An evaluation model based on procedural behaviors for predicting MOOC learning performance: students’ online learning behavior analytics and algorithms construction

Abstract

Purpose The purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners’ online learning behaviors, and comparing three algorithms – multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART). Design/methodology/approach Through literature review and analysis of data correlation in the original database, a framework of online learning behavior indicators containing 26 behaviors was constructed. The degree of correlation with the final learning performance was analyzed based on learners’ system interaction behavior, resource interaction behavior, social interaction behavior and independent learning behavior. A total of 12 behaviors highly correlated to learning performance were extracted as major indicators, and the MLR method, MLP method and CART method were used as typical algorithms to evaluate learners’ MOOC learning performance. Findings The behavioral indicator framework constructed in this study can effectively analyze learners’ learning, and the evaluation model constructed using the MLP method (89.91%) and CART method (90.29%) can better achieve the prediction of MOOC learners’ learning performance than using MLR method (83.64%). Originality/value This study explores the patterns and characteristics among different learning behaviors and constructs an effective prediction model for MOOC learners’ learning performance, which can help teachers understand learners’ learning status, locate learners with learning difficulties promptly and provide targeted instructional interventions at the right time to improve teaching quality.

Analysis of learning behaviour in immersive virtual reality

“…For example, Rivas et al [30] used machine learning techniques to explore the relationship between students' learning behaviors and academic performance and proposed a machine learning-based framework for analyzing student performance. Tong et al [31] proposed a performance prediction assessment model for learning process behavior based on machine learning, and detailed the construction process and feature extraction method of the model. Yan et al [32] also suggested in their study that applying machine learning to learning behavior analysis has the following advantages: efficiency: machine learning can quickly analyze and mine large amounts of online learning data; automation: machine learning can automatically analyze and process online learning data without human intervention; personalization: machine learning can provide students with personalized learning resources and learning paths for students; predictive: machine learning predicts and evaluates students' learning behaviors and academic performance.…”

Predicting Factors that Affect East Asian Students’ Reading Proficiency in PISA

“…There are other studies on predicting the risk of dropouts using log datasets [22]- [26]. In work by [22], weighted attributes are introduced prior to SVM Classifier, resulting in better performance than non-weighted attributes.…”

“…DeepFM [25] is DNN and factorization machine hybrid, achieving 99% in validation data. In work by [26] multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART) are compared with MLP and CART performing better than MLR. Other than predicting the risk of dropout, there are studies on using machine learning to predict graduation [27], [28][29] has created a mobile application with deep learning to enhance English and Arabic vocabulary among children.…”