Semester Project in the ML4ED (Machine Learning for Education) lab at EPFL (Sept. 2023 - Jan. 2024)
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Educational Benefits of Reflective Writing: Reflection on one’s performance through reflective writing offers substantial educational benefits.
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Challenges in Reflective Writing: Students need a very personnalized feedback from the educators and educators face complexities in teaching and assessing reflective writings.
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ML and NLP Solutions: ML and NLP offer potential solutions to challenges in reflective writing by automating the feedback provision.
- Labeling Hurdles in Education: Grapple with the challenges of labeling large datasets in the educational context, demanding expert involvement and high pedagogical knowledge.
- Semi-Supervised Learning with Self-Training: Explore the potential of self-learning, a semi-supervised approach that overcomes labeling challenges by leveraging small amounts of labeled data in multi-class text classification, specifically in the nuanced analysis of reflective writings.
Can semi-supervised approaches reduce human effort in labeling reflective writings?
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Baseline with BERT Models: Establish baselines for multi-class and binary classification on labeled reflection datasets.
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Learning Curves Analysis: Explore the influence of dataset size on model performance and confidence.
EDA_notebook: notebook for the exploratory data analysis of the CeRED dataset.notebook_kaggle: notebook used to experiment and test the functions before running the code on the cluster- run_to_cluster : directory with all the files necessary to run the code on the cluster (see README for more information).
- results_visualization : directory containing 2 jupyter notebooks to visualize the results (saved in the directory
results txt files) after running the code on the cluster. - results txt files : results saved on txt files. Can be directly reused for visualization in the notebooks in
results_visualization- Multi_Class_CLF
- Multiple_Bin_CLF
The full report of this project can be found here.