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An analytic hierarchy process-based student arrangement in post-COVID-19
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DOI：10.4025/actascitechnol.v48i1.71415.png)
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Abstract
En 中文
In the post-COVID-19 era, balancing public health with effective education is essential. Schools have adopted blended learning models combining in-person and distance classes to reduce large gatherings. While distance learning offers flexibility and safety, it poses challenges such as limited supervision, technical barriers, and reduced peer interaction, which can hinder student engagement and performance. Taiwanese elementary schools traditionally use randomization to assign students to classes, promoting fairness but neglecting differentiated learning based on academic performance. To address this, primary education must focus on supporting students with lower academic achievement. This study proposes an analytic hierarchy process (AHP) method to evaluate students' academic performance and guide class arrangements, ensuring a more tailored and effective learning environment. In particular, pupils' midterm test scores at a Taiwanese public elementary school (N = 25) were adopted as an empirical case. Results indicated that the AHP method reduced the respective internal academic performance gaps in in-person classes (N = 12, M = 80.33, SD = 6.52) and distance learning classes (N = 13, M = 93.38, SD = 2.03). Furthermore, the academic performances of the two types of classes had significant differences (t = 6.58, p < 0.001). The AHP method effectively selects students with similar academic performance levels into the same learning group, reducing the troubles of students' with different learning levels within the class and improving their overall learning performance. Moreover, the evaluated results of the subjects' relative importance are expected to meet future education trends.
Keywords:
Student arrangement issue
post-pandemic
distance teaching
COVID-19
analytic hierarchy process
Student arrangement issue
post-pandemic
distance teaching
COVID-19
analytic hierarchy process
Journal
A
IF:
0.6
Papers:
31
Citations:
563
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