ML and DL in Education Analytics

 

Machine Learning and Deep Learning in Education Analytics

Machine learning and deep learning are two powerful technologies that are revolutionizing the field of education analytics. By analyzing large amounts of data, machine learning and deep learning can be used to identify patterns and trends that would be impossible to see with the naked eye. This information can then be used to improve student learning outcomes in a variety of ways.

For example, machine learning can be used to predict which students are at risk of failing a course. This information can then be used to provide those students with additional support, such as tutoring or extra help sessions. Machine learning can also be used to personalize learning experiences for each student. By understanding each student's individual learning style and needs, machine learning can be used to provide them with the most effective learning materials and activities.

Deep learning is a more advanced form of machine learning that can be used to learn complex patterns from data. This makes deep learning ideal for tasks such as natural language processing and image recognition. In education, deep learning can be used to develop new tools for assessing student learning, such as automatic essay graders and facial recognition software that can identify students who are struggling.

Machine learning and deep learning are still relatively new technologies, but they have the potential to revolutionize the way we teach and learn. By harnessing the power of these technologies, we can create more personalized and effective learning experiences for all students.

Here are some of the ways that machine learning and deep learning are being used in education analytics:

a)     Predicting student performance: Machine learning algorithms can be used to predict which students are at risk of failing. This information can then be used to provide early intervention and support to those students.

b)     Personalizing instruction: Machine learning algorithms can be used to personalize instruction for each student. This can be done by tailoring the content, difficulty level, and pace of instruction to each student's individual needs.

c)     Recommending resources: Machine learning algorithms can be used to recommend resources to students. This can include books, articles, videos, and other educational materials.

d)     Identifying cheating: Machine learning algorithms can be used to identify cheating on exams and other assessments. This information can then be used to take appropriate action.

Here are some examples of how machine learning and deep learning are being used in education analytics today:

a)     Predicting student dropout: In 2016, researchers at Stanford University used machine learning to predict which students were at risk of dropping out of college. The algorithm was able to identify students who were more likely to drop out with an accuracy of 84%.

b)     Personalizing instruction: In 2017, researchers at Carnegie Mellon University used deep learning to personalize instruction for students in a math course. The algorithm was able to adapt the content, difficulty level, and pace of instruction to each student's individual needs.

c)     Recommending resources: In 2018, researchers at the University of California, Berkeley used machine learning to recommend resources to students in a STEM course. The algorithm was able to recommend resources that were relevant to each student's interests and learning needs.

d)     Identifying cheating: In 2019, researchers at the University of Illinois at Urbana-Champaign used machine learning to identify cheating on exams. The algorithm was able to identify cheaters with an accuracy of 95%.

There are many popular Python libraries used in Education Analytics. Some of the most popular libraries include:

  • Pandas: Pandas is a powerful data analysis library that can be used to clean, manipulate, and analyze data.
  • NumPy: NumPy is a library for scientific computing that provides high-performance numerical arrays and functions.
  • Scikit-learn: Scikit-learn is a machine learning library that provides a wide range of machine learning algorithms.
  • Matplotlib: Matplotlib is a plotting library that can be used to create graphs and charts.
  • Seaborn: Seaborn is a visualization library that builds on top of Matplotlib to provide a more concise and attractive way to create visualizations.

These are just a few examples of how machine learning and deep learning are being used in education analytics today. As these technologies continue to develop, we can expect to see even more innovative applications in the years to come.

 

Nashat Ali

Business Analytics Intern at Hunnarvi Technology Solutions in collaboration with nanobi analytics

 

**VIEWS ARE PERSONAL**

 

References

https://www.sisense.com/glossary/educational-analytics/

https://www.sas.com/en_in/industry/education.html

 

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