SEPN: A Sequential Engagement Based Academic Performance Prediction Model

Students’ performance prediction is a crucial task in today's online education. By predicting a student's final grade in an academic examination, intervene can be applied in advance. Recently, many machine learning models have been designed to couple students’ online activity with their academic performance. However, it is difficult for these models to effectively make prediction due to the excessive difference in feature selection. While in most cases, too many parameters and heterogeneous features can also be one of the main sticking points. To this end, we propose a sequential engagement based academic performance prediction network. It consists of two main components: an engagement detector and a sequential predictor. The engagement detector leverages the advantages of a convolutional neural network to detect students’ engagement patterns through their daily activities. The sequential predictor adopts the structure of long short-term memory and learns the interaction from the engagement feature spaces and demographic features. By comparing with various existing advanced machine learning models, the results show that this method has better performance than the existing ones when involving the engagement detection mechanism.

Authors

• Xiangyu Song Deakin University
• Jianxin Li Beihang University
• Shijie Sun
• Hui Yin State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System, Beijing Jiaotong University
• Phillip Dawson
• Robin Ram Mohan Doss

Keywords

• Feature extraction
• Machine learning
• Intelligent systems
• Predictive models
• Learning systems
• Time series analysis
• Information technology
• Academic Performance
• Predictor Of Academic Performance
• Sequential Engagement
• Demographic Data
• Daily Activities
• Convolutional Neural Network
• Active Learning
• Short-term Memory
• Risk Of Failure
• Student Learning
• Long Short-term Memory
• Learning Performance
• Student Performance
• Future Performance
• Online Activities
• Online Teaching
• Student Assessment
• Operation Period
• Final Exam
• Long Short-term Memory Network
• Massive Open Online Courses
• Student Engagement
• Daily Mail
• Beginning Of The Course
• Support Vector Regression
• Clickstream
• Highest Level Of Education
• Time Series Data
• Convolutional Layers
• Batch Normalization
• Data mining
• Adaptive and intelligent educational systems