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Title: Towards Final Scores Prediction over Clickstream Using Machine Learning Methods
Authors: HASNINE, Mohammad Nehal
MAJUMDAR, Rwitajit
MOURI, Kousuke
OGATA, Hiroaki
Author's alias: 緒方, 広明
Keywords: Clickstream Analysis
Educational Data Mining
Final Score Prediction
Learning Analytics
Issue Date: 24-Nov-2018
Publisher: Asia-Pacific Society for Computers in Education (APSCE)
Journal title: 26th International Conference on Computers in Education Workshop Proceedings
Start page: 399
End page: 404
Abstract: E-books are capable of producing a significant amount of clickstream data thatinsights students’ learning behavior. Clickstream data are often analyzed in learninganalytics and educational data mining domains to understand students’ synchronous andasynchronous learning processes. The present study analyzed a dataset consisting ofuniversity students’ clickstream data for predicting their final scores using machine-learningmethods. To begin with, the raw data are preprocessed in four steps, namely dataaggregation, feature generation, data balancing, and feature selection. After that, utilizingmachine learning methods, high performing and low performing students’ final scores arepredicted. For this, eight machine-learning methods (Neural Network, AdaBoost, LogisticRegression; Naïve Bayes, kNN, Support Vector Machine, Random Forest, and CN2 RuleInduction) are employed and their performances were compared. Result revealed that CN2Rule Induction algorithm having 88% accuracy outperformed other machine learningmethods when best-5 selected features from the dataset were taken into consideration.However, the Multilayer Perceptron based Neural Network performed best having thesimilar accuracy with CN2 Rule Induction when all features were considered to predict.This paper also focuses on how SMOTE as a data balancing algorithm can be applied tosolve data imbalance problem and various scoring methods can be compared to identify themost important feature attributes in clickstream.
Description: 26th International Conference on Computers in Education, Metro Manila, Philippines, November 26-30, 2018.
Rights: Copyright 2018 Asia-Pacific Society for Computers in Education. All rights reserved. No part of this book may be reproduced, stored in a retrieval system, transmitted, in any forms or any means, without the prior permission of the Asia-Pacific Society for Computers in Education. Individual papers may be uploaded on to institutional repositories or other academic sites for self-archival purposes.
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