There have been increased incidences of dropout that have been noticed in the universities in the recent years. These increased reports have been instrumental in introducing the graduation rate of the course completion rate for majority of universities all over the globe. Dropouts are highly undesirable and are an indication of some underlying inconsistencies that have been plaguing the course since a long time. Therefore, an effective system for the purpose of prediction of the dropout rate is the need of the hour. To reach these goals this research article has utilized machine learning approaches. The proposed methodology utilizes the K Nearest Neighbor, Fuzzy Artificial Neural Network and Decision Tree. This approach has been illustrated in utmost detail in this research article, highlighting the execution of the various important modules of the methodology. The experimentation has been performed to achieve the performance of the approach which has yielded highly accurate results. Shashikant Karale | Rajani Pawar | Sharvari Pawar | Poonam Sonkamble "Discovering Student Dropout Prediction through Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd43700.pdf Paper URL: https://www.ijtsrd.comhumanities-and-the-arts/education/43700/discovering-student-dropout-prediction-through-deep-learning/shashikant-karale
International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 5 Issue 4, May-June 2021 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD43700 | Volume – 5 | Issue – 4 | May-June 2021
Page 1549
Discovering Student Dropout
Prediction through Deep Learning
Shashikant Karale, Rajani Pawar, Sharvari Pawar, Poonam Sonkamble
Student, Padmabhushan Vasantdada Patil Institute of Technology, Pune, Maharashtra, India
ABSTRACT
There have been increased incidences of dropout that have been noticed in the
universities in the recent years. These increased reports have been
instrumental in introducing the graduation rate of the course completion rate
for majority of universities all over the globe. Dropouts are highly undesirable
and are an indication of some underlying inconsistencies that have been
plaguing the course since a long time. Therefore, an effective system for the
purpose of prediction of the dropout rate is the need of the hour. To reach
these goals this research article has utilized machine learning approaches. The
proposed methodology utilizes the K Nearest Neighbor, Fuzzy Artificial Neural
Network and Decision Tree. This approach has been illustrated in utmost
detail in this research article, highlighting the execution of the various
important modules of the methodology. The experimentation has been
performed to achieve the performance of the approach which has yielded
highly accurate results.
KEYWORDS: Fuzzy Artificial Neural Networks, K Nearest Neighbor, Decision Tree,
Online Courses
How to cite this paper: Shashikant Karale
| Rajani Pawar | Sharvari Pawar | Poonam
Sonkamble "Discovering Student Dropout
Prediction
through Deep Learning"
Published
in
International Journal
of Trend in Scientific
Research
and
Development (ijtsrd),
ISSN:
2456-6470,
Volume-5 | Issue-4,
June 2021, pp.1549-
1553,
URL:
www.ijtsrd.com/papers/ijtsrd43700.pdf
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