Journal of Engineering Education Transformations

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Education Data Mining, Visualization and Sentiment Analysis of Coursera Course Review


Affiliations
1 U & P U. Patel Department of Computer Engineering., India
2 Department of Computer Science & Engineering, Chandubhai S. Patel Institute of Technology, Faculty of Technology & Engineering, Charotar University of Science and Technology, Changa – 388421, Gujarat, India
     

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Objective: No Decisions are good or bed they are taken based on the available data. It is very much essential to represent the data in the right form to the right people and at the right time. Higher Engineering Institutes (HEI) is having a plethora of information available to them. Most of the available data are not used properly and remain just as dead storage. Methods: In this study, we have shown the importance of data visualization using a case study on Coursera review dataset. Different useful tools that support improving an Education System are summarized. Sentiment analysis is performed for coursera course review dataset using deep learning method. At the end, dashboard is also created to visualize student data using powerBI tool. Results: Uses of different visualization tools can help to improve the education system and its performance. The Sentiment expressed by students will help to improve the teaching-learning process and research contribution significantly as they are the major components for evaluation when any HEI wants to receive NAAC [National Assessment and Accreditation Council] approval for benefitting all stakeholders of the HEI. Conclusions: Proper analysis of available data and their proper visualization can help us to improve the education system to a great extent in terms of improving the most important factors like student teaching- learning and their placement to make their future bright. Students expressed sentiments are also key features to analyze the success of the teaching-learning process for both teachers and students as well. We have also used our institute students' data to g enerate a d ash bo ard t hat con tain s s tu den t information from a different perspective that can help higher authorities to make better fruitful decisions.

Keywords

Education Data Mining, Dashboard, Data Visualization, Sentiment.
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  • Education Data Mining, Visualization and Sentiment Analysis of Coursera Course Review

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Authors

Dhaval Bhoi
U & P U. Patel Department of Computer Engineering., India
Amit Thakkar
Department of Computer Science & Engineering, Chandubhai S. Patel Institute of Technology, Faculty of Technology & Engineering, Charotar University of Science and Technology, Changa – 388421, Gujarat, India

Abstract


Objective: No Decisions are good or bed they are taken based on the available data. It is very much essential to represent the data in the right form to the right people and at the right time. Higher Engineering Institutes (HEI) is having a plethora of information available to them. Most of the available data are not used properly and remain just as dead storage. Methods: In this study, we have shown the importance of data visualization using a case study on Coursera review dataset. Different useful tools that support improving an Education System are summarized. Sentiment analysis is performed for coursera course review dataset using deep learning method. At the end, dashboard is also created to visualize student data using powerBI tool. Results: Uses of different visualization tools can help to improve the education system and its performance. The Sentiment expressed by students will help to improve the teaching-learning process and research contribution significantly as they are the major components for evaluation when any HEI wants to receive NAAC [National Assessment and Accreditation Council] approval for benefitting all stakeholders of the HEI. Conclusions: Proper analysis of available data and their proper visualization can help us to improve the education system to a great extent in terms of improving the most important factors like student teaching- learning and their placement to make their future bright. Students expressed sentiments are also key features to analyze the success of the teaching-learning process for both teachers and students as well. We have also used our institute students' data to g enerate a d ash bo ard t hat con tain s s tu den t information from a different perspective that can help higher authorities to make better fruitful decisions.

Keywords


Education Data Mining, Dashboard, Data Visualization, Sentiment.

References