SPOC-MFLP: A Multi-feature Learning Prediction Model for SPOC Students Using Machine Learning

Chunyan Yu

doi:10.6180/jase.201806_21(2).0016

SPOC-MFLP: A Multi-feature Learning Prediction Model for SPOC Students Using Machine Learning

Computer Science and Information Engineering

Chunyan Yu This email address is being protected from spambots. You need JavaScript enabled to view it.¹

¹School of Computer and Information Engineering, Chuzhou University, P.R. China

Received: April 9, 2018
Accepted: April 30, 2018
Publication Date: June 1, 2018

Download Citation: ||https://doi.org/10.6180/jase.201806_21(2).0016

ABSTRACT

Learning analysis is one of the most important applications of machine learning. Many studies have proposed solutions to learning performance prediction using online learning data. Unlike the previous studies, this paper analyzes online learning environment and formalizes the problem of online learning prediction. Based on the formalization, a multi-feature based learning prediction model for SPOC is proposed, called SPOC-MFLP, which generalizes the prediction problem of SPOC learning including objective, constraints, system and algorithms. The proposed SPOC-MFLP could be extended for MOOC and other online learning forms. Principle components analysis is adopted to discover the correlations of students’online multi features, and linear regression and deep neural network are used to predict the learning performance. The predicted results include specific scores or segmented grades of the final exam of SPOC, as well as students’future specialized courses. Experimental data are collected from a SPOC in Chuzhou University for two years and the experimental results reveal that the proposed SPOC-MFLP performs well.

Keywords: Machine Learning, Learning Analysis, Features Analysis, SPOC, Learning Performance Prediction

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