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covid 19 learning loss pandemic student structural equation modeling

A Causal Model of Learning Loss in the Midst of COVID-19 Pandemic Among Thai Lower Secondary School Students

Ittipaat Suwathanpornkul , Orn-uma Charoensuk , Panida Sakuntanak , Manaathar Tulmethakaan , Chawapon Sarnkhaowkhom

It is known that the COVID-19 pandemic led to learning losses among students both domestically and internationally. Therefore, situational and casual .


  • Pub. date: July 15, 2024
  • Online Pub. date: April 15, 2024
  • Pages: 1155-1170
  • 68 Downloads
  • 158 Views
  • 0 Citations
I

It is known that the COVID-19 pandemic led to learning losses among students both domestically and internationally. Therefore, situational and casual factors were examined to discover and understand them so that learning loss could be reduced or recovered from. This research aimed to: (a) study learning loss situation; and (b) develop and examine the causal model of learning loss among lower secondary school students affected by the pandemic. The sample included 650 Grade 7-9 students selected by multi-stage random sampling. The data was collected using a self-developing questionnaire as a research instrument. The data was analyzed using descriptive statistics, independent samples t-test, ANOVA, and structural equation modeling (SEM) through the LISREL program. The findings were: (a) Lower secondary school students had an average academic achievement learning loss at the moderate level with the highest mean of learning loss in mathematics (M=3.012, SD=1.074), and an average learning characteristics learning loss at the medium level (M=2.824, SD=0.842). Several situational factors had a different effect depending on the school size with a statistical significance of .05.; and (b) the causal model showed the learning loss of grade 7-9 students was consistent with the empirical data (χ2=46.885, df=34, p= .069, GFI=0.991, AGFI=0.964, CFI=0.999, RMSEA=0.024, SRMR=0.014).

Keywords: COVID-19, learning loss, pandemic, student, structural equation modeling.

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