Grade Prediction in Blended Learning Using Multisource Data
Today, blended learning is widely carried out in many colleges. Different online learning platforms have accumulated a large number of fine granularity records of students’ learning behavior, which provides us with an excellent opportunity to analyze students’ learning behavior. In this paper, based...
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2021-01-01
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Series: | Scientific Programming |
Online Access: | http://dx.doi.org/10.1155/2021/4513610 |
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doaj-e8fd7bd9d8b54af2aed00ee2f06522602021-09-27T00:53:20ZengHindawi LimitedScientific Programming1875-919X2021-01-01202110.1155/2021/4513610Grade Prediction in Blended Learning Using Multisource DataLing-qing Chen0Mei-ting Wu1Li-fang Pan2Ru-bin Zheng3School of Computer EngineeringSchool of Computer EngineeringSchool of ScienceSchool of Computer EngineeringToday, blended learning is widely carried out in many colleges. Different online learning platforms have accumulated a large number of fine granularity records of students’ learning behavior, which provides us with an excellent opportunity to analyze students’ learning behavior. In this paper, based on the behavior log data in four consecutive years of blended learning in a college’s programming course, we propose a novel multiclassification frame to predict students’ learning outcomes. First, the data obtained from diverse platforms, i.e., MOOC, Cnblogs, Programming Teaching Assistant (PTA) system, and Rain Classroom, are integrated and preprocessed. Second, a novel error-correcting output codes (ECOC) multiclassification framework, based on genetic algorithm (GA) and ternary bitwise calculator, is designed to effectively predict the grade levels of students by optimizing the code-matrix, feature subset, and binary classifiers of ECOC. Experimental results show that the proposed algorithm in this paper significantly outperforms other alternatives in predicting students’ grades. In addition, the performance of the algorithm can be further improved by adding the grades of prerequisite courses.http://dx.doi.org/10.1155/2021/4513610 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ling-qing Chen Mei-ting Wu Li-fang Pan Ru-bin Zheng |
spellingShingle |
Ling-qing Chen Mei-ting Wu Li-fang Pan Ru-bin Zheng Grade Prediction in Blended Learning Using Multisource Data Scientific Programming |
author_facet |
Ling-qing Chen Mei-ting Wu Li-fang Pan Ru-bin Zheng |
author_sort |
Ling-qing Chen |
title |
Grade Prediction in Blended Learning Using Multisource Data |
title_short |
Grade Prediction in Blended Learning Using Multisource Data |
title_full |
Grade Prediction in Blended Learning Using Multisource Data |
title_fullStr |
Grade Prediction in Blended Learning Using Multisource Data |
title_full_unstemmed |
Grade Prediction in Blended Learning Using Multisource Data |
title_sort |
grade prediction in blended learning using multisource data |
publisher |
Hindawi Limited |
series |
Scientific Programming |
issn |
1875-919X |
publishDate |
2021-01-01 |
description |
Today, blended learning is widely carried out in many colleges. Different online learning platforms have accumulated a large number of fine granularity records of students’ learning behavior, which provides us with an excellent opportunity to analyze students’ learning behavior. In this paper, based on the behavior log data in four consecutive years of blended learning in a college’s programming course, we propose a novel multiclassification frame to predict students’ learning outcomes. First, the data obtained from diverse platforms, i.e., MOOC, Cnblogs, Programming Teaching Assistant (PTA) system, and Rain Classroom, are integrated and preprocessed. Second, a novel error-correcting output codes (ECOC) multiclassification framework, based on genetic algorithm (GA) and ternary bitwise calculator, is designed to effectively predict the grade levels of students by optimizing the code-matrix, feature subset, and binary classifiers of ECOC. Experimental results show that the proposed algorithm in this paper significantly outperforms other alternatives in predicting students’ grades. In addition, the performance of the algorithm can be further improved by adding the grades of prerequisite courses. |
url |
http://dx.doi.org/10.1155/2021/4513610 |
work_keys_str_mv |
AT lingqingchen gradepredictioninblendedlearningusingmultisourcedata AT meitingwu gradepredictioninblendedlearningusingmultisourcedata AT lifangpan gradepredictioninblendedlearningusingmultisourcedata AT rubinzheng gradepredictioninblendedlearningusingmultisourcedata |
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