Educational Data Mining to Support Programming Learning

Authors: Bhosale R.S., Deshmukh Suraj, Malunjkar Avishkar, Netke Rutuja, Patki Rutuja

Country: India

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Abstract: The majority of academic programs in the fields of engineering aim to enhance practical abilities. Due to its numerous uses & global significance, computer programming has today become a necessary talent. We use academic scores and students' programming logs as an experienced dataset. Kaggle.com Learning is where the programming logs are gathered. The method, datasets, evaluation, and output of machine learning approaches were discovered to be necessary. An analyst can find trends by using clustering and visualization in a low-dimensional representation of student data. Early performance data and exploratory characteristics are utilized to classify students who have previously graduated from three performance levels. The results reveal that technology-enhanced commenter- contributes significantly to learning process satisfaction and problem-solving learning outcomes. The proposed educational data mining techniques allow access to student’s behaviour in the E-learning system for understanding students' interest in studying the learning materials.

Keywords: Machine Learning, Authentication, Security

Paper Id: 230176

Published On: 2023-05-27

Published In: Volume 11, Issue 3, May-June 2023

Cite This: Educational Data Mining to Support Programming Learning - Bhosale R.S., Deshmukh Suraj, Malunjkar Avishkar, Netke Rutuja, Patki Rutuja - IJIRMPS Volume 11, Issue 3, May-June 2023.

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