Cargando…

E-Learning Performance Prediction: Mining the Feature Space of Effective Learning Behavior

Learning analysis provides a new opportunity for the development of online education, and has received extensive attention from scholars at home and abroad. How to use data and models to predict learners’ academic success or failure and give teaching feedback in a timely manner is a core problem in...

Descripción completa

Detalles Bibliográficos
Autores principales: Qiu, Feiyue, Zhu, Lijia, Zhang, Guodao, Sheng, Xin, Ye, Mingtao, Xiang, Qifeng, Chen, Ping-Kuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140884/
https://www.ncbi.nlm.nih.gov/pubmed/35626605
http://dx.doi.org/10.3390/e24050722
_version_ 1784715208575418368
author Qiu, Feiyue
Zhu, Lijia
Zhang, Guodao
Sheng, Xin
Ye, Mingtao
Xiang, Qifeng
Chen, Ping-Kuo
author_facet Qiu, Feiyue
Zhu, Lijia
Zhang, Guodao
Sheng, Xin
Ye, Mingtao
Xiang, Qifeng
Chen, Ping-Kuo
author_sort Qiu, Feiyue
collection PubMed
description Learning analysis provides a new opportunity for the development of online education, and has received extensive attention from scholars at home and abroad. How to use data and models to predict learners’ academic success or failure and give teaching feedback in a timely manner is a core problem in the field of learning analytics. At present, many scholars use key learning behaviors to improve the prediction effect by exploring the implicit relationship between learning behavior data and grades. At the same time, it is very important to explore the association between categories and prediction effects in learning behavior classification. This paper proposes a self-adaptive feature fusion strategy based on learning behavior classification, aiming to mine the effective E-learning behavior feature space and further improve the performance of the learning performance prediction model. First, a behavior classification model (E-learning Behavior Classification Model, EBC Model) based on interaction objects and learning process is constructed; second, the feature space is preliminarily reduced by entropy weight method and variance filtering method; finally, combined with EBC Model and a self-adaptive feature fusion strategy to build a learning performance predictor. The experiment uses the British Open University Learning Analysis Dataset (OULAD). Through the experimental analysis, an effective feature space is obtained, that is, the basic interactive behavior (BI) and knowledge interaction behavior (KI) of learning behavior category has the strongest correlation with learning performance.And it is proved that the self-adaptive feature fusion strategy proposed in this paper can effectively improve the performance of the learning performance predictor, and the performance index of accuracy(ACC), F1-score(F1) and kappa(K) reach 98.44%, 0.9893, 0.9600. This study constructs E-learning performance predictors and mines the effective feature space from a new perspective, and provides some auxiliary references for online learners and managers.
format Online
Article
Text
id pubmed-9140884
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-91408842022-05-28 E-Learning Performance Prediction: Mining the Feature Space of Effective Learning Behavior Qiu, Feiyue Zhu, Lijia Zhang, Guodao Sheng, Xin Ye, Mingtao Xiang, Qifeng Chen, Ping-Kuo Entropy (Basel) Article Learning analysis provides a new opportunity for the development of online education, and has received extensive attention from scholars at home and abroad. How to use data and models to predict learners’ academic success or failure and give teaching feedback in a timely manner is a core problem in the field of learning analytics. At present, many scholars use key learning behaviors to improve the prediction effect by exploring the implicit relationship between learning behavior data and grades. At the same time, it is very important to explore the association between categories and prediction effects in learning behavior classification. This paper proposes a self-adaptive feature fusion strategy based on learning behavior classification, aiming to mine the effective E-learning behavior feature space and further improve the performance of the learning performance prediction model. First, a behavior classification model (E-learning Behavior Classification Model, EBC Model) based on interaction objects and learning process is constructed; second, the feature space is preliminarily reduced by entropy weight method and variance filtering method; finally, combined with EBC Model and a self-adaptive feature fusion strategy to build a learning performance predictor. The experiment uses the British Open University Learning Analysis Dataset (OULAD). Through the experimental analysis, an effective feature space is obtained, that is, the basic interactive behavior (BI) and knowledge interaction behavior (KI) of learning behavior category has the strongest correlation with learning performance.And it is proved that the self-adaptive feature fusion strategy proposed in this paper can effectively improve the performance of the learning performance predictor, and the performance index of accuracy(ACC), F1-score(F1) and kappa(K) reach 98.44%, 0.9893, 0.9600. This study constructs E-learning performance predictors and mines the effective feature space from a new perspective, and provides some auxiliary references for online learners and managers. MDPI 2022-05-19 /pmc/articles/PMC9140884/ /pubmed/35626605 http://dx.doi.org/10.3390/e24050722 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Qiu, Feiyue
Zhu, Lijia
Zhang, Guodao
Sheng, Xin
Ye, Mingtao
Xiang, Qifeng
Chen, Ping-Kuo
E-Learning Performance Prediction: Mining the Feature Space of Effective Learning Behavior
title E-Learning Performance Prediction: Mining the Feature Space of Effective Learning Behavior
title_full E-Learning Performance Prediction: Mining the Feature Space of Effective Learning Behavior
title_fullStr E-Learning Performance Prediction: Mining the Feature Space of Effective Learning Behavior
title_full_unstemmed E-Learning Performance Prediction: Mining the Feature Space of Effective Learning Behavior
title_short E-Learning Performance Prediction: Mining the Feature Space of Effective Learning Behavior
title_sort e-learning performance prediction: mining the feature space of effective learning behavior
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140884/
https://www.ncbi.nlm.nih.gov/pubmed/35626605
http://dx.doi.org/10.3390/e24050722
work_keys_str_mv AT qiufeiyue elearningperformancepredictionminingthefeaturespaceofeffectivelearningbehavior
AT zhulijia elearningperformancepredictionminingthefeaturespaceofeffectivelearningbehavior
AT zhangguodao elearningperformancepredictionminingthefeaturespaceofeffectivelearningbehavior
AT shengxin elearningperformancepredictionminingthefeaturespaceofeffectivelearningbehavior
AT yemingtao elearningperformancepredictionminingthefeaturespaceofeffectivelearningbehavior
AT xiangqifeng elearningperformancepredictionminingthefeaturespaceofeffectivelearningbehavior
AT chenpingkuo elearningperformancepredictionminingthefeaturespaceofeffectivelearningbehavior