Analysis of Blended Learning Model Application Using Text Mining Method

Lin Wang, Yanfen Huang, Muhd Khaizer Omar

Abstract


The rapid development of networks has resulted in the recognition of blended learning as an effective learning model. The text mining method was used to analyze the blended learning practice data of 17 countries provided by the Christensen Institute. By classifying and extracting text information from the use of blended learning model selection and the challenges of blended learning, the factors that hinder the implementation of blended learning were analyzed. The distribution of blended learning courses and practice models in each country were discussed, as was the influence relationship between the region and implementation model. The results demonstrate that the practice of blended learning in primary and secondary schools is mature in four courses of English, Mathematics, Science, and Social Research. The blended learning implementation model can be unaffected by the region, but more tend to “mix.” The practice cycle of blended learning becomes long and requires long-term stable support, while teachers’ ability and students’ ability preparation are the largest obstacles to the effectual development of blended learning. This study provides references for improving the efficiency of blended learning practices, especially in the aspects of practice model selection, infrastructure preparation, and teacher and student ability training.

Keywords


blended learning; online teaching; text mining; education; virtual learning

Full Text:

PDF


Copyright (c) 2021 Lin Wang, Yanfen Huang, Muhd Khaizer Omar


International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
Creative Commons License
Indexing:
Scopus logo Clarivate Analyatics ESCI logo EI Compendex logo IET Inspec logo DOAJ logo DBLP logo Learntechlib logo EBSCO logo Ulrich's logo Google Scholar logo MAS logo