RESEARCH ARTICLE


A Hybrid Teaching Mode Based on Machine Learning Algorithm



Jinjin Liang1, 2, *, Yong Nie1
1 School of Education, Shaanxi Normal University, Xi'an, China
2 School of Sciences, Xi’an Shiyou University, Xi'an, China

Article Information


Identifiers and Pagination:

Year: 2020
Volume: 6
First Page: 22
Last Page: 28
Publisher Id: TOAIJ-6-22
DOI: 10.2174/1874061802006010022

Article History:

Received Date: 15/12/2019
Revision Received Date: 10/03/2020
Acceptance Date: 03/04/2020
Electronic publication date: 18/06/2020
Collection year: 2020

© 2020 Liang and Nie.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Education, Shaanxi Normal University, Xi'an, China and School of Sciences, Xi’an Shiyou University, Xi'an, China; Tel: +86 151 2901 7540; Fax: +86 029 8838 2735; E-mail: myonlyonly@126.com


Abstract

Background:

Hybrid teaching mode is a new trend under the Education Informatization environment, which combines the advantages of educators’ supervision offline and learners’ self-regulated learning online. Capturing learners’ learning behavior data becomes easy both from the traditional classroom and online platform.

Methods:

If machine learning algorithms can be applied to mine valuable information underneath those behavior data, it will provide scientific evidence and contribute to wise decision making as well as effective teaching process designing by educators.

Results:

This paper proposed a hybrid teaching mode utilizing machine learning algorithms, which uses clustering analysis to analyze the learner’s characteristics and introduces a support vector machine to predict future learning performance. The hybrid mode matches the predicted results to carry out the offline teaching process.

Conclusion:

Simulation results on about 356 students’ data on one specific course in a certain semester demonstrate that the proposed hybrid teaching mode performs very well by analyzing and predicting the learners’ performance with high accuracies.

Keywords: Education informatization, Hybrid teaching, Machine learning, Learner’s Characteristics, Learning performance, Massive Open Online Courses (MOOCs).