Python
Inventing Curriculum using Python and spaCy
February 7, 2021
4:00 PM – 4:30 PM Add to calendar
4:00 PM – 4:30 PM Add to calendar
D.python
Are you an educator who wants to design teach an industry-aligned curriculum? Then you have come to the right place. In this talk, we will show how to design a better curriculum using natural language processing libraries in python, i.e., spaCy and Textacy.
The curriculum in the general and undergraduate curriculum, in particular, is one of the most important pillars of an education system. The undergraduate curriculum has two main objectives i.e. employability and higher education. The greatest challenge in designing an undergraduate curriculum is achieving a balance between employability skills and laying the foundation for higher education. Generally, the curriculum is a combination of core technical subjects, professional electives, humanities, and skill-oriented subjects. We used natural language processing and machine learning packages in Python to build a curriculum design system.
The steps to build a curriculum design system are described below:
1. The dataset was built from the job profiles from different job listing websites like stackoverflow.com, indeed.com, linkedin.com, and monster.com. Also from the syllabus of competitive exams and qualifying exams for higher education.
2. On the dataset, we applied natural language processing techniques to identify the subjects and subject content. For natural language processing, we used spaCy an industrial-strength Natural Language Processing package in Python.
3. To generate syllabus content for a particular subject, a pointer-generator network was used. The pointer generator network is a text summarization technique that combines extractive and abstractive summarization techniques. The extractive summarization technique extracts keywords from the dataset, whereas the abstractive summarization technique generates new text from the existing text. The pointer-generator network was implemented using the scikit-learn machine learning package in Python.
4. The generated curriculum was then compared with the existing curriculum to get insights like, how much percent of the curriculum is industry oriented, how much percent of the curriculum is aimed at higher education and job-oriented skills. At this step, we used the ROGUE (Recall-Oriented Understudy Gisting Evaluation) metric to compare the generated curriculum against the reference/proposed curriculum
5. The above steps can be repeated with modified parameters to get better insights and curriculum.
This also gives us an idea of how we can have an evolving curriculum that can help us bridge the gap between industry and academia.
Additional information
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