The effect of face-to-face versus online learning on student performance in anatomy: An observational study using a causal inference approach

Joanna Diong¹, Hopin Lee², Darren Reed¹

1. School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney
2. Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford

Suggested citation

Diong J, Lee H, Reed D (2022) The effect of face-to-face versus online learning on student performance in anatomy: An observational study using a causal inference approach. Discover Education (in press).

Protocol registration

Protocol registration on the Open Science Framework (OSF): https://osf.io/ws8mv

OSF project repository: https://osf.io/xhs83/

Documents

Stored in doc:

• notes.pdf: Project study notes on the thinking process behind development and refinement of the causal graph
• causal_graph.txt: DAGitty code to generate the causal graph

Data

De-identified processed data of examination marks for undergraduate and postgraduate cohorts are available in data -> proc:

• bios1168_proc_.csv
• bios5090_proc_.csv

De-identified raw data of introductory histology marks for the postgraduate unit are available in data -> raw:

• BIOS5090_histology.csv

Code

Python (v3.9) and R (v4.2.2) code files were written by Joanna Diong.

Python files

script: Main script to run analysis.

proc, plot, utils: Modules containing functions used to clean data and plot figures.

Running Python code

A reliable way to reproduce the analysis would be to run the code in an integrated development environment for Python (e.g. PyCharm).

Create a virtual environment and install dependencies. Assuming you are running off Python on an Anaconda distribution or similar, using the Terminal (Mac or Linux, or PyCharm Terminal),

python -m venv env

Next, activate the virtual environment.

For Mac or Linux,

source env/bin/activate

For Windows,

.\env\Scripts\activate

Then, install dependencies,

pip install -r requirements.txt

Run script.py.

R files

script: Main script to run analysis to obtain E-values.

Running R code

Run script.R.

Output

Python generated files, saved in data -> proc:

• bios1168_clean_.csv
• bios1168_result_.txt
• bios1168.pdf figure file
• bios5090_clean_.csv
• bios5090_result_.txt
• bios5090.pdf figure file

R generated file, saved in data -> proc:

• evalues.txt