Using the REACT Teaching Model to Improve Students’ Digital Literacy in Web Design

Abstract

In digital literacy, web design courses can help develop advanced skills to achieve various learning goals, such as software applications, programming skills, website planning, and communication skills (Ivanova, 2018; Tsai et al., 2021). However, the research on web design skills is still scarce in the field of computing education research (Tsai et al., 2021; Wang & Wang, 2011). This study utilizes the REACT teaching model in web design courses to enhance undergraduates’ web design skills and self-concept. The REACT model includes Relating, Experiencing, Applying, Cooperating, and Transferring teaching strategies (Center for Occupational Research and Development, 2016). The instruments were the Web Design Skill Test, the Self-concept Scale, and the course perception questionnaire. The quasi-experimental research method was adopted. The participants were 91 undergraduates taking general education courses, including 47 students in the experimental group and 44 in the control group. The quantitative and qualitative data analyses were used to conduct a comprehensive analysis. This study demonstrates that the average score of web design skills among students in the experimental group was higher than that of students in the control group (F=29.53, p<.001). In addition, the results revealed that the experimental treatment had a notable effect on students’ self-concept in web design, particularly among students with middle and low levels in the pre-test (t=2.87, p=.005; t=3.64, p<.001). Moreover, the open-ended questionnaire showed that students in the experimental group held positive views on the REACT teaching model. Relevant research implications and suggestions are put forward as references for future research.

Presenters

Chun-Yen Tsai
Professor, National Sun Yat-sen University, Taiwan

Details

Presentation Type

Poster Session

Theme

Science, Mathematics and Technology Learning

KEYWORDS

Digital literacy REACT Web design