EconPapers    
Economics at your fingertips  
 

Causal complexities to evaluate the effectiveness of remedial instruction

Chien-Yun Dai and Duen-Huang Huang

Journal of Business Research, 2015, vol. 68, issue 4, 894-899

Abstract: This study investigates 3 models of remedial instruction, e-learning, blended learning, and traditional instruction, to vocational high school students with low mathematics achievement to analyze whether student achievement improves significantly and how each instruction model facilitates improvement. This study applies partial least squares (PLS) and fuzzy set/Qualitative Comparative Analysis (fsQCA) to analyze the effectiveness and causal complexities of the 3 models. The results indicate that all 3 models facilitate substantial academic progress, the e-learning model being the most effective. The combinations of 6 negative antecedents cause the learning problems of the students. After the changes of a few of these antecedents through the use of remedial instruction, the students improve their scores. FsQCA provides antecedent combinations to show the causal complexities; hence, provides more accurate explanations than does the PLS.

Keywords: Blended learning; Learning motivation; Learning attitude; e-Learning (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0148296314004020
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:68:y:2015:i:4:p:894-899

DOI: 10.1016/j.jbusres.2014.11.048

Access Statistics for this article

Journal of Business Research is currently edited by A. G. Woodside

More articles in Journal of Business Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-03-19
Handle: RePEc:eee:jbrese:v:68:y:2015:i:4:p:894-899