International Journal For Multidisciplinary Research

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Applying Predictive Analytics using Clickstream Data for Improving the Students Performance

Author(s) Nidhi Sharma, Ritik Bhardwaj
Country India
Abstract Student performance analysis is an essential aspect of educational institutions. In recent years, machine learning (ML) has emerged as a promising tool to analyze student performance. To predict learning abilities of students and prescribing them a personalized learning curriculum, it is necessary to estimate their behavior to know about their weaknesses, strengths and help institutions to improve enrollment and retention. If it is possible for the teachers to predict in advance and prescribe ways to the at-risk and dropout students, they can plan more effectively to help them. We are describing in this paper various intelligent tutoring systems with Educational Data Mining, Predictive Learning Analytics, prediction of at-risk students at an earlier basis and how this prediction task is done. Predictive Analytics can also offer insights to help students to make informed decisions about each individual student to improve outcomes by understanding what drives each student behavior and how much the institution can create intensional, specific plans that will positively impact students.
Keywords Predicting Students At- Risk, EDM, Learning Analytics, Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), Virtual Learning (VL), Machine Learning (ML), Deep Learning, Recurrent Neural Network (RNN)
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 5, Issue 5, September-October 2023
Published On 2023-10-13
Cite This Applying Predictive Analytics using Clickstream Data for Improving the Students Performance - Nidhi Sharma, Ritik Bhardwaj - IJFMR Volume 5, Issue 5, September-October 2023. DOI 10.36948/ijfmr.2023.v05i05.7520
DOI https://doi.org/10.36948/ijfmr.2023.v05i05.7520
Short DOI https://doi.org/gsv6bf
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