Predicting students performance in final examination using linear regression and multilayer perceptron

Currently, many educational institutions are highly oriented to improve the quality of education and students? learning achievement-examination result. To fulfil such intention, predicting students? performance by analyzing their learning behavior is one of the best way can be taken into account. Once the performance was predicted, it will be easy for teachers, school authority or other related parties to determine the appropriate policies on the issue. Relatedly, this paper aimed to provide the prediction of students? performance in final examination by applying linear regression and multilayer perceptron in WEKA- in terms of accuracy, performance and error rate- to compare their feasibility. The basis of data was derived from extraction and analysis of e-learning logged-post in discussion forum and attendance. Based on the result, it has been concluded that multilayer perceptron provides better prediction results of final examination than linear regression.

[1]  Jianhong Xia,et al.  Achieving better peer interaction in online discussion forums: A reflective practitioner case study , 2013 .

[2]  Sebastián Ventura,et al.  Educational data mining: A survey from 1995 to 2005 , 2007, Expert Syst. Appl..

[3]  Jihie Kim,et al.  Can Online Discussion Participation Predict Group Project Performance? Investigating the Roles of Linguistic Features and Participation Patterns , 2013, International Journal of Artificial Intelligence in Education.

[4]  Sebastián Ventura,et al.  Data mining in course management systems: Moodle case study and tutorial , 2008, Comput. Educ..

[5]  Sherine Dominick,et al.  Analyzing the Student Performance using Classification Techniques to find the better Suited Classifier , 2014 .

[6]  Teknik Informatika,et al.  PREDICTION OF STUDENT ACADEMIC PERFORMANCE BY AN APPLICATION OF DATA MINING TECHNIQUES , 2011 .

[7]  Jure Leskovec,et al.  Engaging with massive online courses , 2014, WWW.

[8]  Umar Manzoor,et al.  Modeling and Predicting Students' Academic Performance Using Data Mining Techniques , 2016 .

[9]  Ryan S. Baker,et al.  The State of Educational Data Mining in 2009: A Review and Future Visions. , 2009, EDM 2009.

[10]  Shieu-Hong Lin.  Data mining for student retention management , 2012 .

[11]  Mashael Nasser AlJeraisy,et al.  Web 2.0 in Education: The Impact of Discussion Board on Student Performance and Satisfaction , 2015 .

[12]  Prashant Bhat,et al.  Educational Data Mining: Classification Techniques for Recruitment Analysis , 2016 .

[13]  Noelia Pinto,et al.  ACADEMIC PERFORMANCE PROFILES: A DESCRIPTIVE MODEL BASED ON DATA MINING , 2015 .

[14]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[15]  S. Vijayarani,et al.  Comparative Analysis of Bayes and Lazy Classification Algorithms , 2013 .

[16]  Sankar K. Pal,et al.  Multilayer perceptron, fuzzy sets, and classification , 1992, IEEE Trans. Neural Networks.

[17]  Shane Dawson,et al.  Improving academic outcomes: does participating in online discussion forums payoff? , 2013 .

[18]  Mukesh Kumar,et al.  Recognition of Slow Learners Using Classification Data Mining Techniques , 2016 .

[19]  D. K. Kirange,et al.  Educational Data Mining Survey for Predicting Student’s Academic Performance , 2019, Lecture Notes on Data Engineering and Communications Technologies.

[20]  P. V. Praveen Sundar.  A COMPARATIVE STUDY FOR PREDICTING STUDENT'S ACADEMIC PERFORMANCE USING BAYESIAN NETWORK CLASSIFIERS , 2013 .

[21]  G. Hommel,et al.  Linear regression analysis: part 14 of a series on evaluation of scientific publications. , 2010, Deutsches Arzteblatt international.

[22]  M. Anoopkumar,et al.  A Review on Data Mining techniques and factors used in Educational Data Mining to predict student amelioration , 2016, 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE).

[23]  Jeena Thomas,et al.  Predicting College Students Dropout using EDM Techniques , 2015 .

[24]  Dragan Gasevic,et al.  Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success , 2016, Internet High. Educ..

[25]  Sebastián Ventura,et al.  Educational Data Mining: A Review of the State of the Art , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[26]  Wahidah Husain,et al.  A Review on Predicting Student's Performance Using Data Mining Techniques , 2015 .

[27]  Sushruta Mishra,et al.  Enhancing the capabilities of Student Result Prediction System , 2016, ICTCS.