Predicting Student Academic Performance using Machine Learning and Time Management Skill Data

Prediction of student academic performance is an important aspect in the learning process. This study applies several machine learning models in predicting student academic performance using Time Management Skills data obtained from Time Structure Questionnaire (TSQ). Previously, some other data has been used as a feature in making predictions, but TSQ result had never been used before as a feature, even though it may shows the conditions of how students use their time in learning. Five different machine learning models were trained using TSQ data to predict student academic performance. In addition, student English performance is also predicted in the same way as a comparison As a result, the Linear Support Vector Machine model can predict student academic performance with 80% accuracy and English performance with 84% accuracy using TSQ data.

[1]  Andreas Holzinger,et al.  Data Mining with Decision Trees: Theory and Applications , 2015, Online Inf. Rev..

[2]  Phil Hodkinson,et al.  The interrelationships between informal and formal learning , 2003 .

[3]  Ching-Chieh Kiu,et al.  Data Mining Analysis on Student’s Academic Performance through Exploration of Student’s Background and Social Activities , 2018, 2018 Fourth International Conference on Advances in Computing, Communication & Automation (ICACCA).

[4]  R. L. Dipboye,et al.  College students' time management : correlations with academic performance and stress , 1990 .

[5]  Ibrahim Aljarah,et al.  Preprocessing and analyzing educational data set using X-API for improving student's performance , 2015, 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT).

[6]  Paulo Cortez,et al.  Using data mining to predict secondary school student performance , 2008 .

[7]  B. K. Britton,et al.  Effects of Time-Management Practices on College Grades , 1991 .

[8]  Barbara L. Stewart,et al.  TIME MANAGEMENT SKILLS AND STUDENT PERFORMANCE IN ONLINE COURSES , 2015 .

[9]  Allan Wigfield,et al.  Academic and Social Motivational Influences on Students' Academic Performance , 1998 .

[10]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[11]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[12]  N. Feather,et al.  Some correlates of structure and purpose in the use of time. , 1988 .

[13]  T. Okazaki,et al.  Comparative Study of Prediction Models on High School Student Performance in Mathematics , 2019, 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC).

[14]  David A. Elizondo,et al.  The linear separability problem: some testing methods , 2006, IEEE Transactions on Neural Networks.

[15]  Ibrahim Aljarah,et al.  Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods , 2016 .

[16]  Leah Mutanu,et al.  Enhancing Computer Students’ Academic Performance through Predictive Modelling - A Proactive Approach , 2019, 2019 14th International Conference on Computer Science & Education (ICCSE).

[17]  Malcolm J. Bond,et al.  Time structure and purposeful activity among employed and unemployed university graduates , 1983 .

[18]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[19]  Thomas J. Watson,et al.  An empirical study of the naive Bayes classifier , 2001 .

[20]  R. Roe,et al.  A review of the time management literature , 2007 .

[21]  Other Contributors Are Indicated Where They Contribute.  Python Software Foundation , 2017 .

[22]  Walid Salameh,et al.  Using Decision Tree and Artificial Neural Network to Predict Students Academic Performance , 2019, 2019 10th International Conference on Information and Communication Systems (ICICS).