Summary of Bert-based Approach For Automating Course Articulation Matrix Construction with Explainable Ai, by Natenaile Asmamaw Shiferaw et al.
BERT-Based Approach for Automating Course Articulation Matrix Construction with Explainable AI
by Natenaile Asmamaw Shiferaw, Simpenzwe Honore Leandre, Aman Sinha, Dillip Rout
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the application of BERT-based models for assessing the alignment between course outcomes (CO) and program-specific outcomes (PSO) in education. The authors experiment with four BERT models – BERT Base, DistilBERT, ALBERT, and RoBERTa – using multiclass classification to evaluate the CO-PO/PSO pairs. Traditional machine learning classifiers are also evaluated, followed by transfer learning for the BERT models. To provide transparency into the decision-making process, Explainable AI (XAI) is applied through Local Interpretable Model-agnostic Explanations (LIME). The system achieves high performance metrics – accuracy, precision, recall, and F1-score values of 98.66%, 98.67%, 98.66%, and 98.66%, respectively. This work demonstrates the potential of utilizing BERT-based models for automated generation of Course Articulation Matrices (CAMs), offering high performance and interpretability in educational outcome assessment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses special language models to help educators make sure their curriculum is correct and working well. They tested four different models from the BERT family and found that they could use these models to create a system that accurately assesses how well course outcomes match up with program-specific goals. The system even provides explanations for its decisions, making it more transparent and reliable. |
Keywords
» Artificial intelligence » Alignment » Bert » Classification » F1 score » Machine learning » Precision » Recall » Transfer learning
