Predictive Analysis Of Student Performance in Online Learning | IJCT Volume 13 – Issue 2 | IJCT-V13I2P73
International Journal of Computer Techniques
ISSN 2394-2231
Author
Kolla Puneeth Sai, Kanyadhri premchand, Kanna Jyothi Siva Prasad, Mrs.Subashri Rajendran
Abstract
In recent years, a number of learning platforms have emerged, and this has resulted in a tremendous growth in data related to learning processes. Analyzing this data helps in improving student learning outcomes. Making early predictions about student performance is a key area of research in data mining in education. Data mining and machine learning techniques are useful in finding patterns in student data related to academic performance and making early predictions of success or failure in academics, as suggested in a study by Shahiri et al. [9]. Keeping this in view, this study presents a predictive analysis model for evaluating student performance in an online learning scenario. Various factors are considered in this model, such as student engagement, attendance, and performance in tests, along with their interactions with learning platforms. Machine learning algorithms are employed in this system to classify students into different performance categories and identify those students who are not performing well in academics[8]. Such a system will help in increasing learning outcomes, reducing dropout rates, and making decisions in the field of education to improve the quality of learning in an online scenario.
Keywords
Educational Data Mining, Student Performance Prediction, Online Learning, Machine Learning, Predictive Analytics, Supervised Learning, Learning Analytics, Academic Performance, Data Analysis, Student Engagement.
Conclusion
This paper has offered a predictive analysis approach to assess the performance of students in virtual learning environments using machine learning techniques. The model can analyze the patterns of students’ performance based on their attendance, assignments completed, quizzes completed, and their interaction with course materials[12]. This can assist in the classification of students according to their performance and can identify students who may not perform well. The experimental results proved that the machine learning approach can accurately predict students’ performance. This can assist teachers to offer academic guidance to students who may not perform well. This approach can improve students’ performance and reduce the dropout rate among students[20] . In the future, the system can be improved by using real-time data and more advanced the
machine learning techniques to improve the accuracy of the predictions made by the model. machine learning techniques to improve the accuracy of the predictions made by the model.
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How to Cite This Paper
Kolla Puneeth Sai, Kanyadhri premchand, Kanna Jyothi Siva Prasad, Mrs.Subashri Rajendran (2026). Predictive Analysis Of Student Performance in Online Learning. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
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