	{March},
	year		=	{2024},
	doi		=	{10.1145/3636555.3636902},
}

Formatting Student Response Data

First, a folder must be created in data/real for each degree (see e.g. 'data/real/a' or 'data/real/b' - 'a' and 'b' are artificial data). The response data must be stored in this folder in .csv format with sep=',' and filename taggedRInput.csv. The exact formatting can be seen in the example degrees 'a' and 'b'.

The responses are stored as a course-response matrix. The row names (index) correspond to the course names, the column names correspond to the student IDs.

EXCEPTION: The first column must be called 'time' and contain the semester names. These must be coded as 'SS10' or 'WS10/11'. SS stands for summer semester, 10 for the year 2010, and WS for winter semester, 10/11 for the years 2010/2011.

The entries in the matrix (except for the 'time' column) are then the responses of the students in the courses. Missing values are coded as -99999.

The data must be available for single degrees only. In the case of multiple degrees, the folders and data are automatically generated from the single-degree folders.

Quickstart

Follow these steps to set up your environment and quickly start the project:

Clone the Repository

To clone the repository, run the following command:

git clone https://github.com/FrederikBaucks/irt-course-evaluation.git
cd irt-course-evaluation

To install python packages and R libraries:

pip install -r config/python_packages
Rscript -e 'packages <- scan("config/R_libraries", what="", quiet=TRUE); install.packages(packages, repos="http://cran.us.r-project.org")'

Open notebooks/expl.ipynb

We share artificial data ('data/real/a' and 'data/real/b') to protect students' privacy. The artificial data has the same format and the expl.ipynb notebook illustrates the complete analysis workflow.

Run the cells in the notebook: notebooks/expl.ipynb!

When using your data: CUSTOMIZATION NEEDED in expl.ipynb! In the first cell of the notebook, the degree names need to be defined. In the case of a single degree as ['degree name', None] and in the case of multiple degrees as ['degree name 1', 'degree name 2']. In addition, the passing and failing grades must be specified.